articles about educational mobile applications

What impacts learning effectiveness of a mobile learning app focused on first-year students?

• Original Article
• Open access
• Published: 26 July 2023
• Volume 21 , pages 629–673, ( 2023 )

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• Florian Johannsen   ORCID: orcid.org/0000-0003-3175-6954 1 ,
• Martin Knipp 2 ,
• Thomas Loy 2 ,
• Milad Mirbabaie 3 ,
• Nicholas R. J. Möllmann 4 ,
• Johannes Voshaar 2 &
• Jochen Zimmermann 2  

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In recent years, the application of digital technologies for learning purposes is increasingly discussed as smartphones have become an integral part of students’ everyday life. These technologies are particularly promising in the so-called “transition-in” phase of the student lifecycle when first-year students start to develop a student identity and integrate into the university environment. At that stage, most premature dropouts are observed, presumably due to a lack of self-organization or self-responsibility. Considering this, a mobile app to tackle insufficient student experiences, support learning strategies, and foster self-organization in the “transition-in” phase was developed. The research at hand proposes a generalizable success model for mobile apps with a focus on first-year students, which is based on the IS success model (Delone and McLean in Inf Syst Res 3(1):60–95, 1992) and analyzes those factors that influence student satisfaction with such an app, the intention to reuse the app, and—foremost—students’ learning effectiveness. The results indicate that learning effectiveness is determined both by the perceived user satisfaction and users’ intention to reuse, which are particularly influenced by perceived enjoyment but also system and information quality. Finally, design principles are derived to develop similar mobile solutions.

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1 Introduction

For some time now, the European labor market is facing a severe lack of skilled professionals (Peichl et al. 2022 ). In 2022 alone, 29% of companies in the European Union (EU) reported problems in finding suitable personnel, which is an all-time high considering the development in recent decades (Peichl et al. 2022 ). The situation is agitated in Germany, with 50% of enterprises seriously suffering from the shortage of specialists (ifo Institut 2022 ; Peichl et al. 2022 ). Consequently, more than 770,000 vacant positions for the entire economy will not be adequately occupied in 2023 (cf. Statista 2023 ). In this context, a high student dropout rate is seen as a serious problem in meeting the economy’s demand for qualified workers in the upcoming years (cf. Ahlers and Quispe Villalobos 2022 ; Behr et al. 2021 ; Heublein 2014 ). While in Germany, 14.7% of Bachelor students do not finish their studies, this number is even higher in other EU countries like the Netherlands (28.3%) or Italy (34.1%) (Behr et al. 2020 ; Schnepf 2014 ). For education policies, such student dropout rates imply not only inefficiently used resources for higher education but also high educational costs for students not achieving the aspired educational goals (Baars and Arnold 2014 ; Behr et al. 2021 ). At the same time, dissatisfaction and negative psychological long-term effects are observed for corresponding students, which may paralyze them when searching for alternative pathways to gain a foothold in the labor market (Behr et al. 2021 ; Ibrahim et al. 2013 ; Roso-Bas et al. 2016 ).

In terms of time, most student dropouts happen during the first year of studies (Isleib et al. 2019 ; Neugebauer et al. 2019 ; Opazo et al. 2021 ). According to the student lifecycle (Lizzio 2011 ), which describes the evolution from a prospective to a commencing, continuing, and finally graduating student, first-year students find themselves in the “transition-in” phase. In this phase, self-organized research-based learning (cf. Huber et al. 2009 ) or self-responsibility, which are essential for a successful transition from the highly-structured school environment into the university system, are often perceived as challenging (cf. Zehetmeier et al. 2014 ). Moreover, an inadequate student experience and psychological factors—such as inefficient learning strategies or insufficient intrinsic motivation—are identified as additional reasons for early dropout (cf. Blüthmann et al. 2011 ; Heinze 2018 ; Neugebauer et al. 2019 ). Therefore, “remedial support early in the curriculum” (Baars and Arnold 2014 , p. 106) is necessary to reach students who are at risk of dropping out prematurely.

Parallel to this, universities also experience a change in students’ way of consuming and processing information, organizing their daily routines, socializing or communicating with one another (Musik and Bogner 2019 ), which is mainly triggered by technological progress (Cho et al. 2021 ; Gómez-Galán et al. 2020 ; Gupta et al. 2021 ; Youssef et al. 2021 ). Consequently, the impact of new technologies on students’ learning behaviors or interactions with lecturers is increasingly discussed in higher education (Ronzhina et al. 2021 ; Sultana 2020 ). By now, it is widely recognized that digital technologies may support behaviorist, constructivist, collaborative, situated, and informal/lifelong learning (e.g., Criollo-C et al. 2021 ; Goksu 2021 ; Gupta et al. 2021 ). Thus, in the recent past, special attention was given to learning management systems (LMS), which are “web-based software platforms that provide an interactive online learning environment and automate the administration, organization, delivery, and reporting of educational content and learner outcomes” (Turnbull et al. 2020 , p. 1). The functionalities of today’s open-source (e.g., Moodle) or proprietary LMS solutions (e.g., Blackboard, WebCT of the University of Columbia) are diverse and range from course management to communication tools and progress tracking abilities amongst others (Al-Sharhan et al. 2020 ; Koh and Kan 2021 ). Although several studies have shown a positive effect of LMS usage on students’ learning performance (e.g., Leontyeva 2018 ; Msomi and Bansilal 2019 ; Oguguo et al. 2021 ), there are concerns that the new generation of “information consumers”—who are now entering the university system—will refrain from using LMS if these systems have not been optimized for mobile devices or just serve the provision of course materials (cf. Koh and Kan 2021 ; Turnbull et al. 2020 ).

As a consequence, higher education gradually focuses on the ubiquity and great acceptance of mobile phones (cf. Al-Bashayreh et al. 2022 ; Author self-citation 2; Beatson et al. 2020 ), which have become an integral part of students’ daily lives to establish and maintain social networks (Criollo-C et al. 2021 ; Diacopoulos and Crompton 2020 ; Goksu 2021 ). The COVID-19 pandemic even accelerated this development, as higher education was challenged to find alternative teaching options and students primarily interacted electronically with fellow students and instructors (e.g., Al-Bashayreh et al. 2022 ). Several studies show a positive effect of smartphone-based learning on student performance for various courses (e.g., accounting, psychology, etc.; cf. Beatson et al. 2020 ; Diliberto-Macaluso and Hughes 2016 ; Voshaar et al. 2023 ). In particular, research outlines the supportive impact of gamification on learning effectiveness (Pechenkina et al. 2017 ; Voshaar et al. 2023 ).

Against this backdrop, this research addresses the necessity for abovementioned “remedial support” (Baars and Arnold 2014 , p. 106) at initial stages of the student lifecycle to prevent first-year students from dropping out early and considers their affinity towards mobile phones equally. So, we focus on designing a mobile app for first-year students who are just about to develop a student identity and integrate into the student world (Lizzio 2011 ; Matheson 2018 ; Msomi and Bansilal 2019 ). We claim that a mobile app may be a suitable solution to tackle insufficient student experiences and support learning strategies as well as self-organization in the “transition-in” phase of the student lifecycle. We built a corresponding mobile app using a Design Science Research (DSR) approach, and the results of a first evaluation at the University of Bremen (Germany) encouraged us to pursue the project and develop the app further (cf. Johannsen et al. 2021 ). Further, in this research, a success model for mobile apps for first-year students in the “transition-in” phase, which is based on the IS success model of Delone and McLean ( 2003 ), is proposed and those factors that influence student satisfaction with the app, the intention to reuse the app, and students’ learning effectiveness are analyzed. This prepares the ground for formulating design principles for mobile app development afterwards. Accordingly, we pose the following research questions:

Which factors contribute to the success of a mobile app to support first-year students in the “transition-in” phase in terms of learning effectiveness, user satisfaction, and intention to reuse the app?

What design principles can be derived for a mobile app to support first-year students during the “transition-in” phase?

The contributions of this research are threefold: First, a self-developed mobile learning app with the aim to support students in the “transition-in” phase by improving learning strategies and self-organization abilities as well as promoting the perceived student experience is introduced. Thereby, we contribute to the ongoing search for (technological) solutions to prevent early student dropouts. Second, factors positively affecting user satisfaction, intention to reuse the app, and students’ learning effectiveness are identified with the help of a success model for mobile apps and data collected in an introductory accounting course at the University of Bremen (Germany). Based on that, mobile app functionalities can be assessed more purposefully regarding their relevance for first-year students, which complements the existing body of knowledge regarding student app design (e.g., Almaiah et al. 2022 ; Laine and Lindberg 2020 ). Third, the findings are used to formulate design principles (cf. Gregor and Hevner 2013 ) for mobile apps to support students in the “transition-in” phase, which are largely missing for apps that focus on this particular stage of the student lifecycle yet. Other institutions may reference these propositions to create beneficial mobile solutions for first-year students who strive to adapt to the university environment.

The structure of this paper is as follows: Section  2 provides an overview of mobile apps for higher education, the student lifecycle, and a self-developed mobile learning app to tackle challenges in the “transition-in” phase. Section  3 introduces the research model and describes the data collection. Afterwards, the results are presented (Sect.  4 ) and discussed (Sect.  5 ). The paper concludes with a summary and an outlook.

2 Conceptual basics and related work

2.1 the use of mobile apps in higher education.

The use of mobile apps in higher education teaching—such as “learning management applications”, “vodcasts and podcasts”, “language learning applications”, “game-based learning applications” or “collaborative learning applications” (Goundar and Kumar 2022 )—is discussed lively in the literature (e.g., Beatson et al. 2020 ; Gupta et al. 2021 ; Liu and Guo 2017 ; Ronzhina et al. 2021 ; Voshaar et al. 2023 ). In the following, we summarize related work on mobile learning apps in terms of potentials and challenges, technical and organizational issues, associated theories, as well as future developments. This should give readers a better overview of this mature research area.

In general, the potential of mobile student apps to support learning effectiveness is well-analyzed for various classroom and course examples (cf. Castek and Beach 2013 ). For instance, Larkin ( 2015 ) evaluates apps to foster the building of mathematical knowledge, while Diliberto-Macaluso and Hughes ( 2016 ) show that mobile apps may help psychology students achieve their learning objectives. Hence, apps can help to develop students’ self-regulation and deep thinking abilities or support them in labeling, summarizing, and discovering new knowledge amongst others (cf. Diliberto-Macaluso and Hughes 2016 ; Larkin 2015 ). In medical education , the benefits of mobile apps to offer an “enjoyable learning experience” are pointed out by Morris et al. ( 2016 ) for a neuroanatomy course. Mohapatra et al. ( 2015 ) present an overview of apps that are judged to be beneficial for medical education in general, with a particular focus on their ability to manage information from one or more sources to foster communication and support effective time management. Steel ( 2012 ) focuses on language students in particular and discusses the potential of mobile apps for this group, e.g., in terms of vocabulary acquisition. An overview of corresponding apps for language students is given by Gangaiamaran and Pasupathi ( 2017 ). In accounting and management , Beatson et al. ( 2020 ) and Voshaar et al. ( 2023 ) find out that students’ behavioral engagement with the help of mobile apps and gamification elements is positively associated with exam results. Seow and Wong ( 2016 ) introduce the so-called “Accounting Challenge (ACE)” app, which helps to keep up students’ motivation in studying accounting through gamification as well.

On the contrary, there are challenges of using mobile apps for student education. As Goundar and Kumar ( 2022 ) point out, the literature to date has a strong focus on “solution papers”, which introduce fully developed mobile applications that are supposed to improve learning performance. However, a discussion as to what degree singular app functionalities affect students’ cognitive knowledge processing or an explication of the implications for learning theories often come up short (e.g., Damyanov and Tsankov 2018 ). Along these lines, Mehdipour and Zerehkafi ( 2013 ) provide technical as well as social and educational challenges for mobile learning scenarios. These include content security and copyright issues, accessibility and cost barriers for end-users, or the lack of a learning theory for the mobile age in general, to mention just a few (cf. Mehdipour and Zerehkafi 2013 ). Furthermore, digital technologies may not adequately reproduce the emotional side of interactive learning, so attention should be given to the right balance between digital and human educational interactions (Montiel et al. 2020 ). A classification scheme for mobile learning challenges according to “management and institutional challenges”, “design challenges”, “technical challenges”, “evaluation challenges”, and “cultural/social challenges” is introduced by Damyanov and Tsankov ( 2018 ). In summary, education institutions need to establish a clear mobile learning policy, offer pedagogical support, consider the hardware capabilities of mobile devices, provide a suitable technical infrastructure, and deal with the cultural differences concerning perceptions and attitudes towards digital technologies (cf. Damyanov and Tsankov 2018 ).

From a technical perspective , the requirements on mobile learning environments, the core functionalities of apps to assure their practicability for educational purposes, and engineering processes for app realization are particularly important. In this context, Zhu et al. ( 2015 ) propose a design framework for mobile augmented reality education in healthcare. Further, Clayton and Murphy ( 2016 ) analyze mobile apps’ peer-learning and -teaching capabilities for conducting collaborative video design projects. The establishment of a content delivery infrastructure for educational material and suggestions on integrating mobile apps is done by Khaddage et al. ( 2011 ). Vázquez-Cano ( 2014 ) focuses on the mandatory capabilities of smartphones to support distance learning, while Pechenkina et al. ( 2017 ) identify the potential of gamification elements to increase student engagement, retention, and achievement. Finally, Papanikolaou and Mavromoustakos ( 2006 ) introduce critical success factors for learning app engineering processes, while Kumar and Mohite ( 2018 ) suggest approaches for testing their usability.

From an organizational perspective , the factors for successfully adopting digital technologies in higher education institutions are discussed (e.g., Chuchu and Ndoro 2019 ). It is accentuated that mobile learning initiatives are not limited to purchasing and deploying digital technologies but require a holistic consideration of diverse factors related to people, technology, or pedagogy (Krotov 2015 ). Thereby, principal factors that may impact user satisfaction, the intention to use, and the actual usage of mobile applications in higher education are examined (e.g., Almaiah and Alismaiel 2019 ; Chuchu and Ndoro 2019 ). As an example, Almaiah and Alismaiel ( 2019 ) focus on Jordanian universities and analyze two apps—one that provides student services (e.g., a timetable) and another one enabling “open virtual classes”—in light of the abovementioned factors. Thereby, so-called “quality factors” and “individual factors” that have been adapted from Delone and McLean ( 1992 ) and Davis ( 1989 ) seem to have a positive effect. Besides, also the variable “intention to use” was examined for this specific student group (cf. Almaiah and Al Mulhem 2019 ). Further, Chuchu and Ndoro ( 2019 ) present indicators that the “perceived usefulness” and “perceived ease-of-use” of a mobile learning app are central factors in creating a positive attitude among the target group and in ensuring its acceptance. An overview of critical success factors for mobile learning in organizations is provided by Krotov ( 2015 ). This study integrates the perspectives “organization” (e.g., executive involvement), “people” (e.g., personal innovativeness), “pedagogy” (e.g., quality of content provided), and “technology” (e.g., quality of mobile system) to arrive at a list of success factors from a socio-technical perspective (cf. Krotov 2015 ).

Considering the complex process of establishing mobile education technologies in organizations, a pedagogical and educational requirements model was proposed by Sarrab et al. ( 2018 ), which supports when searching for a suitable solution to deliver content for mobile learning. Besides, the role of mobile apps in facilitating the inclusion of students with handicaps into the university environment is a subject of investigation. For instance, Ok et al. ( 2016 ) introduce an evaluation scheme to purposefully select apps for students with learning disabilities. Moreover, people with developmental disabilities can benefit enormously from mobile apps, which hold true for educational, communication, and leisure purposes, helping them connect with their environment (Stephenson and Limbrick 2015 ). In addition, Bravou and Drigas ( 2019 ) reflect the suitability of mobile devices and apps for students with sensory, physical, and cognitive disabilities. In this respect, a comprehensive literature review on digital technologies for people with learning or cognitive disabilities was performed by Williams and Shekhar ( 2019 ).

Researchers are also engaged in theory building (cf. Hevner and Chatterjee 2010 ) to guide the purposeful usage of mobile apps in higher education. However, a widely accepted mobile learning theory has not yet been established (cf. Bernacki et al. 2020 ; Curum and Khedo 2021 ). Therefore, Park ( 2011 ) refers to the transactional distance theory (cf. Moore 1991 ), which defines “distance” as a pedagogical concept, and combines this theory with applications of digital technologies to arrive at a “pedagogical framework of mobile learning”. The framework distinguishes between four types of mobile learning depending on whether a (1) high or (2) low transactional distance is given and (3) an individualized or (4) socialized activity is to be solved. Thereby, the transactional distance is defined as the psychological gap between the learner and the instructor, whereas the activity type (i.e., individualized or socialized) assesses the importance of social aspects for a particular learning environment (Park 2011 ). Another mobile learning framework was introduced by Motiwalla ( 2007 ), who proposes to integrate the concepts “mobile connectivity” and “e-learning” for being able to delineate application requirements for mobile learning. Furthermore, a meta-framework to guide the establishment of mobile learning frameworks can be found in Liu et al. ( 2008 ). This meta-framework is, for instance, referenced by Nordin et al. ( 2010 ) as a theoretical base to create a lifelong, continuing learning framework.

Future developments of mobile learning apps will essentially emphasize the integration of Artificial Intelligence (AI) with learning environments (cf. Alzahrani et al. 2021 ; Chong 2019 ; Diaz et al. 2015 ; Kabudi et al. 2021 ). The purpose is to improve students’ learning performance via personalization of learning, facilitate the evaluation of student knowledge, or systematically assess learner requirements (Kabudi et al. 2021 ). Besides, the use of virtual reality (VR) and augmented reality (AR) to progress students’ learning experiences is intensively discussed (e.g., Fradika and Surjono 2018 ; Nicolaidou et al. 2021 ). For instance, Nicolaidou et al. ( 2021 ) show that a VR learning environment can positively affect vocabulary acquisition and learners’ experience when studying foreign languages. Further, the readiness of students to adapt VR technology to achieve learning goals is high (Ismail and Hashim 2020 ). Moreover, the use of chatbots and conversational agents is also rising (Hwang and Chang 2021 ; Liu et al. 2020 ; Smutny and Schreiberova 2020 ). Chatbots can serve as efficient information retrieval tools for specific domains to facilitate learning (cf. Liu et al. 2020 ). In this context, various platform-specific chatbots for learning (e.g., for the Facebook Messenger platform) at different maturity levels have been developed in recent years (cf. Smutny and Schreiberova 2020 ). Having said that, chatbots for education are primarily found for language courses as well as the disciplines of “engineering” and “computers”, while topics like “arts” or “mathematics” are less accentuated (Hwang and Chang 2021 ). Thus, chatbots may not be suitable for all types of courses alike, especially in case students’ hands-on competencies (i.e., arts) or computations and problem-solving skills (i.e., mathematics) are to be promoted. While the effectiveness of chatbots for learning purposes is usually measured by pre-/post-test questionnaires, profound insights on chatbots’ impact on behavioral aspects of the student learning process are still elusive (Hwang and Chang 2021 ).

To conclude this overview, our study aims to analyze factors contributing to the success of a mobile app, which was designed to meet the needs of first-year students in the “transition-in” phase of the student lifecycle. A particular interest is in the ability to positively impact their learning effectiveness, user satisfaction, and intention to reuse the app. To the best of our knowledge, a corresponding study concerning this stage of the student lifecycle has not been done yet. We provide insights that can help establish a mobile learning theory in the “transition-in” phase.

2.2 The student lifecycle and the “transition-in” phase

Throughout university life, students experience an evolution of their “student identity”, which goes along with a shift of priorities and agendas (Lizzio 2011 ). As mentioned above, our research focuses on “commencing” students who are just about to become acquainted with the university system and have an increased interest in opportunities for social interaction, active engagement, and early formative feedback (Matheson 2018 ). Generally, various propositions regarding the development stages of students exist (cf. Burnett 2007 ; Morgan 2013 ) that primarily differ in their conception of student transition (Gale and Parker 2014 ). A widely acknowledged proposition for an integrative framework was introduced by Lizzio ( 2011 ), which is depicted in Fig.  1 and differentiates between four major stages. Whereas future students (“transition-towards”) are engaged in finding an appropriate study program and university, commencing students (“transition-in”) work on the integration into the student world (Lizzio 2011 ). In the “transition-through” phase, continuing students work on developing graduate attributes and seek challenges by authentic curricula and assessments (Lizzio 2011 ; Matheson 2018 ; Msomi and Bansilal 2019 ). Finally, the “transition-up, out & back” stage addresses students that are graduating or returning for postgraduate studies to further strengthen their skills for employability (Lizzio 2011 ; Matheson 2018 ).

The student lifecycle according to Lizzio ( 2011 )

Against this background, most premature dropouts are observed in the “transition-in” phase (Chen 2012 ; Isleib et al. 2019 ; Neugebauer et al. 2019 ). An empirical study focused on German higher education institutions (60 universities and universities of applied sciences) identified a lack of social and academic integration as a major reason for premature dropouts (Isleib et al. 2019 ). More specifically, differences in the perception of study requirements were observed for dropout and non-dropout first-year students. These observations are generally also confirmed for other countries (cf. Chen 2012 ; Kehm et al. 2019 ; Xenos et al. 2002 ; Zvoch 2006 ). In terms of academic integration, many first-year students obviously struggle with self-organizing their studies and balancing the time slots for attending courses, obtaining credits, and preparing or post-processing lectures (Schulmeister 2007 ). In such cases, the danger of not meeting the academic standard and the probability of an early dropout increases significantly (Kehm et al. 2019 ). Consequently, the importance of promoting student retention and student achievements has been identified as a crucial responsibility of higher education institutions at that point (Matheson 2018 ; Sheader and Richardson 2006 ).

In the “Student Adjustment Model” of Menzies and Baron ( 2014 ), the stages experienced by first-year students when entering the university system are specified more in-depth, which helps to gain a deeper understanding of the overall “transition-in” phase. Hence, upon arrival, a sense of excitement can be observed among first-year students, which comes to a halt after some weeks when first negative experiences in the new environment have been made—a phase called the “party’s over stage” (Menzies and Baron 2014 ). Now students need to realistically assess their capabilities, identify gaps (e.g., self-organization skills) and carefully reflect on the institutional requirements (Matheson 2018 ). Here, universities can support by providing informative student feedback and curricula that offer opportunities for social networking and learning, or teaching methods that foster active learning and encourage student engagement (Matheson 2018 ; Whittaker and Brown 2012 ). Afterwards, students are able to enter the so-called “healthy adjustment stage” (Menzies and Baron 2014 ).

Furthermore, the base competencies of first-year students have been a subject of investigation (cf. Krumrei-Mancuso et al. 2013 ; Zehetmeier et al. 2014 ), which provides valuable insights into the academic skills of young people that contemporaneously enter the university system. Whereas some studies focus on special types of competencies—like digital (e.g., Reddy et al. 2020 ) or leadership competencies (e.g., Smart et al. 2002 )—a more extensive investigation at a German higher education institute, comprising 18 competency types in total, was performed by Zehetmeier et al. ( 2014 ). Concerning first-year students, deficiencies in self-organization, accurateness, perseverance, intrinsic motivation, or self-criticism were described (Zehetmeier et al. 2014 ). Based on these findings, universities should develop solutions that can account for diverse student backgrounds, tackle insufficient experiences, and support individual learning and self-organizational strategies to achieve academic success.

2.3 Overview of a mobile app to support students in the “transition-in” phase

Considering this, we argue that a mobile app may be a suitable solution to tackle the abovementioned challenges (e.g., insufficient student experiences, lack of self-organization, etc.) in the “transition-in” phase of the student lifecycle. To provide a general overview, Table 1 gives a brief selection of campus apps that come to use at German universities. Of course, campus apps can be found internationally (e.g., UC San Diego mobile app) (e.g., Almaiah and Alismaiel 2019 ; Holotescu et al. 2018 ).

According to the mobile learning app ontology of Notari and Hielscher ( 2016 ), the majority of mobile campus applications are designed as “learning and teaching support apps” with diverging functionalities and purposes. As a common denominator, almost all of them provide campus maps, an overview of cafeteria offerings, official timetables, or event directories, while some of them (e.g., apps of the University of Arts Bremen, University of Hohenheim) also enable access to learning content or the registration for exams. However, communication functionalities are rare since students usually evade to commercial apps like Facebook and Instagram to share information with their peers (cf. Statista 2020b ).

Besides, a broad range of commercial apps supports users in organizing their daily lives, e.g., for scheduling daily routines and tasks (e.g., “24me”, “Todoist”), structuring brainstorming ideas (e.g., “MindNode”), or tracking fitness activities (e.g., “FitNotes”, “MyFitnessPal”). Such mobile apps are these days usually well-integrated into young peoples’ lives (Goodyear et al. 2019 ; Statista 2020a ). However, this does not necessarily hold true for campus apps that run danger of not being further developed as soon as students lose interest or their commitment to using the app (Potgieter 2015 ). That may be especially true if a campus app provides (redundant) content already readily available elsewhere (e.g., the university’s homepage or LMS).

In light of the above explanations, we aimed to provide a mobile app that supports first-year students academically during the “transition-in” stage of the student lifecycle and is easily and practically integrable into everyday student life to ensure long-term student acceptance and commitment. The app was designed to combine functionalities of commercial apps to organize daily routines (e.g., “24me”, etc.) with university-related content, functionalities (e.g., timetables, define tasks and goals, etc.), and gamification elements. The design and development of the app are also described in a prior work in more detail (cf. Johannsen et al. 2021 ).

Our app’s target user group is business and economics students at the University of Bremen (Germany). We initially focus on this narrow group because requirements can be specified more precisely, and the authors are well acquainted with study-related challenges of this relatively homogeneous user group. Though, adapting the app to the needs of other departments and universities is generally possible. The app was developed in a DSR project (Baskerville et al. 2018 ; Peffers et al. 2007 ) with the goal of supporting students’ experience, learning strategies, and self-organization in the “transition-in” phase. Considering this, our artifact is based on three meta requirements (cf. Gregor and Hevner 2013 ) to address the student factors “student experience”, “learning strategies”, and “self-organization”, which are of utmost importance to successfully tackle the transition into the university environment (cf. Blüthmann et al. 2011 ; Heinze 2018 ; Neugebauer et al. 2019 ) (Fig.  2 ). In DSR, meta requirements define “what the system is for” (Gregor and Jones 2007 , p. 325) and outline the purpose and scope of the type of artifact to be developed (Gregor and Jones 2007 ; Schmid et al. 2022 ) in our case a mobile solution for first-year students. Referring to the abovementioned student factors that have been derived from the literature (see Sect.  2 ), the following meta requirements were formulated:

MR 1: The mobile app should support students’ experience.

MR 2: The mobile app should improve students’ learning strategies.

MR 3: The mobile app should support students’ self-organization.

Overview of design requirements

Thereby, the term “student experience” subsumes “all experiences of an individual student” while being in the “identity as a ‘student’” including all “facets of the university” (e.g., administrative processes, IT support etc.), which “contribute” to the “personal development” as a learner (Baird and Gordon 2009 , p. 194).

To specify the meta requirements and arrive at design requirements for the app, (I) user stories, (II) market research, (III) user requirements, and (IV) user journeys were used (Schilling 2016 ). In this context, also second- and third-year undergraduates (N = 54), who are still well familiar with the challenges experienced at the beginning of their studies were surveyed. Finally, we came up with eight major design requirements classified into the categories “course attendance/reminders” (Fig.  2 —DR 1-2), “support of study phases” (DR 3-5), and “technical requirements” (DR 6-8) to support students’ experience, learning strategies, and self-organization (cf. Fig.  2 , Johannsen et al. 2021 ).

The architecture of the app consists of a front- and a back-end. The frontend was developed with the help of the IONIC Framework ( https://ionicframework.com/ ), which works based on Angular ( https://angular.io/ ) (Green and Seshadri 2013 ). Further, the back-end was realized via the Spring Framework ( https://spring.io/ ) and the Spring Boot solution (cf. Walls 2016 ). Figure  3 shows exemplary screenshots. So, a new course is added to a student’s timetable (Screenshot 1), and sample functionalities for this course—derived from the design requirements—are shown, such as the conduction of quizzes (Screenshot 2) or the comparison with a peer group (Screenshot 3). The app can be classified as type 2 (i.e., high transactional distance and individualized mobile learning activity) in the “pedagogical framework of mobile learning” of Park ( 2011 ). Hence, this type allows a high degree of flexibility and portability, enabling students to integrate it flexibly into their mobile lifestyle (Park 2011 ).

Screenshots of the application prototype

With respect to the challenges in the “transition-in” phase (see Sect.  2.2 ) and the meta requirements, various functions that support students’ learning strategies, experience, and self-organization are offered by the app. For instance, the features of tracking learning time along with an overview of exam dates and events largely foster students’ self-organization abilities (cf. Zehetmeier et al. 2014 ). This is further supported by push notifications or newsfeeds about new learning content as well as a calendar function with reminders for lectures and important academic dates contributing to student experience (e.g., Staddon and Standish 2012 ; Trotter and Roberts 2006 ). Gamification is used to enrich students’ learning strategies (e.g., performance tests via quizzes), while they can work on exercises independent of time and place.

3 Research design

In the following, we present the research model, the hypotheses, and the data collection. We heavily rely on the IS success model of Delone and McLean ( 1992 ), which is a commonly referenced model to measure the success of information systems, and has been referenced in many studies in the field of technology-supported education (cf. Almaiah and Alismaiel 2019 ; Aparicio et al. 2017 ; Cidral et al. 2018 ; Dorobat 2014 ; Holsapple and Lee‐Post 2006 ; Huang et al. 2015 ; Kruger-Ross and Waters 2013 ; Wang et al. 2019b ). Hence, it arguably is one of the most widely applied models in this field (Almaiah and Alismaiel 2019 ).

When it comes to user acceptance of technologies also the TAM (Technology Acceptance Model) approach is discussed in literature (e.g., Davis et al. 1989 ; Liu and Guo 2017 ; Mohammadi 2015 ). According to TAM, the factors influencing the acceptance and usage of technologies can be categorized into the clusters “external variables”, “perceived usefulness”, and “perceived ease of use” (Davis 1989 ; Davis et al. 1989 ). Nevertheless, the model is criticized since it mainly focuses on individuals’ perception of technology, while the context in a business, university, or organizational setting (e.g., policy, IT guidelines) is neglected (Ajibade 2018 ).

In this light, the IS success model is particularly suitable for our research for several reasons: First, its quality dimensions can be easily aligned with web-based applications (cf. Delone and McLean 2003 ; Efiloğlu Kurt 2019 ), which are dominant in e-learning environments to foster students’ learning activities (Freeze et al. 2010 ; Muhammad et al. 2020 ). Our mobile app (see Sect.  2.3 ) represents a corresponding solution to support student learning, whereby the querying of database information (e.g., timetables), the login-logics, or the provision of content (e.g., training questions) are enabled by a backend server, while the data is sent to the frontend by help of the JSON and HTTP standard. Further, the mobile app considered in this study can be allocated to the “communication and system phenomenon” (Freeze et al. 2010 ) of e-learning solutions, for which not only the quality of the system is of interest but also the communication with a “service provider”, who creates study-relevant content, provides advice, or resolves problems (cf. Aparicio et al. 2017 ). The IS success model explicitly covers these aspects by corresponding constructs and, thus, represents the base of our research model introduced hereafter, whereas the quality dimensions are adapted to the study context as proposed by Delone and McLean ( 1992 ).

Second, although the model has already been intensively used in education research for years, the focus of this study is on a mobile app that was designed for the “transition-in” phase in particular and represents an instance of a “type 2 app” according to the “pedagogical framework of mobile learning” of Park ( 2011 ). In literature, there is a lack of knowledge regarding the success factors for apps of this type with a special focus on first-year students. Considering this, using a widely established success model and quality dimensions is promising to prepare the ground for further developments of similar solutions. Hence, results attained in other studies with the help of the IS success model may not necessarily be confirmed for the type of mobile app investigated herein. Furthermore, the way to compare the impact of the IS success model across studies is paved and, hence, the relevance of singular dimensions of IS success for various types of apps directed at different stages of the student lifecycle may be assessed more profoundly in the next steps.

Third, for being able to derive design principles that allow the creation of similar instances of artifacts that belong to the identical class (cf. Kruse et al. 2016 ; Sein et al. 2011 ), the use of the widely accepted IS success model is promising. This is because its success dimensions have already been broadly recognized and therefore represent a solid base for defining verifiable and comprehensible design principles. These may be extended in future steps as soon as the knowledge about beneficial mobile app development for the focused application field evolves along with additional insights about beneficial success dimensions for learning effectiveness. The proposed research model, its variables, and our hypotheses are introduced in the following section.

3.1 Theoretical model and hypotheses development

3.1.1 system quality.

According to Delone and McLean ( 1992 ), system quality is a central success factor for IS. The variable describes the desired characteristics of the information system to produce the required information (Urbach et al. 2009 ). Thereby, Wang et al. ( 2019b ) have shown that the system quality of paid mobile learning apps has a positive impact on “user satisfaction” and the “intention to (re-)use”. Similar results for mobile learning apps at Jordanian universities were introduced by Almaiah and Alismaiel ( 2019 ). Though, a study on an e-learning system in Brazil was less clear about the beneficial role of system quality (cf. Cidral et al. 2018 ). Moreover, Aparicio et al. ( 2017 ) investigated “grit” as a determinant of “e-learning system success” and confirmed the supportive effect of system quality on “user satisfaction”. A positive effect on “user satisfaction” was also shown by Chiu et al. ( 2016 ) for a “cloud e-bookcase system” for libraries, whereas Huang et al. ( 2015 ) identified a positive impact on both, “intention to (re-)use” and “user satisfaction” for a mobile library service system. Considering literature and the preferences of business students concerning mobile applications (e.g., Kouser et al. 2014 ), an app’s ease of use (Wang et al. 2019b ), its structuredness (Cidral et al. 2018 ; Urbach and Müller 2012 ), an easy-navigation (Kouser et al. 2014 ), and the ability to efficiently retrieve relevant information (Wang et al. 2019a ) are highly appreciated by the target group in terms of system quality (Aparicio et al. 2017 ; Urbach et al. 2010 ). Hence, we hypothesize:

H1a: System quality will have a positive effect on first-year students’ intention to reuse the app.

H1b: System quality will have a positive effect on first-year students’ satisfaction with the app.

3.1.2 Service quality

The construct “service quality” refers to the overall support for users offered by a service provider (Delone and McLean 2003 ). In terms of “e-learning”, Aparicio et al. ( 2017 ) emphasize the importance of the willingness and readiness of the support staff to resolve students’ difficulties at any time because this positively influences the intention to use the system. This positive effect was also confirmed in earlier studies (e.g., Chiu et al. 2016 ; Huang et al. 2015 , among others). Generally, “service quality” may be interpreted from different angles and refer to concepts such as assurance, empathy, or flexibility—just to mention a few (Urbach and Müller 2012 ). In alignment with the propositions of Aparicio et al. ( 2017 ) and Urbach et al. ( 2010 ), we see the willingness of the service personnel to provide support upon request immediately, the personal attention offered to students, the timeliness of the service response as well as the competence and knowledge of the service personnel as central factors for the app’s success. Considering this, we claim:

H2a: Service quality will have a positive effect on first-year students’ intention to reuse the app.

H2b: Service quality will have a positive effect on first-year students’ satisfaction with the app.

3.1.3 Information quality

Information quality addresses the system output or the information that is produced by a system (Delone and McLean 1992 ). According to Almarashdeh et al. ( 2010 ), information quality is the most crucial factor when determining the success of educational technology systems (Almaiah and Alismaiel 2019 ). Hence, the positive impact of information quality on the “intention to (re-)use” and “user satisfaction” is confirmed by manifold studies that focus on e-learning or mobile learning systems (e.g., Aparicio et al. 2017 ; Cidral et al. 2018 ; Wang et al. 2019b ). However, there are also studies in which information quality played a subordinate role for the acceptance of a system (cf. Chiu et al. 2016 ). Once more, the construct “information quality” can be reflected from various perspectives such as data accuracy, adequacy, or completeness (cf. Klier 2008 ; Urbach and Müller 2012 ). For our app, we determine information quality based on the reliability and understandability of the information provided and its usefulness and relevance for the target group (cf. Aparicio et al. 2017 ; Urbach et al. 2010 ).

H3a: Information quality will have a positive effect on first-year students’ intention to reuse the app.

H3b: Information quality will have a positive effect on first-year students’ satisfaction with the app.

3.1.4 Perceived enjoyment

Davis et al. ( 1992 ) summarize enjoyment in the information systems context as “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (p. 1113). Against this background, the construct of “perceived enjoyment” is increasingly getting attention when it comes to the measurement of IS success (cf. Kim et al. 2007 ; Wang et al. 2019b ). Therefore, it is suggested that technology adoption is more likely in cases where users experience immediate pleasure or joy through mere use (Kim et al. 2007 ). Since the positive influence of perceived enjoyment on users’ attitudes is well examined in the mobile services and mobile commerce context (cf. Tseng and Lo 2011 ; Wang and Li 2012 ; Wang et al. 2019b ), it is increasingly discussed in terms of e-learning technologies, as well (cf. Balog and Pribeanu 2010 ; Hussein 2018 ; Khalid 2014 ). Hence, we also assume a positive effect on “user satisfaction” and “intention to (re-)use”. In this regard, gamification elements may purposefully impact the hedonic motivation to engage with mobile apps and, as a positive side-effect, impact users’ perceived enjoyment (cf. Beatson et al. 2020 ; Pechenkina et al. 2017 ; Wang et al. 2019b ).

Generally, gamification is seen as a means to overcome a lack of motivation among students to deal with study-related content (cf. Kiryakova et al. 2014 ). Thereby, principles such as “freedom to fail”, “rapid feedback”, “progression”, or “storytelling” play a decisive role for the successful application of gamification elements in learning environments (Stott and Neustaedter 2013 ). Hence, specific mechanisms that are traditionally used in game design (e.g., Laine and Lindberg 2020 ) to increase user engagement and hedonic motivation have found their way into modern pedagogy, although their purposeful selection should be made in regards to the target group (Stott and Neustaedter 2013 ). For the design of mobile education apps, corresponding principles need to be purposefully transferred to corresponding design requirements (cf. Herrington et al. 2009 ; Laine and Lindberg 2020 ). Section  2.3 presents the design requirements of our mobile app (see also Fig.  3 ), whereas these are taken up in Sect.  5.2 once again and reflected against the findings of the study.

In summary, we determine perceived enjoyment based on the fun and enjoyment experienced by app users (cf. Kim et al. 2007 ; Wang et al. 2019b ) and the abilities of entertaining and playful features to enhance users’ learning experience and structure their learning efforts (cf. Suki and Suki 2007 ).

H4a: Perceived enjoyment will have a positive effect on first-year students’ intention to reuse the app.

H4b: Perceived enjoyment will have a positive effect on first-year students’ satisfaction with the app.

3.1.5 Intention to reuse, perceived user satisfaction, and learning effectiveness

“Intention to use” is specified as users’ intent to perform a defined behavior (Davis 1989 ). The construct is acknowledged to be strongly associated with the acceptance of an information system (Almaiah and Alismaiel 2019 ) and it largely depends on the users’ attitude towards the system (Agrebi and Jallais 2015 ). However, there is a distinct difference between “intention to use” and actual “use”, because the former represents an attitude, whereas the latter concept describes a concrete behavior (Delone and McLean 2003 ). To resolve the closed-loop relationships between user satisfaction, intention to use, and use in the original IS success model (Wang et al. 2019b ), “intention to reuse” is commonly proposed as a worthwhile measure (Delone and McLean 2003 ; Wang 2008 ). In line with the proposition of Wang ( 2008 ), “intention to reuse” thus represents the favorable student attitude towards our app in this study.

In addition, “perceived user satisfaction” helps to measure the successful interaction of users with the IS (Delone and McLean 1992 ). Generally, user satisfaction can be interpreted as “the extent to which users believe the information system available to them meets their information requirements” (Ives et al. 1983 , p. 785). Thereby, perceived user satisfaction leads to an increasing “intention to reuse” in the post-use situation (Wang 2008 ).

Either way, the major purpose of mobile learning technologies is to increase knowledge acquisition (cf. Wang et al. 2019b ) and, hence, improve learning outcomes (cf. Noesgaard and Ørngreen 2015 ). Generally, the beneficial individual or organizational impact of IS, which is supposed to be measured by the IS success model may occur in many ways (e.g., awareness/recall, competitive advantage, etc.) (cf. Delone and McLean 2003 ; Urbach and Müller 2012 ). Considering this, there is a lively discussion on how to operationalize the individual benefits of using e-learning technologies that cumulate in better knowledge acquisition and learning outcomes in the end (e.g., Chiu et al. 2016 ; Noesgaard and Ørngreen 2015 ; Wang et al. 2019b ; Zhang et al. 2006 ). In that context, “learning effectiveness” (cf. Noesgaard and Ørngreen 2015 ) has become a commonly accepted measure to assess the success of technology-assisted learning for individuals (Smith et al. 2006 ; Wang et al. 2019b ; Zhang et al. 2006 ). The variable builds on the recognition that effective learning asks for learners’ engagement, motivation, awareness, and an individualized learning process, which can be enabled by offering access to content randomly or repeatedly on demand for instance (Zhang et al. 2006 ). This, in turn, promotes learning skills (e.g., enhanced problem-solving or critical thinking abilities; Zhang et al. 2006 ) and leads to an improved understanding of study-related content, which can be recollected any time (cf. Chiu et al. 2016 ; Gable et al. 2008 ; Wang et al. 2019b ). Hence, improved knowledge acquisition and learning outcomes emerge from a general point of view.

To properly address these considerations, the literature proposes to ask for students’ perceptions of learning performance, efficiency, motivation (cf. Liaw 2008 ), awareness, and recollection of study-related information (Gable et al. 2008 ) along with their understanding of the course content (cf. Chiu et al. 2016 ). Accordingly, these aspects determine the items of our questionnaire to assess the variable “learning effectiveness” (see Appendix).

As evident from the above explanations, a rather broad spectrum of factors (e.g., awareness, motivation, etc.) is required to describe “learning effectiveness” comprehensively. Nevertheless, the variable allows students to carefully reflect on the achieved individual (net) benefits (cf. Delone and McLean 2003 ) when using a mobile learning app to cope with the challenges of the “transition-in” phase. Therefore, the variable is used hereafter to measure students’ (net) benefits since we believe that other variables that have been proposed in the context of the IS success model (e.g., recall, job simplification, etc.) (cf. Urbach and Müller 2012 ) would not comply with the multidimensionality of first-year students’ learning success in the “transition-in” phase and may not be adequately transferred to our context.

We formulate the following hypotheses:

H5: The perceived user satisfaction will have a positive effect on first-year students’ intention to reuse the app.

H6: The intention to reuse will have a positive effect on first-year students’ learning effectiveness.

H7: The perceived user satisfaction will have a positive effect on first-year students’ learning effectiveness.

Figure  4 summarizes the proposed research model, variables, and hypotheses.

Proposed research model

3.2 Design of the questionnaire

We developed a questionnaire based on the abovementioned established and validated scales from previous studies and modified them accordingly for the mobile learning context to test our hypotheses. As previously described, the constructs of “system quality”, “service quality”, and “information quality” were adapted from Aparicio et al. ( 2016 ) and Urbach et al. ( 2010 ) and are all measured by four underlying items. “Perceived enjoyment” consists of five items, three being adapted from Kim et al. ( 2007 ) and Wang et al. ( 2019b ), and two used by Suki and Suki ( 2007 ). Three items were adapted from Wang et al. ( 2019b ) and Wang ( 2008 ) and are complemented by one item each from Chiu et al. ( 2016 ) and Sun et al. ( 2008 ) to measure the construct “intention to reuse”. “Perceived user satisfaction” was measured by four underlying items used by Liaw ( 2008 ). Finally, we adopted three items from a previous study of e-learning effectiveness from Liaw ( 2008 ) and added one item each from Chiu et al. ( 2016 ) and Gable et al. ( 2008 ) in order to measure “learning effectiveness”.

Initially, we developed the survey in English, in accordance with prior research, and then translated it to German through a professional translation service in order to ensure a low-threshold participation opportunity and, thus, a high number of participants. Subsequently, a different professional translator translated it back into English to ensure conversion correspondence (Brislin 1970 ). As previously described, the constructs were unanimously measured with four or five items each. All items were assessed on a seven-point Likert scale (from 1 = “strongly disagree” to 7 = “strongly agree”). Table  4 in the Appendix presents the final survey consisting of the mentioned items used in our research model.

3.3 Data collection and sample selection

The mobile learning app was initially implemented in a mandatory introductory accounting course in the Winter semester 2020/21. Because of the COVID-19 pandemic, the social distancing requirements, and the sudden closures of university campuses, all lectures were held digitally in an asynchronous format via screencasts. Complementing the lectures, students could participate in synchronous, live tutorials and submit exercise sheets. Additionally, preparatory courses were also offered synchronously via Zoom. For our study, we invited all students who used the mobile learning app at some point in the Winter semester 2020/21 to participate in the online survey conducted during the final week of teaching (i.e., before the final exam) and administered on the university’s LMS. We did not offer any additional (e.g., monetary or extra course credit) incentives for participating, and the students were informed of the research purpose and their voluntary participation in the study. Even if they took part in the survey, they had the possibility to refuse to answer any question. Subsequently, one member of the research team, who was not involved with the empirical analysis, merged and pseudonymized the data from students’ questionnaires with data from several other sources, including students’ demographics being collected through another survey in the first week of the semester, students’ course attendance during the semester, and the academic performance data. More specifically, the students’ attendance at tutorials and workshops has been manually evaluated via Zoom participation protocols. Finally, the central examination office provided the student’s exam performance. In our analysis, we only use the final pseudonymized dataset, which does not allow identification of individual students.

The students were asked to answer the questionnaire according to their user experience throughout the semester. Thereby and due to the requirements of the IS success model, we were ex-ante limited to the population of 367 students who used the app during the semester and participated in the final exam to draw our sample. Our initial sample consists of 131 students who participated in our survey regarding their user experience. Out of the initial sample, we exclude 10 observations due to missing values in their survey responses and 1 without any variation in the responses. Hence, we received 120 usable responses, bringing our usable response rate to 91.60%. Further, we exclude 7 students because of missing values in their demographics, resulting in a final sample of 113 students who participated in the final exam Footnote 1 of the mandatory introductory accounting course and used the mobile learning app for learning purposes. Accordingly, our sample represents 30.79% of the underlying population that could be used for a study of this type. Footnote 2 The final sample comprises 63 female and 50 male students, with the overwhelming majority (94.69%) being 25 years old and younger. Table  2 presents the summarized descriptive statistics for the final sample of 113 students. Footnote 3

3.4 PLS-SEM approach

Our research model was evaluated using PLS-SEM as the most favorable method to validate multistage models with complex relationships, interdependencies, constructs, and indicators (Hair et al. 2011 ; Sarstedt et al. 2016 , 2021 ). We thereby followed recent recommendations as suggested by Hair et al. ( 2019 ) and Sarstedt et al. ( 2021 ). The minimum sample size was ascertained by multiplying the total number of constructs by ten (Hair et al. 2011 ; Marcoulides et al. 2009 ) and met with our 113 participants. The construct indicators in our model represent reflective measurements caused by latent variables (Churchill Jr 1979 ). We used SmartPLS (v. 3.3.2; Ringle et al. 2015 ) and applied a path weighting scheme with 300 iterations with \({10}^{-7}\) as the stop criterion. Bootstrapping was done via two-tailed bias-corrected and accelerated (BCa) confidence interval method with 4,999 subsamples followed by blindfolding with an omission distance of 7 (Henseler et al. 2016 ).

Initially, we ensured that the indicator loadings are above the threshold of 0.708. Slightly weaker indicators were only kept if they contribute to content validity and are relevant on the grounds of measurement theory (Hair et al. 2011 ). Internal consistency was given with Cronbach’s alpha, composite reliability, and Rho_A with values greater than 0.7 (Diamantopoulos et al. 2012 ; Dijkstra and Henseler 2015 ; Drolet and Morrison 2001 ; Hair et al. 2019 ). Convergent validity was measured via average variance extracted (AVE) with values greater than 0.5 (i.e., at least half the variance of the construct’s items is explained; Fornell and Larcker 1981 ; Hair et al. 2019 ; Henseler et al. 2016 ). Table  5 in the Appendix presents the detailed results of the reliability and validity measurements. The Fornell-Larcker criterion (Fornell and Larcker 1981 ) was examined to assess the discriminant validity, which can be assumed as the square root of AVE is greater than any inter-factor correlation (see Table  7 in the Appendix; Fornell and Larcker 1981 ). Common method bias (CMB) was examined via Harman’s one-factor test for a full collinearity assessment approach. The values for the variance inflation factors (VIF) were below the threshold of 3.30 (see Table  8 in the Appendix; Kock 2015 ). Finally, we analyzed cross-loadings to rule out misassigned indicators (Henseler et al. 2016 ).

Statistical significance was provided with p-values lower than or equal to 0.05 and t-statistics greater than 1.96 (Greenland et al. 2016 ). Cohen’s f 2 indicates statistical relevance, where effect sizes are considered small, 0.02 < f 2  ≤ 0.15; medium, 0.15 < f 2  ≤ 0.35; or large, f 2  > 0.35 (Cohen 1988 ). The exploratory power of the model was measured using R 2 , which ranges between 0 and 1 where higher values indicate greater explanatory power (Hair et al. 2011 ; Reinartz et al. 2009 ). The Stone-Geisser Q 2 measure was calculated to support explanatory significance, that is, explaining how well the data could be (artificially) reproduced by the research model (Geisser 1974 ; Stone 1974 ). We achieved predictive accuracy with results above 0 where values greater than 0, 0.25, and 0.5 are considered as small, medium, and large effect sizes, respectively (Hair et al. 2019 ). Goodness of fit (GoF) is assessed using AVE and the adjusted R 2 . Our value of 0.78 is above the threshold of 0.36 (Wetzels et al. 2009 ), which indicates a valid model. We finally controlled our model using the participants’ age, grade, courses of study, and current semester, which we unanimously found not to be significant for the research question at hand. The final results of our evaluation are presented in Fig.  5 , and Table  3 provides an overview of the results for the hypotheses.

Research model with results (N = 113). * p  ≤ 0.05; ** p  ≤ 0.01; *** p  ≤ 0.001; n.s. = not significant

5 Discussion and benefits for research and practice

5.1 factors that contribute to the success of a mobile app to support first-year students in the “transition-in” phase in higher education.

As mentioned, our app’s key user group are students who have just entered the university system. This focus on the “transition-in” phase of the student lifecycle differentiates our study from prior literature in this field (e.g., Almaiah and Al Mulhem 2019 ; Cidral et al. 2018 ; Wang et al. 2019a ). Furthermore, we focus on students at a German university and, hence, in a typical Continental European higher education setting. The main differences between the Continental European and the Anglo-Saxon model of higher education arise primarily through universities’ funding and tuition fees. The Continental European model is characterized by state sponsorship of universities and free or very low tuition (e.g., Jongbloed 2004 ). As a result of this greatly reduced financial burden, students from disadvantaged backgrounds can also participate in higher education, and, therefore, the student population might be more (economically) diverse (Lenzen 2015 ). At the same time, limited state funding results in large lectures and a high student-lecturer ratio, which probably disadvantages students in need of greater guidance and, thus, increases the need for technological learning solutions.

At first, the research indicates a positive effect of system quality on perceived user satisfaction (H1b), a finding in line with prior results (e.g., Almaiah and Alismaiel 2019 ; Aparicio et al. 2017 ). As expected, factors like ease of use, easy navigation, structuredness, and the ability to efficiently retrieve relevant information help to increase user satisfaction. However, this effect could not be observed regarding the impact of system quality on the intention to reuse the app (H1a). A possible explanation for this finding, which is in line with prior literature (Aparicio et al. 2017 ; Chiu et al. 2016 ) is that students tend to use a system independently from its perceived quality, in case a university has committed to this particular system. Transferred to our context, the developed mobile app is the only solution of its kind. As such, first-year students obviously use it—regardless of their perception of system quality—as a means to (potentially) increase their learning performance. Nevertheless, further analysis of further factors seems promising to better understand the relationship between system quality and the intention to reuse the app (cf. Almaiah and Al Mulhem 2019 ).

Second, contrary to the proposed expectation in hypotheses H2a and H2b, service quality neither significantly impacts the intention to reuse nor perceived user satisfaction (see Table  3 or Fig.  5 ). Thus, service quality seems to be a minor issue in evaluating the mobile app’s benefits in terms of learning effectiveness. This is also reasonable as we noticed in the field that app users rarely contact the administrative or teaching staff for help with respect to mobile app usage. Likewise, previous research finds evidence that service quality is relatively less important in the context of knowledge-orientated information system success (e.g., Wang et al. 2019b ; Wu and Wang 2006 ).

Third, information quality positively impacts the perceived user satisfaction in our study (H3b). Accordingly, the reliability, understandability, and relevance of the information provided by the app for students in the “transition-in” phase are greatly appreciated by our target group. However, no significant impact on the intention to reuse could be observed (H3a). A similar finding is presented by Chiu et al. ( 2016 ). As a potential explanation, students may believe that using the app is decisive for a successful start of studies, and therefore, they do not draw the presented information into question. This assumption aligns with the observation that students’ critical thinking abilities are only starting to take shape in their first year at university and significantly increase in the subsequent semesters (cf. Ralston and Bays 2015 ; Wallace and Jefferson 2015 ). Hence, analyzing students from more advanced semesters might lead to different results.

Fourth, the effects of perceived enjoyment on both the intention to reuse (0.407) and user satisfaction (0.346) are significantly positive and greater than the impact of system quality, service quality, and information quality. These findings support our hypotheses H4a and H4b. Therefore, since perceived enjoyment is the only construct that influences both intention to reuse as well as perceived user satisfaction, it can be stated that the enjoyment and joy-related aspects are of utmost importance to promote learning effectiveness in our research setting. This result is quite striking and provides further evidence on the value of gamification in higher education, a field of research which is still quite in its infancy.

Fifth, we find support that perceived user satisfaction mediates the effects of system quality, information quality, and perceived enjoyment on the intention to reuse (H5). This is also reasonable since it might be more likely that students intend to reuse the app when their level of satisfaction is high. Additionally, this mediating effect might be why the service quality (H2a) and the information quality (H3a) are not directly associated with the intention to reuse. We also find evidence that intention to reuse significantly positively affects learning effectiveness (H6). In other words, the greater the likelihood to reuse the app, the greater the app’s positive effect on students’ learning effectiveness.

Lastly, perceived user satisfaction plays the most important role in determining students’ learning effectiveness, supporting our hypothesis H7. Since user satisfaction influences learning effectiveness, both directly and indirectly as mediated by the intention to reuse, it has the most significant total impact on learning effectiveness (0.448 + (0.445 × 0.426) = 0.6376). Moreover, it also has the most substantial direct effect (0.448), while the intention to reuse has a slightly lower impact (0.426).

5.2 Linking the factors to design requirements and derivation of design principles

As described above, system quality, information quality, and perceived enjoyment positively affect perceived user satisfaction, while the latter also promote the intention to reuse. Furthermore, perceived user satisfaction and intention to reuse have a supportive influence on students’ learning effectiveness. In this light, we now compare and contrast the design requirements and their realization as app functionalities with the influencing factors established before (see Fig.  6 ). By that, we contribute to discussing how digital technologies can be purposefully designed to support student retention in higher education. Hence, our artifact and the design requirements may serve as propositions for developing similar student apps specifically for the “transition-in” phase. Furthermore, based on the findings and the instantiated artifact, we derive design principles (cf. Gregor and Jones 2007 ) for mobile app creation in this field.

Linkage to design requirements

First, considering information quality, the option to self-track one’s course attendance and analyze this data against the course offerings during the semester (DR 1), the functionality to allow pop-up messages as reminders for lectures and academic events (DR 2) as well as the availability of training and exam-oriented exercises to control the learning process (DR 3) have proven useful to provide understandable, interesting, and reliable information to first-year students. Thus, regarding DR 1, the user may navigate to a site called “course overview” after login, showing the portfolio of courses the user has registered for. There, the app provides the option to confirm attendance for lectures. On a more detailed level, the user can create a time tracker for each course on demand, which allows for tracking attendance and self-study periods. Concerning DR 2, students are notified via push messages if an important event (e.g., lecture, exam registration deadline) in a course for which they have registered is about to take place. As a further option, students can export the course or event dates to their personal smartphone calendars. Via the site “exercises”, exam-oriented exercises are offered to check one’s personal learning process (DR 3). At this point, the training content also comprises learning videos and course scripts, which can be accessed at any time, enabling students to adjust the pace of learning at their own discretion.

The information quality offered to students with the help of the abovementioned functionalities (see Fig.  6 ) supports them in planning their participation in academic events and, thus, integrating into the daily student life (student factor “student experience”). Furthermore, they can improve their self-organization and adjust their learning strategies in accordance with the results that were scored for the exam-oriented exercises for instance (student factors “self-organization” and “learning strategies”).

To increase users’ perceived enjoyment, gamification elements like quizzes (DR 4) and competitions with the peer group are available (DR 5). This is meaningful since a lack of motivation and engagement to participate in the learning process has been identified as a major challenge in contemporaneous education (Hassan et al. 2021 ; Kiryakova et al. 2014 ). In order to reach a higher level of commitment and motivation among students, we propose the functionalities DR 4 and DR 5. Therefore, the game design mechanisms “freedom to fail” and “rapid feedback” (Stott and Neustaedter 2013 ; Thakur et al. 2020 ) were purposefully transferred to our learning app. We consider both mechanisms as helpful for first-year students to reduce mental barriers to interact with fellow students and teachers. The principles can be ideally addressed by quizzes, which give students direct feedback, even beyond the classroom, and disassemble psychological barriers to “fail” or to give wrong answers (cf. Alberti et al. 2019 ; Gordon et al. 2021 ). Furthermore, rewarding students’ efforts immediately (e.g., by credits) is a recognized way to increase motivation (Kiryakova et al. 2014 ). Hence, during app usage, students may collect credits through various actions (e.g., quizzes, attending courses, correctly solving exam-oriented exercises, etc.), determining their ranking in a playful competition with their peer group. Further, the training exercises can also be offered in the form of an interactive survey during the lecture (i.e., a “clicker” functionality) to test students’ current knowledge and support their “progression” in becoming familiar with the course content (cf. Stott and Neustaedter 2013 ).

Against this background, we propose the described functionalities to increase students’ perceived enjoyment (when dealing with subject-related content) as means to address the student factors “learning strategies” and “self-organization” (see Fig.  6 and Sect.  2.3 ).

Additionally, we suggest the design of the solution as a hybrid app (DR 6), which differentiates between a front- and backend (DR 7) and transfers data with the help of HTTP and JSON (DR 8) as a way to effectively produce the required information and, hence, contribute to system quality in our setting (cf. Urbach et al. 2009 ). The design as a hybrid app allows us to offer the app for both common mobile phone platforms (i.e., iOS and Android), whereby the development efforts were less than for corresponding native apps (e.g., Schilling 2016 ). The app’s architecture enables easy maintenance, further development, as well as the addition of further services. Finally, the data (e.g., exam-oriented exercises, quiz questions, etc.) are stored in a database, which strongly facilitates content management and the provision of new content once users log in as “lecturers”. Figure  7 provides our proposition for the architecture.

General architecture of the app

Based on our findings, we propose the following four design principles to facilitate the design of related instances of the artifact (cf. Kruse et al. 2016 ; Sein et al. 2011 ), namely mobile solutions to support students in the “transition-in” phase. Generally, design principles build on the knowledge that is gained when developing and using a specific instance of an artifact and are formulated when reflecting upon the results from a more generic perspective (Kruse et al. 2016 ). Concretely, we propose the following design principles:

Principle of fostering course attendance management : In order to support students’ self-organization, a mobile app should offer the option to systematically plan and track one’s course attendance. This would help to structure the time at university and give an overview of the time spent in courses.

Principle of using self-learning control functionalities : To control the learning progress, mechanisms to evaluate one’s subject-related knowledge must be provided. For that purpose, various propositions have been made in the literature, like quizzes, practice tasks, open-ended questions, or criterion tests, amongst others (cf. Chou and Feng 2019 ; Pauli et al. 2020 ). That way, students may begin to critically reflect upon their learning strategies and move from a surface learning approach to deep or strategic learning efforts (Lau and Lim 2015 ).

Principle of assuring a widespread availability : To guarantee the availability of the mobile solution for a wide range of students, it needs to be executable on different platforms (e.g., iOS, Android) and devices (smartphones, Tablet-PCs). Further, active promotion is required to make students aware of the availability of the solution. Moreover, mobile phones have truly become ubiquitous in the student age group.

Principle of easy content management : The content offered for the students (e.g., quizzes, training questions, videos, etc.) should be easy to manage. This calls for an architecture that decouples the front- from the backend and uses a database for content storage. By that, the provision of new materials or a revision of existing content is tremendously fostered.

These design principles have been formulated based on a concrete instance of an artifact, considering the findings of this study. They can purposefully complement existing design principles, which are directed at the creation of innovative learning environments (cf. Herrington et al. 2009 ), the design of mobile course material (cf. Ally 2005 ), or making use of gamification elements (cf. Laine and Lindberg 2020 ). However, our design principles are different from existing propositions in the field of mobile learning (e.g., Palalas and Wark 2017 ) since we focus on support during the “transition-in” phase and the corresponding challenges. As such, the suggestions may be referenced and consolidated with further design principles to cover all stages of the student lifecycle in future research, contributing to an even better understanding of mobile learning in higher education.

5.3 Benefits for research

As mobile phones are widely available among students and the field of higher education becomes aware of the potential to use them for learning purposes through the development and usage of mobile learning apps, it is crucial that researchers and practitioners develop a better understanding of what makes learning apps successful and—equally important—how to measure their success in the first place. Considering this, our study contributes to research in the following ways.

First, the mobile app to support students in the “transition-in” phase was developed with the help of a DSR procedure (cf. Peffers et al. 2007 ), as outlined in Sect.  2.3 . The app represents the artifact (i.e., outcome) of the Design Science (DS) effort and, hence, a “human-made object” (Goldkuhl and Karlsson 2020 , p. 1241) to solve a practical problem (March and Smith 1995 ). However, besides the artifact itself also its contribution to theory should be clearly highlighted in DSR (Baskerville et al. 2018 ). This contribution to scientific knowledge is addressed by Hevner’s “rigor cycle” (Hevner 2007 ) and a mandatory element from the perspective of the “design theory school of thought” (Baskerville et al. 2018 , p. 359). Thereby, the IT-artifact (i.e., the mobile app) of our DS effort was built in previous work (see Sect.  2.3 ) and can be seen as an instance of a “type 2 app” according to the “pedagogical framework of mobile learning” (Park 2011 ) for students of an accounting course. In this paper, the app’s contribution to the scientific knowledge base is analyzed by identifying the factors that impact a student’s learning effectiveness, user satisfaction, and intention to reuse the app and linking these to design requirements (see Sects.  4 and 5 ). Furthermore, we present design principles that offer DS researchers “actionable knowledge useful in building new versions of similar artifacts” (Kruse et al. 2022 , p. 1236). Accordingly, these insights may extend the “pedagogical framework of mobile learning” (Park 2011 ) by laying the foundation for success factors and design propositions that determine the acceptance of mobile apps among students in the “transition-in” phase.

Additionally, this research supports previous findings on the employment of system success models in the field of mobile learning. In this respect, we confirm the model of Wang ( 2008 ), which suggests that user satisfaction has a direct as well as indirect impact on other net benefits (e.g., learning effectiveness) through the mediation of intention to reuse. Another implication that can be drawn from this result is that the perceived user satisfaction and the intention to reuse are prerequisites for students’ learning effectiveness. Following Wang et al. ( 2019b ), who implemented perceived enjoyment in the context of fee-based mobile learning, we now use the perceived enjoyment in our proposed success model for free mobile learning apps. Therefore, this study benefits future research by providing a validated IS success model for free mobile learning apps by combining perceived enjoyment and learning effectiveness with the established IS success model.

However, deviating from the traditional IS success model, we found service quality to have no significant impact on perceived user satisfaction and intention to reuse (see Fig.  5 ). Since this can be explained, as shown in Sect.  5.1 , it can be stated that service quality is relatively less important in the context of knowledge-oriented IS success, which is in line with previous research (e.g., Wang et al. 2019b ; Wu and Wang 2006 ) as well as rather good news for higher education organizations which mainly expend their resources on teaching staff instead of administrative personnel, which could provide user support for such apps.

In summary, the empirical results emphasize the importance of extending the traditional IS success model by other dimensions like perceived enjoyment when assessing mobile learning app success. Accordingly, future research can rely on this multidimensional approach, compare it to existing models, or examine the influence of the included constructs on mobile learning system success.

5.4 Benefits for practice

This research also provides several implications and benefits for practice. First, the results show that an app, which was collaboratively developed with the target groups (i.e., students and educators) and adapted to the particular needs of first-year students, can positively influence students’ learning effectiveness. This highlights the value of the design and development procedure of the app following a DSR approach (cf. Peffers et al. 2007 ) with several iterative steps.

Second, according to the employed and validated model, learning effectiveness is considered a more effective measure of mobile learning app success than the other six variables. In this regard, learning effectiveness should develop if the model components of system quality, service quality, information quality, perceived enjoyment, intention to reuse, and perceived user satisfaction are appropriately managed. Thus, to support learning effectiveness, an implication for developers of mobile learning apps is to focus on high system quality, information quality, and, foremost, an enjoyable learning experience. To this end, design requirements and principles have been proposed that allow the creation of similar artifacts. More detailed, our results show clear evidence that the impact of students’ perceived enjoyment on user satisfaction, intention to reuse, and learning effectiveness is substantially greater than the total effect of system quality, service quality, and information quality. This calls for strongly emphasizing gamification elements for mobile apps in corresponding DSR efforts.

Third, the four components of system quality, service quality, information quality, and perceived enjoyment have both direct and indirect effects. They all directly influence students’ perceived user satisfaction and the intention to reuse. The perceived user satisfaction, in turn, affects the intention to reuse as well as the learning effectiveness. Therefore, the intention to reuse and learning effectiveness are also influenced indirectly. The findings of this study suggest perceived user satisfaction has the most significant impact on intention to reuse and learning effectiveness. Moreover, perceived user satisfaction has the most substantial direct and total effect on students' learning effectiveness (see Fig.  4 ). Thus, the importance of student’s satisfaction with the learning app in improving their learning effectiveness is emphasized. However, the findings also suggest that developers as well as educators must track changes in both the perceived user satisfaction and intention to reuse, as user satisfaction does not totally mediate the impact of intention to reuse on students’ learning effectiveness.

To summarize, this study helps practitioners like developers and educators to identify the factors that make mobile learning applications more successful. The empirical findings encourage developers to consider the constructs of system quality, information quality, perceived enjoyment, perceived user satisfaction, intention to reuse, and learning effectiveness when designing their products. Moreover, the importance of students’ enjoyment and satisfaction while using the app for their learning outcomes is emphasized. Besides the implications for developers, this aspect is also a fundamental implication for educators, as it requests and motivates them to deliver learning content entertainingly to help their students succeed. Together, both aspects could help reduce early dropout rates among students and, thus, contribute to fighting the current shortage of skilled workforce and help meet the economy’s demand for qualified workers in the next decades.

6 Limitations and further research

This study deals with the analysis of factors that contribute to the success of a mobile app to support first-year students in the “transition-in” phase in view of learning effectiveness, user satisfaction, and intention to reuse the app. Furthermore, the factors are linked to design requirements and the derived design principles. Our study covers one semester during the COVID-19 pandemic and thus includes only digital courses, which should be considered when interpreting the results. Nevertheless, if this circumstance has any impact, we expect it to be in favor of our results rather than against them. Presumably, the impact of our learning app would have been even stronger in the pre-pandemic period than in the online teaching period. This is because the app functionalities purposefully complement the attendance of face-to-face lectures. In a pure online semester, the “value” provided by the app, which is still positive for users, may be less than in the era of traditional teaching. Though, this proposition needs to be explored in more detail in future studies.

Besides providing several benefits for both research and practice, this study has some noteworthy limitations. First, the discussed findings and the implications drawn are limited to a specific context of an app adapted to first-year students’ particular needs in a mandatory introductory accounting course at the University of Bremen (Germany). However, in terms of the topics, structure, and practicalities, the course setting is similar to most foundational undergraduate courses in Continental European study programs in business and economics. Second, since we rely on self-reported data to examine the mobile learning app’s success, this may introduce the risk of common method and response bias. Having said that, as we assured participants of the confidentiality of their responses and offered no monetary rewards or other incentives for participation, we assume that the risk of systematically biased responses is minimal. Third, we employ a cross-sectional approach, which causes possible feedback links from learning effectiveness to perceived user satisfaction and the intention to reuse could not be considered in this study. Finally, our research model largely builds on the initial elements of the IS success model. This was done to receive design principles that are based on widely accepted elements for success, which may positively affect the general acceptance of such principles in the DSR community. Moreover, to the best of our knowledge, corresponding studies based on the IS success model have not been done for “type 2 apps” according to the “pedagogical framework of mobile learning” of Park ( 2011 ).

In future research, a longitudinal design to take these possible feedback links into account and, thus, enhance the understanding of the causality and interrelationships of the research elements in the context of mobile learning app success will be performed. Going forward, the app will be continuously developed further and is planned to be fully integrated into the entire undergraduate curriculum at our faculty. In this course, the integration of AI-based conversational agents to further improve students’ learning experience will be investigated more closely. Particularly their impact on students’ learning behavior is to be considered since the literature on accounting education as well as information systems lacks theoretical foundations in this respect. Moreover, the research model will be extended by additional elements in the next step to identify additional influencing factors that may positively affect student performance (e.g., base competencies or grit; cf. Aparicio et al. 2017 ; Zehetmeier et al. 2014 ).

Exam performance is coded according to the German grade scale from 1.0 (best) through 5.0 (fail).

In order to ensure the representativeness of our drawn sample, we conducted two-tailed t-tests for differences in means between the group of students included in the sample and the underlying population of app users. The results indicate that the characteristics are essentially similarly distributed. The only documented significant differences are in the share of students in Business Studies and Engineering and Management, in the share of (almost) always and (almost) never attending students, as well as in the share of students with a very good exam performance. However, the significance level is only slightly pronounced ( p  < 0.1) for most differences. We present a comparison between the sample and the underlying population with the conducted t-tests in Table 9 (Appendix).

The high proportion of never and rarely attending students is likely due to the conitions of COVID-19 induced online teaching. In order not to disadvantage any students, we provided the recordings of the zoom sessions of tutorials and workshops afterwards. However, we were not able to assess which students accessed the recordings. Moreover, the distribution of the exam performance in our sample, which documents a high level of insufficient performance and thus failure, is in line with both the exam performance of the whole population of the course and the distribution of the exam performance in previous cohorts.

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Uses and gratifications of educational apps: A study during COVID-19 pandemic

Although educational apps have emerged as an easily available and accessible alternative to classroom learning, particularly at the time of pandemics like COVID-19, no research has attempted to identify learners intentions behind the usage of different educational apps. The current study developed a valid and reliable research instrument to measure the motivations behind using educational apps. Using the mixed method approach commonly used in uses and gratification (U&G) research, i.e., open-ended essays & national survey (N  =  552), this study identified seven gratifications behind learners intention to use educational apps: academic assistance, convenience, entertainment, social influence, novelty, engagement and activity. The result suggests that academic assistance, convenience and social influence were the significant predictors of the intention to use educational apps. The current research also identified the moderating effect of gender in selecting educational apps. One of the most significant contributions of the present study is that it extended the uses and gratification theory applications beyond the traditional media to explain the intention to use educational apps.

1. Introduction

The advancements in communication technology have resulted in various applications for accessible and affordable education. As a result, students and educators have access to new technologies, gadgets, and applications to augment their pedagogical experiences [38] . Applications based on digital technologies have transformed the teaching and learning experience by opening up myriad opportunities [ 40 , 109 ]. The rapid internet connectivity, developments in phone technology and the emergence of compact and compatible smartphones and tablets have put "education" into "apps" [51] . Educational apps reduce the cognitive load on the learners by easily and effectively communicating concepts and contents with a faster flow of information beyond time and space [ 22 , 40 , 123 ].

Past studies have identified that well-designed educational apps can facilitate an interactive learning experience [ 25 , 35 , 75 ]. Researchers found that various factors motivate learners intentions to use educational apps. Scholars have observed that motives such as entertainment [ 4 , 20 , 39 , 76 ], convenience [35] , academic assistance [ 21 , 35 , 75 ], interactivity [23] and engagement [35] influence students' selection of educational apps.

The review of prior literature shows many gaps in the existing literature. First, although educational apps have emerged as an important learning alternative in most countries, very scant literature is available on the motives behind their usage. The available literature on educational apps focused more on app design [ 39 , 85 , 93 ] and content features [ 32 , 115 ] besides identifying various user motivations. For example, Falloon [39] , in his study on iPad based educational apps, identified interactive design, convenience, and entertainment gratifications that motivate students to use learning apps. However, the primary objective of his study was to explore how the app design and content influence students learning pathways. Similarly, past studies like Bomhold et al., [13] , Falloon [40] , Dubé et al.; [35] , Dias & Brito [33] etc., gave more emphasis on educational apps' contents, their design and various features, besides locating various user motivations.

Second, the limited prior literature ( [16] & [ 17 , 75 ]) investigating the user motivations of educational apps portrays an ambiguous picture of the learners' motives for using educational apps by providing conflicting results. Third, the existing literature analysed the usage of educational apps from teachers' [16] & [ 17 , 52 ]) or parents' [ 80 , 121 ] perspectives. The end-users of the educational apps are students, and literature probing into students motives for using educational apps are not available yet. Consequently, an investigation of educational apps' various uses and gratifications (U&G's) and the intentions behind their usage is highly warranted. We argue that various U&G's behind educational apps are significantly associated with learners intention to use them.

Lastly, educational apps became increasingly popular across the globe recently after the outbreak of the COVID-19 pandemic (Kondylakis et al., 2020; [95] ). The lockdowns and social distancing norms have disrupted the education sector, and physical attendances of schools and colleges were suspended for a long time, making the students and educators search for a feasible alternative [ 106 , 112 ]. Through advances in technology, accessibility and affordability, educational apps emerged as a viable alternative for classroom teaching. In addition, the COVID-19 crisis further caused a surge in the usage of educational apps across the globe [103] . During COVID-19, India witnessed an unprecedented spike in the usage of educational apps [ 30 , 74 ]. However, no study has ever attempted to identify what motivates students to use educational apps in India.

The current study addresses the gap mentioned above by examining the different uses and gratifications behind the usage of educational apps exclusively from the students perspective and thus provide a new dimension to the existing literature. Also, understanding the motives for using educational apps and gratifications sought or obtained from them help educators to design the content in accordance with the learners taste and alter the pedagogy to facilitate an interactive learning experience. Unlike the prior studies, we conducted our study in India, a developing and culturally diverse country, thus increasing its external validity. Scholars [ 70 , 101 ] argue that conducting research on a culturally diverse country can increase the study's external validity. Another unique contribution of this study is that we have used the Uses and gratification theory as our theoretical framework to understand the various motivations behind the usage of educational apps. Thus we have extended the U&G theory beyond the conventional media to locate the gratifications obtained from educational apps.

The primary focus of this study is to identify learners motives for using educational apps in India. We utilised a mixed-method approach, including qualitative and quantitative methods, to locate the user motivations. After identifying the user motivations, we developed a comprehensive research model and tested it to see which motive better predict the intention to use educational apps. Finally, we also attempted to see the moderating effect of gender in the usage of educational apps. Further, the current research also has some important theoretical and practical implications.

2. Literature review

An educational application or simply an 'educational app' is a software programme integrated with learning materials that can be downloaded and installed on mobile phones or tablets [27] . Educational apps allow students and learners to access content anywhere, anytime [ 13 , 32 , 115 ]. Smartphones and tablets with touch screen facilities have increased the popularity of educational apps among students, teachers and parents [ 53 , 85 , 93 ]. Although many studies have been conducted on educational apps, very few researchers have attempted to identify the motivations for using educational apps [86] . 'Motivations are general dispositions that influence people's actions taken to fulfil a need or a want ( [84] , p.179)'. Identifying the motivations behind using a particular media can predict the recurring usage of the media [91] . Most of the prior studies that analysed the motivations for using educational apps were conducted on developed or western countries such as Canada[ 75 , 35 ], Malta [21] , United States [51] , New Zealand [39] , Netherlands [ 16 , 17 ], and Portugal [33] .

Falloon [39] conducted a study on iPad-based educational apps to identify factors influencing students' learning pathways in New Zealand. However, their study focussed primarily on the design and content features of the apps developed for school children; they also identified that interactive design, convenience and entertainment were some of the parameters that motivated teachers to recommend apps for children. Some of the recent studies also support these findings. For example, researchers [ 4 , 20 , 76 ] recommended the usage of virtual reality and augmented reality in the design of educational apps to make them more interactive and entertaining. Papadakis et al., [85] and Dias& Brito [33] ' also located entertainment as an important motivation behind the adoption of learning apps.

Many researchers [ 21 , 22 , 35 , 75 ] stressed that academic assistance is one of the key gratifications that motivate students to adopt educational apps. For example, Camilleri & Camilleri [23] conducted a qualitative study with the help of semi-structured face to face interviews with students between 6-8 years of age in Malta. Their study results showed that although academic assistance is the primary motivation behind educational apps, students also reported that interactive and engaging educational apps had improved their academic competency. Camilleri & Camilleri [23] also recommends the gamification of educational apps as many students expressed that entertaining content also motivates them while choosing educational apps.

Dubé et al., [35] argue that well designed educational apps can facilitate an experience of multi-level engagement that can improve the competence in the subject being taught. Their study also underscored that student engagement occurs because of the novelty of the new technology, the interactivity of the apps, entertainment or gamification and convenience such as hands own aspect of the touch screens. Hirsh-Pasek et al., [51] also suggest that the popularity and acceptance of education apps largely depend on course content and their meaningful, interactive and engaging presentation.

Social influence is regarded as one of the major factors influencing the adoption of new technologies [ 8 , 46 , 50 , 118 ]. Researchers ([ 21 , 24 ]&b) have found a positive association between the usage of educational apps and social influence. Children's selection and usage of educational apps are largely decided by their parents [ 80 , 121 ]. Broekman et al., [16] conducted a study to identify factors that motivate parents while selecting their children's apps using U&G theory. The study result showed that parents expect five gratifications when they select learning apps for their children, i.e. need for entertainment, information seeking, social interaction, emotional satisfaction and passing time. Another study conducted by Broekman et al., [17] on parents of young children aged 3-7 to identify the app features that fulfil parents' need for selecting apps for their children and identified four U&Gs: clear design; tailorable, controllable, educational content; challenges and rewards; and technological innovation behind educational app selection. Their study also revealed that a child's age and gender play a key role in app selection. Similarly, Montazami [75] identified five motives behind parents' intention to download apps for their children, i.e. scaffolding, academic utility, the development team's expertise, feedback, and learning theory.

Dias & Brito [33] recently conducted a study to locate the factors that influence the selection of education apps from perceptions of students, parents and app developers. The results showed that students, parents and app developers have different perspectives on selecting apps. Students preferred education apps that afford entertainment. On the other hand, parents were inclined to apps that provide good academic assistance. Their study concluded that since children and parents have contrasting perspectives on app selection, developers struggle to please both.

The review of prior literature shows many gaps in the existing literature. First, although educational apps have emerged as an important learning alternative in most countries, very scant literature is available on the motives behind their usage. Even though educational apps are widely used in developing countries like India, it has not received much scholarly attention. However, a few recent studies [ 30 , 77 ] related to online learning at the time of the COVID- 19 indicated a sudden boom in educational apps downloads. COVID-19 pandemic has intensified the usage of educational apps, and they are slowly and steadily expanding their digital footprints even in remote areas of developing countries like India [ 74 , 77 ].

Second, the above mentioned existing literature on educational apps provides an ambiguous picture of the learners' motives for using educational apps. Although past researchers have observed entertainment, convenience, academic assistance, interactivity and engagement influence students' selection of educational apps, the main objectives behind these studies were not to locate the motivations behind students uses of educational apps. Rather these studies were focused more on app design and its content features. Two of the specific studies by Broekman et al., [16] and Broekman et al., [17] to identify the motives behind using educational apps were from the parents perspective instead of learners. Also, the results of these two studies were conflicting as they identified different sets of motivations unrelated to each other. Thus, the analysis of prior research findings demands an exclusive study on students' motivations for using educational apps from students' perspectives, particularly from developing countries that are largely affected by the COVID-19 pandemic. To address the existing research gap, we ask the following research question:

• RQ1 : What are the learner's primary motives for using the educational apps?

In technology adoption research, 'intention to use' is considered an important determinant that reflects the recurring usage of a particular technology [ 113 , 114 ]. Various intrinsic and extrinsic factors influence people's intentions to use new technology. Motives for using a particular technology or the gratification obtained is considered as one of the significant predictors of users' intention to use new technology and applications [88] . Prior studies ([ 21 , 24 , 62 , 97 ]&b) suggest that motivations behind the usage of educational apps influence learners intentions to use them. For example, Camilleri & Camilleri [ [21] &b] have found a positive association between the usage intention of educational apps and social influence.

Similarly, Shroff & Keyes [97] observed that educational apps' interactivity and engagement positively influences learners intention to use them. In the light of these findings, it is plausible to assume that students motives for using educational apps can predict their intention to use them. Hence we pose our second research question:

• RQ2: Which usage motive better predict the intention to use educational apps?

2.1. Gender difference in educational apps usage

Prior research ascertained that males intentions to use the internet and related technology-driven by leisure, entertainment and functional needs, whereas females use the internet and associated applications more for social interaction and communication [ 94 , 116 ]. Moreover, past studies indicate that a gender difference exists in the uses and gratification of smartphone usage. For example, studies [ 3 , 78 ] have ascertained that male and female students' time spent on smartphones is significantly different. Andone et al., [3] observed that females spent more time on mobile phones than males, with an average difference of about 8%. Similarly, Nayak [78] , in his study on students smartphone usage and addiction in India, found that females spent more time on smartphones than male students. As educational apps are a new entrant and most of them are designed to operate on smartphones with an active internet connection, we assume that the intentions to use educational apps are sensitive to gender. Hence to explore the influence of gender in the usage of educational apps, we asked the following research question:

• RQ3: Do the intentions to use educational apps differ depending on the gender of its users?

To address the research questions, we have used the Uses and gratification theory as our theoretical framework.

2.2. Uses and gratification theory

Uses and gratification (U&G) theory is the widely utilised theoretical framework to explain the different motives and reasons behind the usage of any given medium [ 43 , 57 ]. U&G theory assumes that the media can satisfy people's innate needs [91] . Gratifications are conceptualised as the satisfaction people receive when their innate requirements are fulfilled by the media usage that matches their expectations. In other words, gratifications are the perceived fulfilment of one's needs through media usage [83] . The most important tenets of this theory are that users are active, selective, and motivated to use a particular media [ 57 , 87 ]. Hence U&G theory provides a user-centred angle of the various socio-psychological gratifications obtained from a given medium [64] . Although this theory originated pre-digitalisation era, scholars widely used it to examine the gratifications obtained from new communication technologies like the internet [84] and social media [117] .

To address the various challenges and conceptual refinement of U&G theory posed by scholars in the light of emerging technologies, Sundar & Limperos [108] suggested that U&G scholars consider the technology themselves while assessing audiences' media usage gratifications. Sundar & Limperos [108] reviewed prior U&G studies on various media technologies since the 1940s. They pointed out the need to tap the potential gratifications emerging from new interactive media, which gave rise to the MAIN model and U&G.2.0. The MAIN model helps to devise the potential gratifications emerging from new media in the light of four classes of affordances, i.e., modality, agency, interactivity, and navigability. Based on their MAIN model Sundar and Limperos [108] suggested that usage of new media (e.g., smartphones, smartphones' apps) paved the way for new sets of needs, called "medium-specific needs". Therefore, while examining the uses and gratifications from new media technologies besides considering "general needs", researchers should also emphasise emerging "medium-specific needs". Thus, the U&G theory is an axiomatic and robust theory that can examine the gratifications from traditional and new media.

Furthermore, scholars have used U&G theory to study the gratifications behind using new technologies such as mobile phone usage [64] , internet use [ 31 , 84 ], social media [117] and various smartphone applications: E.g. Facebook [ 5 , 100 ], Instagram [ 2 , 96 ], Tinder [105] , TikTok [73] etc. U&G theory was also used to study educational apps in two different contexts. i.e. parents motives for choosing apps for their children ( [17] & 2019) and learners motives for selecting apps for themselves [75] . Therefore, we utilised the U&G theory as our theoretical framework for exploring the intention to use educational apps.

3. Methodology

3.1. scale development.

Because of the availability of scanty literature on the topic under study, we have used a mixed-method [71] approach to develop the scale. The mixed-method uses a qualitative approach and a cross-sectional survey [ 88 , 111 ]. Initially, an open-ended essay writing (Dhir et al., 2017; [111] ) with 58 educational app users was conducted. Open-ended essays are the easiest and most parsimonious method to gather in-depth qualitative data [111] and are widely used by the child and adolescent researchers working on human-computer interaction [ 14 , 56 ]. In qualitative essays, predefined questions or themes were given to the respondents to instigate them and build up and share their ideas and experience.

The samples were selected randomly from the pool colleges in Southern India obtained from their affiliated universities' websites. Twenty colleges were selected initially, and selected colleges were contacted by email and telephone and informed of the study objectives, research procedure and expected benefits from the research. Four colleges were agreed to participate in the study. All the colleges that agreed to participate were private colleges, and the medium of instruction was English. The author, along with the help of teachers, distributed the open-ended survey questionnaire to students who agreed to participate. Students completed the essays between January 2020 to February 2020. Participation in the survey was voluntary, and students were free to withdraw from the survey anytime. The survey was confidential, and no personal information was collected.

The qualitative essays focussed on various issues related to the usage of educational apps. However, in the current study, the focus is only on the uses and gratifications of educational apps. The grounded theory approach [ 9 , 49 , 61 ] with affinity diagramming was utilised to analyse the data collected through the open-ended essays to locate and classify the themes based on their commonalities.

In affinity diagramming, researchers go through essays thoroughly to analyse and record each participant's response. The data analysis was concluded with the development of different themes representing various gratifications obtained from educational apps. The themes obtained were classified and categorised through the uses and gratification theory lens. The qualitative data analysis identified seven themes, i.e. academic assistance, social influence, convenience, entertainment, engagement, novelty and activity. Based on the suggestions of prior literature [ 92 , 111 ], the pool of items that emerged from the qualitative analysis is placed for a review before a group of experts, including professionals in app development and academicians. This expert review was to know whether changes are required in the questionnaire's wording and ensure that the survey instrument is error-free. The questionnaire is also pilot tested among a few students before final data collection. The final questionnaire after the pilot testing depicting seven gratifications was used for final data collection. A five-point Likert scale anchoring between 1(strongly disagree) to 2 (strongly agree) was used to measure the items.

3.2. Survey participants and procedure

The population identified for the study were high school and college students up to post-graduation in the age group ranging from 15-25 years from India. Data collection was done between March 2020 to February 2021. Data collection utilised an internet-based national survey using a snowball sampling method. The targeted respondents were accessed through multiple methods, e.g., hosting the survey links on various social media platforms (like- WhatsApp, Facebook, Instagram, Telegram), asking students who already completed the survey to share among their friends' networks, and requested teachers to post the survey link on online teaching platforms and ask their students to fill the questionnaire.

The resulting sample (N = 552) consisted of 53.3 % female and 46.7 % male students with an average of 18 years. The minimum age of the respondents was 15, and the maximum age was 24. Most of the participants were higher secondary students, followed by graduate students. The average time spent on educational apps in a single sitting is about 47 minutes. The majority of the students (62.6%) prefer to use mobile phones for accessing educational apps (See Table 1 )

Sample characteristics.

N = 552.

3.3. Research model

The researchers used U&G as the theoretical lens and proposed a model consisting of seven different U&Gs as the predictor variables. Prior scholarship [87] suggests that identifying U&Gs is important because these gratifications can influence actual technology use. The intention to use (adapted from [88] ) is the only criterion variable (see Fig. 1 ). Past literature utilised U&G theory to delineate the influence of various U&Gs on usage intentions [ 42 , 67 , 68 ]. Hence, we assume that the U&G theory can provide an axiomatic and closely fitting theoretical framework for identifying the relationship between the U&Gs of educational apps and their usage intentions.

The proposed research model.

Past researchers [ 88 , 108 ] have classified the gratifications of media usage into four main categories: content, process, social and technology. Guided by this, the seven U&Gs emerged from our qualitative data analysis is classified into four dimensions: process (i.e.convenience), social (i.e. social influence), content (i.e. academic assistance, entertainment) and technology (novelty, activity and engagement). The different research hypotheses were developed in the light of this classification and presented below.

3.4. Hypotheses

Academic assistance in this study refers to the academic help extended by the educational apps to learners in the form of audio or video lectures and e-course materials. Educational apps available in the market are designed to help students learn their courses easily [51] . Besides providing extensive information related to the course of study, these apps also help students complete their regular classroom assignments, prepare them for examinations by conducting mock tests, and give extra information about their course beyond their proposed syllabus. The prior literature studied academic assistance provided by the educational apps from different contexts ([ 21 , 22 ]&b; [ 35 , 75 ]). Furthermore, scholars [ 53 , 66 ] have also found a positive relationship between academic assistance and the intention to use educational apps. Therefore we hypothesise that:

• H1. Academic assistance gratification is positively associated with the intention to use educational apps.

Entertainment in the present study refers to designing educational content interestingly to catch the learners' attention. Most educational apps make their content interesting by using entertaining language or with the help of eye-catching pictorial representations or with the help of good quality graphics and animation. Furthermore, such apps are integrated with features that make students play and learn [122] . This kind of gamification approach of education increases learners motivation and engagement by incorporating the game design environment with the educational environment [34] . In addition, some apps use virtual reality (VR) or augmented reality (AR) techniques to make their content more interactive and entertaining [ 4 , 20 , 76 , 82 ]. Prior research [ 33 , 39 , 85 ] shows that entertainment is an important aspect of adopting learning apps. Therefore, we propose:

• H2. Entertainment gratification is positively associated with the intention to use educational apps.

Convenience in this study refers to the perceived ease of use of educational apps. Educational apps allow users to install it on their mobile phones or tablets and enable them to access it anywhere anytime [ 13 , 44 , 115 ]. Furthermore, some of the educational apps are stand-alone. It comes preloaded in a tablet which often does not require an internet connection making them more convenient and easily accessible [ 10 ]. Besides these, most educational apps allow users to navigate and filter content and make them read, listen or watch the specific content they require [58] . Also, users can bookmark content and resume or play from the point where they have stopped previous lectures or sessions. In the case of video lectures, students can play, rewind and watch the lecture as much as they want. Also, the convenience of educational apps enables students to learn from their homes even in difficult times of pandemics like Covid-19 [ 6 , 77 , 106 ]. Hence the current study proposes:

• H3. Convenience gratification is positively associated with the intention to use educational apps.

Previous research [88] has identified that peers, family, friends, teachers and various media can influence product purchase and Ist usage intentions. In the context of the study undertaken here, social influence can be identified as the advisements on educational apps from many sources such as friends, peers and mass media. Prior studies have identified social influence as one of the major determinants in adopting new technologies such as mobile applications [ 8 , 46 , 50 , 118 ]. Furthermore, scholars [ 23 , 24 ] have found a positive relationship between the usage of educational apps and social influence. Therefore in the current research, we hypothesise that:

• H4. Social Influence gratification is positively associated with the intention to use educational apps.

Novelty in this study refers to the technological affordances of the educational apps, like their newness and unusual user experience [108] . Novelty is a medium-specific gratification [65] that emerged due to the advancement of user interactions with newer gadgets. Sundar & Limperos [108] classified novelty under modality based gratification and suggest that newer media has given rise to new features like mobile apps. As far as educational apps are concerned, they offer interactive content to engage and comprehend learners easily. In their MAIN model, Sundar & Limperos [108] argue that new media's technological affordances can instigate cognitive heuristics in users. Past studies [ 19 , 55 , 59 ] have found that novelty gratification positively influences the intention to use mobile apps. Hence in this study, we propose that:

• H5. Novelty gratification is positively associated with the intention to use educational apps.

Activity refers to the technological affordance that facilitates real-time interaction with the content and features of the app. Sundar & Limperos [108] argue that interactivity affordances triggers a heuristic and allow users to interact with and through the medium (pp.515). The interactivity affordance makes the digital applications meaningful [ 102 , 107 ]. All the educational apps have an interactive interface that allows the learners to interact with them and keeps them engaged [11] . Also, few studies on mobile apps [ 79 , 119 ] suggest that interactivity positively predict the intention to use mobile apps. Therefore, we assume that interactivity is likely to positively affect the educational apps' usage intention. Hence we state our next hypothesis :

• H6. Activity gratification is positively associated with the intention to use educational apps.

In the current study, engagement refers to the users' degree of involvement with the learning process. Educational apps have many features that help learners stay on the medium and reduce the impediments that distract them. According to Hirsh-Pasek et al., [51] , the quality of the educational apps depends upon their ability to support students engagement with the learning process. Dubé et al., [35] suggested that a well-designed education app creates an environment for the students to experience multi-level engagement, leading to increased interest in learning. Prior studies [ 60 , 62 , 97 ] suggest that educational apps' engagement positively influences their intention to use. Hence we argue that:

• H7. Engagement gratification is positively associated with the intention to use educational apps.

Prior studies suggest that a gender difference exists in the uses and gratification of various media. Andone et al., [3] and Nayak [78] have ascertained that male students' time spent on smartphones and female students is significantly different. They found that female students spent more time on mobile phones than male students. In another study, Zhou & Xu [120] observed that females are lesser competent in adopting new education technologies. Albelali & Alaulamie [1] conducted a study on mobile learning apps among Saudi Arabian students and found that male students had more inclination towards using M-learning apps than females. In the light of prior research, we argue that gender moderates the usage of educational apps. Thus we hypothesise:

• H8 . There is a significant difference in the intention to use educational apps across male and female students.

3.5. Data analysis

The data gathered through essays were analysed with the help of the grounded theory approach [ 15 , 26 , 45 ] using NVivo 12. The survey data were analysed with SPSS 23.0 and AMOS. The research model was tested using the structural equation modelling (SEM) procedure [47] . As part of the procedure, a confirmatory factor analysis (CFA) was conducted to establish the proposed research model's goodness of fit and confirm its reliability and validity. After the model was statistically confirmed, then research hypotheses were tested.

4.1. Measurement model

We performed CFA using the robust Maximum Likelihood algorithm [89] . The proposed measurement model was examined using popular goodness of fit indices. The CFA confirmed that the measurement model possess a good model fit with χ 2 / df  = 3.23 , Comparative fit index ( CFI ) = 0.95, Tucker-Lewis Index ( TLI ) = 0.93, and Root mean square error approximation ( RMSEA ) = 0.06 [18] . The final solution of constructs and indicators are depicted in Table 2 .

Factor loadings of measurement and structural model.

4.2. Reliability and validity

The CFA checked the reliability and validity of the measures. Convergent validity is checked by looking into the average variance extracted (AVE) for each study of the measures [47] . (Refer Table 3 ). From the table, it can be seen that all the study measures have good convergent validity and discriminant validity [ 41 , 47 ]. Besides these, the construct reliability scores (CRS) of the study measures were higher than the defined limit, i.e. 0.75 [ 28 , 29 , 81 ], confirming its construct reliability (see Table 3 ).

Mean, S.D, discriminant and convergent validity. EG = Engagement, SI = Social Influence, CN = Convenience, AC = Activity, EN = Entertainment, NV = Novelty, AA = Academic Assistance, IU = Intention to Use, S. D = Standard Deviation, AVE = Average Variance Extracted, MSV = Maximum Shared Variance.

4.3. Structural model testing

The proposed structural model returned a good fit with model fit with χ 2 / df  = 3.23 , Comparative fit index ( CFI ) = 0.95, Tucker-Lewis Index ( TLI ) = 0.93, and Root mean square error approximation ( RMSEA ) = 0.06 [18] . Also, the model explained high percentages of variances [48] , i.e., 49 % of the variance in usage intentions (see Fig. 2 ). The hypotheses H1, H3, and H4 were supported (see Table 4 ) because academic assistance (p < 0.01), convenience (p < 0.05), and social influence (p < 0.001) U&Gs were found to be significant positive predictors of education app usage intentions.

Results of the structural model.

Results of hypothesis (# H) testing.

n.s  = not significant.

The current study's findings are supported by past research [ 22 , 23 , 35 , 75 ] that identified academic assistance as a significant predictor of students usage of educational apps. Scholars [ 23 , 24 ] have found a positive association between the usage of educational apps and social influence. Our study corresponds to this finding by identifying social influence motive as a significant positive predictor of usage intention. Lastly, supporting prior studies [ 6 , 58 , 77 , 106 ], in the current study, convenience gratification obtained from educational apps positively predicted intention to use them.

4.4. Moderation analysis

The final hypothesis in the present research was to check the moderating effect of gender (H8). It has been assumed that the intention to use educational apps differed among male and female students significantly. In the current study, a two-group model is used to find whether gender moderates the intention to use educational apps. The result (see Table 5 ) shows that the intention to use educational apps is significantly varied among the male and female users showing a moderating effect. It is observed that academic assistance and social influence gratifications influence male students' intention to use educational apps, whereas convenience and social influence gratifications influence the female students' intention to use educations apps. This finding corroborates the findings of Zhou & Xu [120] and Albelali & Alaulamie [1] .

Gender as a moderator.

⁎⁎⁎ p < 0.001, **p < .0.01, *p < 0.05

5. Discussion

Recent studies [ 6 , 7 , 106 , 112 ] have shown that COVID -19 pandemic has disrupted the traditional classroom education system, and students were forced to adapt themselves to the online class and learn through apps. Many developing countries like India have been affected by the COVID-19 pandemic. Schools and colleges were closed for a long time to protect the students from viral infections, and alternative mechanisms such as online learning and learning through apps were put in place to cope with [77] . Educational apps play a vital role among the different measures and methods to cater to quality education during COVID-19. Due to their portability, interactivity and entertaining content, educational apps successfully struck a chord among students, parents and teachers in India (India Today, 2020 [ 54 ]). In the backdrop of this extreme situation, the first research question of the current study was intended to investigate the uses and gratifications behind the use of educational apps. The present study is the first empirical research that looks into the different U&Gs for using educational apps.

Furthermore, the study examines which gratification motive better predict the intention to use educational apps. This study used a mixed-method approach that involved open-ended essays with 58 educational apps users and an internet-based cross-sectional survey with 553 education app users in India during the COVID-19 pandemic. The current research utilised the Uses and Gratification theory as its theoretical framework to locate learners intentions and motivations for using educational apps. This research offers potential theoretical and practical implications for academicians, researchers, educational app developers and app users.

The first research question was stated to identify learners' motivations behind using educational apps. The current study identified seven motivations for using educational apps: academic assistance, convenience, entertainment, social influence, novelty, activity, and engagement. The finding is consistent with the past scholarships [ 16 , 17 , 23 , 33 , 36 , 51 ], which reported that academic utility, convenience, user interactivity, and entertaining content were the motivations behind the adoption of educational apps. Besides this, our study also confirms that parents and students have slightly different motives for choosing educational apps. For example, Broekman et al., [16] identified five gratifications for parents selecting education apps for their children: need for entertainment, information seeking, social interaction, emotional satisfaction and pass time. But, except for entertainment, no other gratifications emerged in our study. Hence our findings support the argument of Dias & Brito [32] that students and parents have contrasting perspectives on app selection.

The first hypothesis of this study examined the relationship between academic assistance and the intention to use educational apps. The current research findings suggest a positive association between academic assistance and the intention to use educational apps. The result indicates that during the COVID-19 pandemic, many students depend on educational apps for learning. This finding corroborates recent literature [ 22 , 23 , 33 , 35 , 66 , 75 ] that suggested the primary intention behind the education applications is academic assistance by bridging the gap between classroom learning and home learning [98] . In the light of this finding, we recommend students, parents, and educators increase the usage of educational apps in academics.

The second hypothesis investigated the association between entertainment gratification and intention to use educational apps. However, the findings of this study were inconsistent with past literature ([ 16 , 18 ]) by identifying no significant relationship between entertainment and the intention to use educational apps. The possible reason for disconnect can be due to the participants under study. Broekman et al., [ [16] , 18] studied parents of primary school children, and our study focussed on high school and college students. Due to their high maturity level, they may be looking for more subject-specific content than entertaining content. Furthermore, Dias & Brito [32] found that young children and parents vary in their criteria for selecting educational apps. Children preferred apps that afford fun and entertainment, whereas parents preferred the academic utility of the apps.

The third hypothesis tested the relationship between convenience and intention to use educational apps. The study result supports this hypothesis which is in line with the findings of the past studies (e.g., [ 16 , 51 ]). The perceived ease of use and accessibility of educational apps make it a convenient learning tool. Also, educational apps offer 'tailorable' and 'controllable' education content [17] that can comprehend easily. Thus, when educational institutions closed at COVID-19, these educational apps slowly and steadily created their niche in the academic arena due to their perceived ease of use and technological advances.

The fourth hypothesis examined the relationship between social influence and the usage of educational apps. The result indicated a positive association between social influence and the intention to use educational apps, which supports the findings of prior literature [ 16 , 33 , 75 , 80 ]. Social pressure often triggers adopting new technology and innovations [99] . Apart from teachers, parents and peers, mass media also significantly influence the intention to use educational apps. Some education app companies are doing extensive media campaigning in India with film stars and celebrities to endorse their learning apps ( [37] , June 11).

Hypothesis H5, H6 and H7 examined the relationship between technological gratifications, i.e. novelty, activity and engagement and the intention to use educational apps. The result indicated an insignificant relationship. In U&G 2.0, Sundar & Limperos [108] suggest that technological affordances such as smartphones and tablets have created new gratifications that have paved the way for novel, interactive and engaging media experiences. However, this study result indicates that novelty, interactivity and engagement are not positive predictors of adopting educational apps. This could probably be because users find it difficult to adapt to this new learning method [30] . In addition, the COVID-19 outbreak forced many students who are not regular educational apps users to migrate to app-based education [63] . Also, the small screen size of the tablets and mobile phones could be another potential reason for the insignificance of technological gratifications. Larger screens have offered more attention and more content absorption than small screens like smartphones and tablets [ 69 , 72 ].

Finally, the current study revealed that gender moderates the relationship between U&Gs and the intention to use educational apps. The results showed that male students intention to use educational apps was more influenced by academic assistance and social influence gratifications. One of the main reasons behind these findings is the gender difference in the usage patterns of mobile phones and tablets. In Indian society, male students get more privileges and access to smartphones much earlier than girls [78] .

6. Contributions, limitations and concluding remarks

6.1. theoretical contributions.

The current research findings have many theoretical contributions. First, the study extended the Uses and gratification theory beyond the conventional media to capture the motivations for using educational apps. The U&G is the most popular and widely used theory to study media usage behaviour and antecedents. However, we have given a new perspective to this theory by utilising it to test the educational app usage intention. We have also statistically tested and validated a model using new measures of education app usage. The developed gratification measures can help the academic community conduct further in-depth research on educational apps.

Second, the study identified three technological gratifications for using educational apps: novelty, activity, and engagement. Thus, this study has validated Sundar & Limperos [108] argument that new technologies have given rise to newer affordances and, in turn, has created new gratifications. However, the study result showed that the new gratifications were not significant predictors of the intention to use educational apps.

Third, we have used the mixed-method approach and proved a sophisticated research method to tap the U&Gs of new and emerging media [110] . Further, this research reaffirms the potential of the mixed-method approach and grounded theory [ 26 , 45 ] in analysing new technologies. The mixed-method approach is the easiest and most parsimonious research method to study new media behaviours of vastly diverse populations.

Fourth, this study identified the moderating effect of gender in the usage intention of educational apps. Thus the current study corroborates past U&Gs research [ 1 , 120 ] that females are lesser competent in adopting new education technologies. Albelali & Alaulamie [1] on internet-related technologies have identified the moderating role of gender. Also, this research upheld the popular argument [78] that in Indian society, boys get more privilege than girls in terms of technological affordances and accessibility.

Lastly, the study is conducted in a developing country, i.e. India, where limited research was conducted using U&G theoretical framework. Ruggeiro (2000) argued that outside the United States, particularly in non-western countries, the U&G theory has limited acceptability. Nevertheless, our study negates this argument by extending U&G theory to study a new media, empirically testing and validating a model using new measures in a developing country outside the United States. Also, India is undergoing a massive transformation in digitalisation initiatives [110] , and the sudden outbreak of the COVID-19 has created an increased demand for online education and educational apps. Hence the educational apps industry is expected to grow fast in the coming years. We hope that the current research results will contribute to the growing body of education app-related research and set the stage for further development in the U&G theory.

6.2. Practical implications

The current study has many practical implications as well. Firstly this study identified one of the key motivations behind using educational apps as academic assistance. Hence, we recommend that teachers and parents encourage students to use educational apps as the world is struggling under the clutches of the COVID-19 pandemic, and the education system is disrupted. Educational apps are an ideal alternative learning system that can compensate for the traditional classroom learning system at the time of the pandemic, particularly in developing countries like India.

Secondly, we found that convenience is one of the U&G that predicted the students' intention to use educational apps. Hence, we recommend that the education app designers and content creators develop convenient and easier solutions for students to comprehend easily. Also, since app-based education is a more feasible alternative to mitigate the impasse created by COVID-19, complex disciplines like science and engineering can be taught using more interactive education apps. Students can read/watch/listen to the lectures and course materials anywhere anytime. If feedback and doubt clearing mechanisms are embedded in the educational apps, that can make distance learning more convenient.

Lastly, social influence gratification has emerged as the most significant predictor of the intention to use educational apps. That means the social pressure can create an ideal environment for the adoption of educational apps among students. Hence, the parents, teachers, and peers can influence the students to adopt and migrate to app-based learning. In India, to cope with the COVID-19 pandemic govt of India came with various free educational apps and portals to help the students learn from home. However, many students are unaware, and many have inhibition towards this new learning technology. Hence, based on our study, we suggest that teachers, parents, and peers can influence laggards [90] to use educational apps effectively.

6.3. Limitations and future research

Despite the number of contributions of this research, limitations also exist. First, although the current study has identified a comprehensive number of educational apps usage intentions, it may not be exhaustive. We recommend that future researchers expand the current study to tap more nuanced gratifications of educational apps. Second, data collection utilised a snowball sampling method hence. Although this can be justified against the backdrop of COVID-19, the sample has the inherent limitations of non-random sampling. Thus, based on our findings, we do not claim that generalisations can be made about the whole population. Third, this study is mainly based on education app users in India. Hence, caution must be taken while extending the findings to different cultures in different countries. We expect future researchers to conduct a similar study with a random sampling method in other cultures. Fourth, the current research only conducted a comparative analysis and investigated the relationship of a few antecedents of the intention to use. Hence future researchers can utilise a longitudinal approach to analyse the other constructs that influence the intention to use educational apps. Lastly, the present study examined the moderation effect of only one variable, i.e. gender. Many other demographical, technological, and social factors can moderate the intention to use educational apps. Hence, we recommend that future scholars consider a study from those angles.

Declaration of Competing Interest

The author declares that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

• Research article
• Open access
• Published: 04 August 2017

Using a gamified mobile app to increase student engagement, retention and academic achievement

• Ekaterina Pechenkina   ORCID: orcid.org/0000-0001-6997-6974 1 ,
• Daniel Laurence 2 ,
• Grainne Oates 3 ,
• Daniel Eldridge 4 &
• Dan Hunter 5  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  31 ( 2017 ) Cite this article

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This study investigated whether the use of a gamified mobile learning app influenced students’ academic performance and boosted their engagement in the subject. Created to better engage students in lecture content, the app was used to deliver multiple-choice content-based quizzes directly to students’ personal mobile devices post-lecture and pre-tutorial. After measuring the relationships between students’ app usage and their engagement, retention and academic achievement in the subject, it is suggested that following the app’s introduction, student retention rates and academic performance increased, and there was a positive correlation between students’ scoring highly on the app and achieving higher academic grades. While the app’s affordances for learning are promising, the causal relationship between the app usage and improved student outcomes requires further investigation. Conclusions made in the context of the wider scholarship of mobile app enhanced learning and applied game principles in HE.

Introduction

Mobile applications (apps) used as learning and teaching tools are not uncommon in Higher Education (HE) (Pechenkina, 2017 ). However, what makes an educational mobile app effective is a subject of ongoing interest to academics, lecturers, learning designers and other stakeholders invested in education (Hirsh-Pasek et al., 2015 ). Recent studies report on various HE mobile app offerings which despite the differences in their design and intended uses are united by a common goal of enhancing learning to improve student outcomes. For instance, HE mobile apps can be used to facilitate learners’ knowledge acquisition and transfer (Hannon, 2017 ), simulate experiential situations to train medical students (Amer, Mur, Amer, & Ilyas, 2017 ), assess in-class activities to provide immediate feedback (Deb, Fuad, & Kanan, 2017 ), and engage students in situated learning using augmented reality affordances (Bower, Howe, McCredie, Robinson, & Grover, 2014 ). Apps with gamified elements integrated into their design, in addition to facilitating learning, can further engage and motivate students (Hamari, Koivisto, & Sarsa, 2014 ) – where engagement is of particular importance because it may factor into student retention.

Many gaps still remain in the HE mobile app research, with further studies needed to better inform impactful app designs. Contributing to this goal, this article presents the findings of a study which investigated whether the use of a gamified mobile learning app positively affected students’ academic performance and engagement in the subject. The app in question was created specifically to better engage students in lecture content by delivering multiple-choice quizzes directly to students’ personal mobile devices post-lecture and pre-tutorial. The app’s customisation capability meant it could be tailored to suit any discipline-specific content, curriculum or a mode of teaching. Digital leaderboards and badges were embedded into the app’s design to keep students motivated to use the app, while push notifications alerted students each time a new quiz became available, allowing them to do the quiz ‘on the go’ regardless of their physical location. Grounded in the pedagogy of spaced education (Kelley & Whatson, 2013 ), the app’s primary purpose was to systemically test students on what they learnt in the low-stakes environment, hence consolidating their knowledge of the content. After its initial trial with a first-year accounting unit, the app was then also introduced into several first-year science units. Considering small student numbers in each unit using the app, all cohorts were combined into one sample to ensure meaningful statistical results. Findings are contextualised in the wider scholarship of mobile app enhanced learning and applied game principles in HE.

Mobile app technologies in higher education: An overview

Compared to the fast-growing body of scholarship concerned with educational mobile apps for children (Hirsh-Pasek et al., 2015 ; Hswen, Murti, Vormawor, Bhattacharjee, & Naslund, 2013 ; Judge, Floyd, & Jeffs, 2015 ), more evidence-based research into the impact of HE mobile learning apps is urgently needed. Where a mobile learning app’s effectiveness is concerned, some factors remain constant regardless of a student’s age: such as, learning is most effective when learners are engaged, cognitively active and guided by a goal, and when learning activities are scaffolded and interactive (Hirsh-Pasek et al., 2015 ). For older learners, however, a set of additional factors may influence their learning patterns and behaviours, such as their motivations, expectations and prior experiences (Salmon, Pechenkina, Chase, & Ross, 2016 ). All these factors need to be taken into account when designing mobile apps to enhance HE learner experience.

A number of mechanisms were found effective in the task of keeping learners engaged. Question-prompting and automated immediate feedback combined with explanatory strategies (Byun, Lee, & Cerreto, 2014 ; Sung, Chang, & Liu, 2016 ) were among them. The ‘push notification’ technology was also deemed useful when tasked with encouraging immediate learning and helping learners stay up-to-date with content (Garbrick & Clariana, 2015 ; Kudo et al., 2015 ). A mobile learning intervention’s length is another significant factor attributed to the intervention’s effectiveness, with longer interventions found more effective as they allowed to fully integrate students into the learning process, with the desired effects achieved over time (Sung et al., 2016 ). While learners’ age did not appear to factor into a mobile app intervention’s success, groups of learners homogenous in age showed higher rates of impact compared to the mixed-age cohorts (Sung et al., 2016 ). At the same time, mobile learning apps with gamified elements did not appear to achieve significant positive effects on learning (Sung et al., 2016 ). However, other studies found that when mindfully integrated into an intervention’s design, the game elements worked in accord with other aspects of the intervention to positively impact on student learning (Laine, Nygren, Dirin, & Suk, 2016 ; Woo, 2014 ).

Several educational psychology principles attributed to effective learning must also be considered when designing mobile learning app interventions. Based on Ebbinghaus’s ( 2013 ) theorising of ‘the forgetting curve’ as a way to explain the finer workings of human cognition and memory, the spacing effect and the testing effect emerged as two pedagogical principles significant when designing mobile apps for learning. With some positive results recoded (Kerfoot, Turchin, Breydo, Gagnon, & Conlin, 2014 ; Shenoi et al., 2016 ), when operationalised within an app’s design, these principles could allow students to revisit and consolidate what was learnt by being systematically tested on their knowledge.

The spacing and testing effects and the pedagogy of spaced education (Kelley & Whatson, 2013 ) behind them are well aligned with a knowledge organisation principle known as ‘chunking’. In the HE context, ‘chunking’ is best understood as a cognitive strategy used to enhance mental performance, where a bulk of information is (re)organised into smaller segments (‘chunks’) for improved comprehension (Afflerbach, Pearson, & Paris, 2008 ; Cowan, 2014 ). Enabled by smartphones and similar devices, mobile app technology is well suited to facilitate student engagement with such ‘chunk-sized’ knowledge, leading to better comprehension of lecture material (Lah, Saat, & Hassan, 2014 ).

Considering student demand for personalised learning options is growing (Shah, Sid Nair, & Bennett, 2013 ; Wanner & Palmer, 2015 ) while personal mobile devices become ubiquitous (Mackay, 2014 ), it is timely to take advantage of mobile app technologies to create new ways for students to personalise their educational experiences. These considerations are particularly salient where the experiences of first-year HE students are concerned as these students are at higher risk of dropping-out compared to their senior peers (O'Keeffe, 2013 ; Ryan, 2004 ).

Applying game principles in mobile app design

Gamification in education can be broadly understood as the use of game elements in non-game contexts (Deterding, 2011 ; Domínguez et al., 2013 ), purposed with increasing student engagement and motivation. With various studies reporting significant correlations between introducing the gamified elements into the learning process and increased student motivations (Domínguez et al., 2013 ), some gamification scholars (Hamari et al., 2014 ; Koivisto & Hamari, 2014 ) point out that success of any gamification initiative is greatly dependent on the context of its implementation and the attitudes of its intended users. Other recent research found that students generally hold positive perceptions of gamified learning and appreciate social interaction, engagement and immediate feedback it affords (Cheong, Filippou, & Cheong, 2014 ). Taking all these success factors into account, when strategically embedded into an online learning initiative, game elements have a potential to improve student outcomes (Hirsh-Pasek et al., 2015 ; Jere-Folotiya et al., 2014 ; Ke, 2015 ; Laine et al., 2016 ; Werbach & Hunter, 2015 ).

Learner motivation remains a primary concern of game-based mobile learning initiatives, with developers and educators alike trialling different approaches of introducing game elements into the learning process. These include ‘progression trees’, score-generated leaderboards, and digital badges (Abramovich, Schunn, & Higashi, 2013 ; Ahn, Pellicone, & Butler, 2014 ; Lokuge Dona, Gregory, & Pechenkina, 2016 ). Further, considering that low-stakes assessments offered early on in the teaching period build confidence and engagement, and, in turn, have a beneficial effect on retention (Meer & Chapman, 2014 ), delivered in a gamified format such assessments can make a mobile learning app more effective in its task (Weitze & Söbke, 2016 ).

Drawing on various examples of impactful mobile learning gamification initiatives, the app at the centre of this study was fitted with such features as push notifications, leaderboards and digital badges, while the pedagogy of spaced education operationalised through multiple-choice quizzes strategically scheduled post-lectures and pre-tutorials was used to boost student engagement.

The app’s development process

After noticing her first-year accounting students growing disengaged whenever a difficult concept was introduced, the unit’s head lecturer turned to mobile technology to improve the situation. Student disengagement is not uncommon in the discipline of accounting (Byrne, Flood, & Griffin, 2014 ), but the unit in question also ‘suffered’ from being a mandatory breadth (or foundation) unit as it tended to attract students who did not specialise in accounting and who were generally direct-school leavers entering university for the first time. As a result, the unit routinely showed low test performance and high attrition. The lecturer experimented with using such online tools as Kahoot! to boost in-class engagement, but the issue of keeping students engaged in learning after the lecture was over remained an ongoing concern. Because of the proliferation of personal mobile devices among the students, it was decided that a mobile app was the most efficient way to reach out to them and keep them interested in the unit.

Before embarking on an app development process, a number of existing commercial HE learning apps were analysed, but deemed as either too costly and/or failing to combine all of the features and tools identified as desirable during the project’s scoping phase. For instance, it was deemed crucial that the lecturer would have full control over the app’s weekly content and curate quiz questions and their timed release. This would make the app fully customisable and allow its use across various disciplines and classrooms. Therefore, after securing an internal seed grant from the university’s Teaching and Learning Unit, the app was developed ‘in-house’ for both Android and Apple devices. All students in the accounting unit were invited to download the app. Those who elected to use the app would receive regular post-lecture and pre-tutorial push notifications delivered directly to their mobile devices, inviting them to test their knowledge of the concepts introduced in-class by taking a multiple-choice quiz. Based on the students’ app engagement, various data was collected through the app’s analytics centre, including the speed of student responses to quiz prompts and the number of attempts it took a student to get the answer right. Based on these statistics, a leader scoreboard was populated and a winner announced each week. The app’s usage statistics were also ‘fed’ into the university’s learning management system where students could view their current leaderboard placement and digital badges awarded based on quiz performance. Students could also opt out from the leaderboard component and use the app solely for the purpose of revision.

After its initial trial with the accounting unit, the app was also introduced into several first-year science units. While the app’s structure and features remained the same, the quiz questions were tailored to suit the content of each participating unit. Considering small student numbers in each unit using the app, all cohorts were combined into one sample to ensure meaningful statistical results. After the app’s Semester 2, 2015 implementation, its impact on student learning was evaluated. This research was driven by the following questions:

Did the app’s introduction correlate with improved student retention rates?

Did the app’s student usage correlate with improved academic performance?

Did students’ app performance correlate with their academic performance in the unit?

Due to time constraints and limited resources necessary to conduct an in-depth qualitative or mixed methods study, it was decided that as a first step, there would be a quantitative statistically-driven study into the app. The decision was reinforced by the immediate availability of analytical data generated by the students’ app use as well as the relative ease of accessing aggregate student data on retention and academic performance. Finally, the quantitative research undertaken is envisaged as a foundation for the study’s next phases, which would include qualitative investigations.

Student participants in this mobile learning app trial were recruited from the wider cohort of students enrolled in the first-year accounting and sciences units in Semester 2, 2015. Of 462 students enrolled in the accounting unit, 265 (57%) opted to use the app; and out of 249 science students, 129 (52%) opted in to use the app. Average grades and retention rates for the pre-app cohorts (enrolled in Semesters 1, 2015 and in both Semesters in 2014 and 2013) were obtained via institutional learning management system. After initially conducting a comparative analysis between the accounting and science cohorts, it was decided to combine all cohorts into one sample to achieve a higher total number and ensure statistically meaningful outcomes. As a result, of the combined sample of 711 students, 394 (55%) signed up to use the app and consented to have their engagement data analytics collected for research purposes.

The study has received clearance from the university’s ethics committee. In regards to student consent mechanism, in the first instance of accessing the app, each new student user was asked to create an account and then proceed directly to the app under the condition that their data would be collected in a de-identified aggregate manner for the purposes of the app improvement and research. Students, however, had a chance to click the ‘opt out’ button if they did not wish to have their analytics data used for research purposes. If the latter option was chosen, student usage analytics would still be recorded but in a separate database, hence would not be used for research purposes. Furthermore, students were informed via email about the research projects, its goals and parameters, and advised to contact the first author (who had no teaching or assessment duties in any of the units involved in this research) with any questions and concerns. None of the students who decided to use the app opted out of the research and no queries or complaints were received.

Both participating lecturers were guided in the process of creating multiple-choice quizzes in alignment with their lecture topics. Commencing on the first day of the semester, one quiz question was set to release daily and expire at midnight of the same day. Once a student commenced the quiz, a timer was set allowing between 20 and 60 s to answer a question. For each correct answer a student was assigned points, which, when accumulated would convert into digital badges based on the following achievement levels: for giving five consequent correct answers, for answering a question in under 15 s, for earning more than 100 points and for reaching the top of the leaderboard.

Statistical analysis was conducted using SPSS with the following data:

Final student grades for pre and post app cohorts, where the goal was to measure the relationships between the average final grades (percentages) and the app engagement and uptake data; and

The app usage analytics (such as scores achieved when using the app).

The app’s introduction and student retention rates

In Semester 2, 2015 when the mobile app was first introduced in the trial units, there was an improvement in student retention (calculated at 12.23%), when compared with the pre-app cohorts of Semester 1, 2015. The same pattern of improvement (though not as strong) was also found when compared with Semester 2, 2014 (improvement of 9.22% recorded) and Semester 2, 2013 (improvement of 5.37% recorded) (Fig. 1 ).

Student retention rates for accounting and science cohort, before and after the mobile app was introduced in Semester 2, 2015 ( N  = 6939)

The app’s usage and academic performance

Students who used the app demonstrated an average grade/percentage mark of 65.19% compared to those who did not use the app, the latter averaging a grade/percentage mark of 58.16%. Therefore, the app users on average achieved marks 7.03% higher compared to students who chose not using the app.

Students’ performance within the app and their academic performance in the unit

A significant positive correlation of .40 was found between performing well on the app tasks and achieving higher academic grades (as per Fig. 2 ).

Correlation between app score and percentage mark (academic performance) ( N  = 6939)

Limitations

This study’s focus was on correlations between students’ app usage and their academic performance in the unit, however causal relations between these variables would need to be gauged further through future in-depth studies of student experience. A potential limitation of this study is attributed to the novelty effect which must be taken into account when evaluating educational technology initiatives as it can skew the results (Clark, 2015 ). A common phenomenon that students can experience during a course of study, particularly so when new technologies such as gamification are introduced (Koivisto & Hamari, 2014 ), the novelty effect has implications for extended application. For instance, the perceived usefulness of gamification declines with use and with learner’s age (Koivisto & Hamari, 2014 ). At the same time, because participants in our experimental group self-selected to use the app, it was likely there was a sampling bias which may have resulted in more conscientious students who were open to new experiences participating in the experimental group (Cochran, 1977 ).

Another limitation of our study lies in the sample composition. While our initial goal was to undertake analysis separately for the accounting and sciences cohorts (see Figs. 3 and 4 , for examples of initial findings), a decision was made to combine the cohorts to create a larger sample and produce statistically meaningful results. It is worth noting that when analysed separately, the app’s positive impact for the combined science cohorts was found to be significantly lower than for the accounting cohort, showing only 5–7% improvements in retention rates. These differences can be explained with disciplinary specifics of the accounting and science cohorts.

Student retention rates for sciences and accounting

Student retention rates by course

This study evaluated the effectiveness of a gamified mobile app as a learning tool. Relationships between student retention rates, academic achievement and the app usage were studied, with a number of positive correlations emerging. With interdisciplinary comparisons deemed problematic due to small cohort numbers, combining accounting and several sciences cohorts into one larger sample allowed for more statistically significant findings to be generated.

Keeping in mind that correlations do not necessarily entail causations and that other factors outside the study’s purview could be at play, the marked increase of student retention rate by 12.23% may suggest there was indeed some important trigger present for this increase to happen, especially since other variables (teaching staff, curriculum, and assessment types) did not change. This increased retention phenomenon is of interest, considering the expected decrease in first-year university student retention rates (O'Keeffe, 2013 ). At the same time, a 7.03% increase in students’ average academic performance may also suggest there is a potential to go beyond simply ‘engaging’ students in the learning activities with the app to actually boosting their academic knowledge acquisition and retention, converting it into higher final grades.

Finally, the positive correlation of .40 between students’ scoring highly on the app and achieving higher academic grades is also of importance as it can be used as a measure of internal consistency, suggesting that a subject-integrated app is well-positioned to help guide students towards better learning outcomes. With its design drawing on such pedagogical principles as spacing and testing effects as well as taking advantage of such known motivation-boosting features as digital badges and leaderboards, the app worked in synch with other learning processes, serving as a self-regulated but content-aligned learning tool. Considering evidence that mobile learning interventions tailored to learner groups homogenous in age have generally stronger impact (Sung et al., 2016 ), the fact that the presented intervention was introduced in the units traditionally populated by direct school leavers likely factored into the overall positive findings. Further trials, including those involving inter-class control groups and comparative studies with different disciplines and modes of learning can help clarify the findings further.

General implications of the findings are concerned with three main stakeholder groups in HE: universities, lecturers and students. In regards to the implications for universities, economic and social consequences of student attrition are substantial and can be understood in terms of lost tuition fees, forfeited funding for student places, reputational damage, bad publicity and various resources used up on students who ultimately do not complete their degrees (Johnson, 2012 ). The presented analysis of student engagement with the app and the consequent increased student retention suggests it is worth continuing to explore the retention-boosting potential of mobile app technologies.

Another set of implications are of concern to lecturers, and educators more widely. The following affordances of the app emerge as important to be considered when designing impactful mobile app offerings for students: an option to customise content to a specific course, the ease of use and general flexibility, the automated collection of the app usage analytics, and taking advantage of students’ existing patterns of engagement with mobile technologies to integrate the learning app into their daily usage repertoire.

In regards to students as stakeholders of mobile learning initiatives, there are three key implications. First, as students’ demand for personalised education is growing, mobile apps can enable student access to course material at a time of their own choosing by ‘pushing’ the quizzes to their mobile devices in sync with lecture and tutorial cycle. Second, the app’s leaderboard and digital badges features generate feedback for students allowing them to see how they are performing compared to their peers, with points and badges serving as milestones of the learning process. Third, the app can be used as a revision tool students can utilise when preparing for assessments.

The implications for these three groups of stakeholders present some useful considerations for HE institutions, academics and students, who are interested in taking better advantage of mobile technologies for learning.

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Acknowledgements

All authors would like to thank Professor Mike Keppell for his unceasing support and encouragement. The authors also would like to express their gratitude to the journal’s editors and reviewers for their invaluable feedback. Last but not least, the authors thank all students who participated in this research.

This research received ethical clearance from Swinburne’s Human Research Ethics Committee (SUHREC). The initial development of the app prototype was supported by a 2015 Learning Futures Seed Project grant awarded to Dr. Grainne Oates. Any potential conflicts of interest were minimal and resolved by having participant recruitment and data collection and analysis for this study conducted by researcher and learning technologist who were not involved in teaching and assessment in units described in this study. For the quantitative segment of this study, only aggregate statistical data was accessed and used, as per the approved institutional ethics protocol.

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Learning Transformations Unit, Swinburne University of Technology, Box 218, Mail H63, John Street, Hawthorn, VIC, 3122, Australia

Ekaterina Pechenkina

La Trobe Learning and Teaching, La Trobe University, Melbourne, Australia

Daniel Laurence

Swinburne Business School, Swinburne University of Technology, Melbourne, Australia

Grainne Oates

School of Science, Swinburne University of Technology, Melbourne, Australia

Daniel Eldridge

Swinburne Law School Swinburne University of Technology, Melbourne, Australia

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Contributions

EP designed the research project and developed the first draft of this paper while DL performed the statistical analyses of data and contributed to various critical aspects of the writing process and study design. Together, EP and DL were in charge of participant recruitment and data collection. GO and DE were teaching instructors in the units which piloted the app and both have contributed to the sections of this paper describing the app’s development and operationalisation processes. DH provided overall advice on the study’s design and data collection methods. All authors read and approved the final manuscript.

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Correspondence to Ekaterina Pechenkina .

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Ekaterina Pechenkina is Research Fellow based at the Learning Transformations Unit, Swinburne University of Technology. Ekaterina holds a PhD in cultural anthropology from the University of Melbourne (2014) and several other degrees. She was an International Research and Exchange Board fellow based at the California State University Bakersfield (2003–2004) and since 2016 is a member of Australian and New Zealand mobile learning group anzMLearn. Ekaterina is primarily interested in understanding the relationships between technology and education from critical standpoints. She has published widely on various education topics.

Daniel Laurence is a Senior Educational Designer with La Trobe Learning and Teaching, La Trobe University Learning. His primary research interest is in the effective application of game principles and technologies in education.

Grainne Oates is a Senior Lecturer in Accounting at Swinburne University of Technology. She specialises in teaching introductory and management accounting at undergraduate and post graduate levels.

Daniel Eldridge is a chemistry academic at Swinburne University of Technology. He teaches heavily into chemistry units and researches student engagement and sustainable study habits.

Professor Dan Hunter is the founding dean of Swinburne Law School. He is an international expert in internet law, intellectual property and cognitive science models of law. He holds a PhD from Cambridge on the nature of legal reasoning, as well as computer science and law degrees from Monash University and a Master of Laws by research from the University of Melbourne.

Ethics approval and consent to participate

This research received ethical clearance from Swinburne’s Human Research Ethics Committee (SUHREC). At the first instance of using the app, students were informed that their anonymous aggregate usage data would be collected for research purposes and might be published. While students could opt-out from this process, there were no opt-outs or complaints received.

Competing interests

Authors declare no competing interests in this research, however they would like to acknowledge that the initial development of the app prototype was supported by a 2015 Learning Futures Seed Project grant awarded to Dr. Grainne Oates. Any potential conflicts of interest resulting from Dr. Oates receiving this funding were minimal and resolved by having participant recruitment and data collection and analysis for this study conducted by the lead researcher (first author) and learning technologist (second author) who were not involved in teaching and assessment in units described in this study or had any personal stake in the app’s success. Further, the first author was in charge of designing this research project and developing the first draft of this paper; the second author performed the statistical analyses of data and contributed to various critical aspects of the writing process and study design; the third and the fourth authors were instructors in the units which piloted the app and have accessed the app analytics data and aggregate completion and performance rates from their respective units. Finally, the last author provided advice on the study’s design and data collection methods.

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Pechenkina, E., Laurence, D., Oates, G. et al. Using a gamified mobile app to increase student engagement, retention and academic achievement. Int J Educ Technol High Educ 14 , 31 (2017). https://doi.org/10.1186/s41239-017-0069-7

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DOI : https://doi.org/10.1186/s41239-017-0069-7

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Educational mobile apps for programming in python: review and analysis.

1. Introduction

2. background, 2.1. educational app assessments, 2.2. contribution, 3.1. overview, 3.2. selection of eligible solutions, 3.3. eliminating personalisation during the search, 3.4. data extraction and solution assessment, 3.5. data analysis, 4.1. search and categorizations, 4.2. features, 4.3. revenue streams: ads and cost, 4.4. user rating, 4.5. downloads, 4.6. country and developer, 4.7. market overload—difficult for users to navigate, 4.8. interface vs. review score, 4.9. in-app purchases and ads vs. review score, 4.10. country vs. review score, 4.11. others, 5. discussion, 5.1. principal findings, 5.2. initiatives for user navigation, 5.3. limitations, 6. conclusions, limitations, and future research, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Schnieder, M.; Williams, S. Educational Mobile Apps for Programming in Python: Review and Analysis. Educ. Sci. 2023 , 13 , 66. https://doi.org/10.3390/educsci13010066

Schnieder M, Williams S. Educational Mobile Apps for Programming in Python: Review and Analysis. Education Sciences . 2023; 13(1):66. https://doi.org/10.3390/educsci13010066

Schnieder, Maren, and Sheryl Williams. 2023. "Educational Mobile Apps for Programming in Python: Review and Analysis" Education Sciences 13, no. 1: 66. https://doi.org/10.3390/educsci13010066

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The impact of mobile apps in education learning on the go.

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Chief Growth Officer of Tynker , a leading K-12 edtech platform that has helped more than 100 million kids learn to code.

Mobile learning has become increasingly popular, mainly due to the Covid-19 pandemic that forced many schools to close and shift to online learning. This trend also led to mobile educational applications to support students, teachers and parents.

These apps provide greater flexibility, accessibility, engagement, personalization, real-time feedback, innovation and empowerment, but they also pose some challenges and risks. Concerns about screen time, data privacy, and the growing digital divide are becoming more prominent. Meanwhile, I believe the benefits of these revolutionary experiences to support learning are being overlooked.

I see both the concerns and the benefits firsthand as my company offers an educational app to teach kids coding skills.

Although there are many benefits of these mobile apps, in this article, I will focus on how they can facilitate learning on the go. I will also discuss the latest trends in mobile learning, including the advancements in artificial intelligence (AI), virtual reality (VR) and augmented reality (AR) in mobile education.

How Mobile Apps Facilitate Improved Learning

Redefining Traditional Learning Boundaries

Mobile apps have redefined learning by making it more personalized, accessible and flexible. They allow students to learn at their own pace, receive instant feedback and access various courses and skills from different platforms that offer education on general subject matter, to language and even coding. With features like AI-driven learning paths and gamification, these apps have added to the education sector, offering more opportunities for learners of all ages and backgrounds.

Personalized Learning Experiences

Mobile apps can use adaptive learning algorithms to cater to individual learning styles and paces. These algorithms adjust educational content to the needs and abilities of each learner, offering tailored on-demand academic resources. This can make education more student-centric, increasing engagement and retention. Adaptive learning algorithms can also observe learners' actions and provide appropriate interventions, personalized content and customized learning paths. They can also enhance motivation and confidence by providing immediate feedback and support.

Collaborative Learning And Global Connections

Mobile apps allow for global classroom collaboration, which promotes diverse perspectives, cultural exchanges and collaborative projects. Educational apps offer features such as chat rooms, discussion boards, video calls, file sharing and feedback tools, which enable students to connect with peers worldwide. Collaborative learning helps students communicate, share ideas and work on projects together. Examples of educational apps with collaborative features are EdApp, Slack and Google Drive. These apps help students learn effectively through collaboration and efficiently by leveraging the power of collaboration.

Skill Development Beyond Academics

Educational apps teach more than academic subjects. They can also focus on developing soft skills like communication, teamwork, problem-solving and creativity. Apps like EdApp, Slack, Duolingo and my company, Tynker, offer opportunities for learning languages, coding and other niche areas.

Challenges And The Road Ahead

Mobile educational apps are a great tool for students to enhance their learning and explore new perspectives. However, it's important to acknowledge some of the drawbacks that come with excessive or long-term use. Overuse of smartphones can lead to various health issues such as eye strain, poor physical fitness, sleep problems, pain, migraines, unhealthy eating habits, cognitive issues and changes in the brain's gray matter volume.

Moreover, mobile apps often collect and store personal data from users such as location, contacts, browsing history, preferences, etc. This data can be used for targeted advertising, marketing, or even harmful purposes. Users may not be aware of how their data is used or shared by app developers or third parties. Data breaches and cyber attacks can also expose sensitive information and compromise user security.

At my company, we acknowledge the significance of maintaining a healthy and balanced approach to screen time for young learners. As a team, we have made efforts to highly encourage parental involvement in establishing time limits, monitoring activity and filtering content to ensure their children use the app responsibly. Additionally, we've built into our product the means to promote the importance of taking breaks from the screen and support a well-rounded learning environment that includes activities beyond screen time.

It's essential to continue having these discussions around balancing the positive and negative impacts of these apps while ensuring that parents monitor their children's usage of this technology. Knowing the apps they download, the websites they visit and the amount of time they spend on screens can help parents not only control their child's educational growth but also their overall health and well-being.

Closing Remarks

Mobile applications have transformed and will continue to transform the education industry for students and teachers alike. The future of mobile apps in education is likely to be influenced by emerging technologies, including AR, VR and AI. AR and VR can create immersive and interactive learning environments that enhance learners' motivation, creativity and collaboration.

As we look toward the future of educational apps, it is essential to prioritize user trust and transparency around data collection. In doing so, apps can offer increasingly personalized, adaptive and data-driven learning experiences. This will allow for tailored learning paths, identification of areas for improvement and targeted feedback, ultimately enhancing the overall learning experience. Companies can do this by clearly outlining privacy policies, giving parents control over their children's data and collecting only the data necessary to provide services and improve the platform. It is also important to adhere to strict data security measures to protect user information and imbue the trust of users.

As a leader, parent, educator or simply an observer, I encourage you to keep learning about these emerging technologies and experiences. By doing so, you can better understand their impact and help us create a learning environment that is accessible to all.

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The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage

• David Manuel Duarte Oliveira   ORCID: orcid.org/0000-0002-8763-6997 1 ,
• Luís Pedro 1 &
• Carlos Santos 1  

Smart Learning Environments volume  8 , Article number:  14 ( 2021 ) Cite this article

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This paper was developed within the scope of a PhD thesis that intends to characterize the use of mobile applications by the students of the University of Aveiro during class time. The main purpose of this paper is to present the results of an initial pilot study that aimed to fine-tune data collection methods in order to gather data that reflected the practices of the use of mobile applications by students in a higher education institution during classes. In this paper we present the context of the pilot, its technological settings, the analysed cases and the discussion and conclusions carried out to gather mobile applications usage data logs from students of an undergraduate degree on Communication Technologies.

Our study gathered data from 77 participants, taking theoretical classes in the Department of Communication and Arts at the University of Aveiro. The research was based on the Grounded Theory method approach aiming to analyse the logs from the access points of the University. With the collected data, a profile of the use of mobile devices during classes was drawn.

The preliminary findings suggest that the use of apps during the theoretical classes of the Department of Communication and Art is quite high and that the most used apps are Social networks like Facebook and Instagram. During this pilot the accesses during theoretical classes corresponded to approximately 11,177 accesses per student. We also concluded that the students agree that accessing applications can distract them during these classes and that they have a misperception about their use of online applications during classes, as the usage time is, in fact, more intensive than what participants reported.

Introduction

The use of mobile devices by higher education students has grown in the last years (GMI, 2019 ). Technological advancements are also pushing society with consequent rapidly changing environments. Higher Education Institutions (HEIs) are not exempted from these technological changes and advancements, and it is compulsory that they follow this technological evolution so that the teaching-learning process is improved and enriched.

HEI’s are trying to integrate digital devices such as mobile phones and tablets, and informal learning situations, assuming that the use of these technologies may result in a different learning approach and increase students’ motivation and proficiency (Aagaard, 2015 ).

In a study by Magda, & Aslanian ( 2018 ), students report that they access course documents and communicate with the faculty via their mobile devices, such as smartphones. Over 40% say they perform searches for reports and access institutions E-Learning systems via mobile devices (Magda, & Aslanian, 2018 ). The EDUCAUSE Horizon Report - 2019 Higher Education Edition (Alexander et al., 2019 ) also mentions M-Learning as the main development in the use of technology in higher education. However, teachers believe students use their gadgets less than they actually do, and mobile devices also challenge teaching practices. Students use devices for off-task (Jesse, 2015 ) or parallel activities and there may be inaccurate references to their actual use of mobile devices.

Mobile device users have very different usage habits of their devices and their applications, and it is important to study and characterize these behaviours in different contexts, as explained below. The reports that usually support these studies are made with questions directed to the users themselves asking them questions about the apps they have on the devices and the reasons for using them. However, Gerpott & Thomas ( 2014 ) argue that other types of studies are needed to properly support this type of research.

Studies are usually conducted in organizations, based on the opinion of the participants, and cannot be replicated and generalized, for example, regarding the use of the internet or mobile applications by the general public, because these devices, unlike desktop devices, can be used anywhere and at any time (Gerpott & Thomas, 2014 ).

Furthermore, in mobile contexts, it becomes difficult for people to remember what they have used, because mobile applications can be used for various tasks, in various contexts, whether professional or personal, and the variety of applications, the use made, the periods of use are usually so wide and differentiated, that it can become difficult for users to refer which services or applications they have used, under which circumstances and how often. (Boase & Ling, 2013 ).

Thus, it is relevant, for several areas and especially for this research area, to have studies that cross-reference reported usage with actual usage. One of the ways to achieve this is with the use of logs of the use of mobile devices and applications, as mentioned by De Reuver & Bouwman ( 2015 ):

Using this approach this pilot study aims to create and validate a methodology:

i) to show the profile of these users,

ii) to reveal the kind of applications they use in the classroom and when they are in the institutions,

iii) and also, to compare the users’ perceptions with the real use of mobile applications.

Knowing the real usage and the usage students mention may provide valuable insights to teachers and HEIs and use this data for decision making about institutional applications to support students and teachers in their teaching and learning activities. Such information can also bring insights on the integration of M-Learning strategies, promoting interaction, communication, access to courses and the completion of assignments using students’ devices.

The central focus of this study is, therefore, to show preliminary results of the use of applications by students in class time during theoretical classes, through logs collected during class time.

The paper is divided into five parts. In the first part, relevant theoretical considerations are addressed, having in mind the current state of the art in terms of the literature and empirical work in this field. The second part outlines the study methodology. In the third part, the technological setting is highlighted. The cases and the results of the data analysis are described in the fourth part. Lastly, the results are interpreted, connected and crossed with the preliminary considerations.

Literature review

The massive use of mobile devices has created new forms of social interaction, significantly reducing the spatial difficulties that could exist, and today people can be reached and connected anytime and anywhere (Monteiro et al., 2017 ). This also applies to the school environment, where students bring small devices (smartphones, tablets and e-book readers) with them, which, thanks to easy access to an Internet connection, keep them permanently connected, even during classes.

In HEIs there is also a growing tendency among members of the academic community to use mobile devices in their daily activities (Oliveira et al., 2017 ), and students expect these devices to be an integral part of their academic tasks, too (Dobbin et al., 2011 ). A great number of users take advantage of mobile devices to search information and, since they do not always have computers available, these devices allow them an easy access to academic and institutional information (Vicente, 2013 ).

One of the challenges educational institutions face today has to do with the ubiquitous character of these devices and with finding ways in which they can be useful for learning, thus approaching a new educational paradigm: Mobile Learning (M-Learning) (Ryu & Parsons, 2008 ).

M-learning allows learning to take place in multiple places, in several ways and when the learner wants to learn. As learning does not necessarily have to occur within school buildings and schedules, M-Learning reduces the limitations of learning confined to the classroom (Sharples, M., Corlett, D. & Westmancott,  2002 ), leading UNESCO to consider that M-Learning, in fact, increases the reach of education and may promote equality in education (UNESCO, 2013 ). The EDUCAUSE Horizon Report - 2019 Higher Education Edition (Alexander et al., 2019 ) also mentions M-Learning as the main development in the use of technology in higher education and, therefore, it becomes increasingly relevant to rethink learning spaces in a more open perspective, both physically and methodologically, namely through mobile learning that places the student at the centre of the learning process.

Quite often studies that intend to determine the use of mobile applications focus on general questions, but the most common ones are related to the frequency and duration of the use of these devices, for example, questions such as “how many SMS or calls are made?” or “how often do you use the device?”

In fact, instruments like questionnaires are widely used in this type of studies. However, since mobile devices are completely integrated in our daily life and we use them quite extensively, it is difficult to retain and define with plausible accuracy the actual use that we make of them.

It is therefore relevant to effectively understand how these students use these devices, more specifically the applications installed on them. To this end, most studies have been based on designs that are focused on the users’ perceptions and based are on these reports.

Thus, it was important to understand if what users report using corresponds to what they actually use, and if this use does not occur for distraction or entertainment, for example.

Considering the above, some studies have focused on the validity of the use of these instruments. One of these first studies, carried out by Parslow et al. ( 2003 ), aimed at determining the number of calls made and received in the days, weeks or months preceding the date of the questionnaire, and their duration. The answers were compared with the logs of the operators and it was concluded that self-report questionnaires do not always represent the actual pattern of use.

Finally, in self-report instruments, which refer to questions of daily activity on mobile devices, this activity may not represent a general pattern of activity, since from individual to individual the patterns of daily use may vary considerably and thus reflect a very irregular use.

In a study by Boase & Ling ( 2013 ), the authors mentioned that about 40% of studies on mobile device use, based on articles published in journals (41 articles between 2003 and 2010), are based on questionnaires.

The questions asked aim to estimate how long or what type of use they have made of their devices on a daily basis, and sometimes aim to know about time periods of several days. In most of these studies, 40% of papers use at least one measure of frequency of use and 27% a measure of duration of use that users make. Another factor that is mentioned is that users do not always report their usage completely accurately. On the other hand, the same study mentions that users may over or under report the use they make for reasons of sociability (Boase & Ling, 2013 ).

Given the moderate correlation between self-report instruments and data from records or logs (Boase & Ling, 2013 ), the author considers that researchers can significantly improve the results if they use, together with other instruments, data from logs to make their studies more accurate and rigorous. Another suggestion would be the use of mobile applications that record these usage behaviours (Raento et al., 2009 ).

Indeed, this kind of instrument is widely used in this type of studies. However, given that mobile devices are fully integrated into our daily lives and we use them quite extensively, it becomes difficult to retain and define with plausible accuracy the use we make of them. In addition to the factors mentioned in the previous paragraph, it is important that these types of studies are validated with other methods, such as the use of logs, as presented in this study. The logs in this study refer to the capture records of the mobile device traffic made by the students.

This article therefore aims to present preliminary results with an approach that uses cross-checking of log data with questionnaire results.

Methodology

This article intends to present and discuss preliminary results of a study that aims to characterize the use of mobile applications at the University of Aveiro through collected logs, crossing its results with questionnaires answered by students during the classes, and also with an observation grid with data from the analysed class and questions to teachers related to what teachers recommend regarding the use of mobile phones during class time.

The research question that motivated this article is: which digital applications/services are most frequently used on mobile devices by the students of the University of Aveiro during their classes?

The study was composed of 40 students, that answered the questionnaires.

The research was based on the Grounded Theory method aiming to analyse the logs from the access points of the University. With the collected data, a usage profile of mobile devices during classes was drawn.

Figure  1 presents a diagrammatic representation of the created methodological process.

General diagram of the study

Therefore 3 instruments were used for the data collection: a questionnaire, an observation grid and logs collected through mobile traffic in the wi-fi network of the university.

The questionnaire allowed for a quantitative assessment of the profile of the participants and collected data on the use that participants claimed to make of their mobile devices. The observation grid served as a guide for the implementation of the study, allowing to record data on the classes where the collections took place and to verify whether certain items were present, such as permission to use mobile devices or planning to use them by teachers. The observation grid would also serve to make the link between use and content in class, but in this pilot, it was not possible to make this link between the class content and the usage of mobile applications, because the author could not observe the applications used by students.

The database containing the usage records enabled the analysis of the logs, resulting in the quantification and verification of the type of activity that each (anonymous) participant made of their device.

The 3 instruments used aimed to i) determine which application(s) students were really using during the classes, through the analysis of the data logs collected from the Wi-Fi network of the University; ii) identify the participants’ representations of their activities by means of several questions regarding mobile usage during class time; iii) observe students’ behaviour and focus via an observation grid that was used by the researcher/observer when he was attending the classes.

The group who participated in this pilot study was selected in accordance with the professors and classes available, so it is considered a convenience sample. The group was constituted by students of undergraduate classes from the Communication and Arts Department of the University of Aveiro.

Table  1 summarizes the schedule of the pilots carried out, the curricular units where they took place, their duration and the instruments used. For ease of management, all the pilots took place in the same department of the University.

The Table  2 summarizes the collected data from questionnaires and logs.

This pilot aimed to build an approach to data analysis, close to the Grounded Theory methodology, in which a provisional theory is built based on the observed and analysed data (Alves et al., 2017 ; Long et al., 1993 ). The data collected in this pilot will serve to define a more complete methodology to be used in a larger study.

This chapter is divided into three parts: context, technological setting and cases analysed. In the context part, the classes which are part of the study will be described, relating the answers from the questionnaires with the teachers’ recommendations about the use of mobile devices. In the technological scenario section, it is intended to describe the technological background underlying the collection process of the logs and in the last part, analysed cases, the objective was to validate if the data to be collected matched the outlined objectives.

In the questionnaire, the questions were divided into two main groups: aspects related to the participant’s profile and aspects directly related to the use of the applications. Aspects related to participants were intended to characterize them. Regarding the use of applications, we aimed to find out the students’ perception of the applications they use in their daily routine, inside and outside of the classroom, and how they do it. Data were collected using a Google Forms form and processed using Microsoft Excel.

In this subchapter, through the data collected from the students’ answers to the questionnaires, and by crossing this information with the data collected from the teachers in the observation grid, we try to describe the context of the pilot.

All of the teachers stated that they allowed their students to use mobile phones during class time, but that they did not plan that use. They also stated that in most part of the classes several students use their mobile phones and apps to search for class related materials. The teachers also showed curiosity about knowing, with more detail, the mobile phone use their students actually have.

In the three classes analysed (Aesthetics, Scriptwriting and Music in History and Culture), when asked about the possibility of using mobile applications as a pedagogical complementary resource 43%, 47% and 55% of students fully agreed that these should be used. In these three classes, 31%, 44%, and 67% of students showed a more moderate opinion: they agreed (but not in such an assertive way) that these should be used.

Another conclusion is that most of the students used a smartphone (88,9%, 75%, 52%) during class time, but many of them also used a computer (66,7%, 100%, 84%). The percentage use of tablets is much lower (11,1%, 0%, 15%).

In the analysed scenario, the majority of the students used the android operating system and 94% also agreed that mobile applications could help to manage the academic tasks, except in the case of the “Aesthetics Curricular Unit”.

When it comes to the time of use, per week, in classes, 53%, 58%, and 22% of the students answered they used these devices between 4 to 5 days a week and 15%, 40% and 70% said they used them between 1 to 3 days a week.

Students were also asked about how frequently they accessed mobile applications during class time and, in all, 77% of the respondents reported accessing apps at least between 1 to 5 times per class. About 20% referred they accessed apps from 6 to 10 times per class.

As for the purposes of accessing apps during classes, most students mentioned categories related i) to support the class / to research (70%, 100%, 77,8%), ii) to access institutional platforms (47.4%, 66.7%, 89, 9%), iii) to access to information (47.4%, 50%, 66.7%) and iv) to work (36.8%, 50%, 44.4%).

Interestingly, the categories communication (52.6%, 41.7%, 22.3%), collaboration (10.5%, 16.7%, 0%), access to institutional services (5.3%, 0% 0%) and “I do not use them” (10.5%, 0%, 0%) presented very low percentages, namely the last one.

When questioned about the use of mobile devices that did not include academic reasons, many students referred to the categories “to be linked/connected” or “to be updated” (42.1%, 66.7%, 33.3%), “to communicate” (57.7% 75.7%, 66.7%), “to share and access content” (31.6%, 58.3%, 33.3%), but few mentioned “for entertainment” (26.3%, 16.7%, 22.2%), “as a habit or routine” (10.5%, 41.7%, 11.1%) and “I do not use them” (10.5%, 0%, 11.1%).

When asked about which mobile applications are most used in an academic context, the most relevant category was “to research / to study” (73.7%, 58.3%, 89.9%), “to check the calendar” (31.6%, 25%, 66.7% %) and “to surf the web” (47.4%, 50%, 55.6%). Again, categories such as “to work” (36.8%, 33.3%, 33.3%), “to take notes” (26,2%, 33.3%, 55.6%) and “to create content” (31.6%, 25%, 11.1%) presented relatively low percentages. It should also be noted that the respondents presented answers that created categories which were not expected such as “to watch films” (10.5%, 8.3%, 0%), “to listen to music” (31.6%, 33.3%, 33.3%), “to take photos” (10.5%, 0%, 0%) and “to play games” (5.3%, 0%, 0%) All the students said that they used applications during classes in at least one of the categories. In fact, in the three courses no one stated “not to use them” (0% in all).

When asked about the teachers’ permission to use the mobile devices in the classroom, most of the students said that teachers allowed free use (52.6%, 100%, 77.8%). Only a few stated that teachers allowed using them specifically when planned (41, 1%, 0%, 22.2%). The respondents of one course stated that teachers did not allow the use of devices (Aesthetics - 5.3%). Finally, when asked about the usefulness of integrating mobile applications in class, there was an overwhelming majority of respondents (100%, 78,9%, 100%) saying they believed that such integration could be enriching and useful.

Below is presented a table describing the most used mobile apps during class activities. It should be noted that only the two answers with the greatest relevance for each category were considered.

Table  3 systematizes what the results have been showing until now: there is an important part of students that use mobile phones during their classes and, even when teachers advise them not to use them, they ignore the recommendations and use them anyway. The main purposes stated were: to be in contact with others through social networking but also to access different kinds of information in browsers. Moreover, the classes where the use of devices is not recommended by the teachers seems to be the one where some applications are most used.

Technological setting

In this section we intend to describe the technological background underlying the process of collecting the logs. The first goal was to register and capture logs from the wi-fi network of the university, which consists of a wireless network that users can access using their universal user credentials.

In order to do that a meeting was scheduled with the university’s technology services, as our main concern was the anonymization of the data collected in order (i) to confer more neutrality to the data treatment, and (ii) to comply with European data protection legislation. Another issue for discussion was the need of powerful machines so that they could process the large amount of data collected.

In this meeting the necessary steps were agreed in order to guarantee the users’ privacy, the authorization of the university’s central services to do the study and the registration method of the logs. The overall procedure demanded several experiences of data collection to fine-tune the final pilot, which works as the basis capture setting for all the main study.

The Wi-Fi traffic capture software (Wireshark) was selected to work both with Android and IOS devices and it was possible to understand the functionalities of the software.

The pilot also helped to understand and solve additional problems that appeared during the previous tests, related to the anonymization of the users’ data. It was necessary to ensure that the users’ personal data were not identifiable, which was a commitment: in fact, only HTTPS Footnote 1 traffic was captured, being all the other information encrypted.

After the first tests, an initial data collection pilot took place in a classroom context. A specific capture system was created to allow the capture of mobile application logs used only by a certain group of students, from a designated Curricular Unit. A specific scenario was set up to ensure that only those students communicating through the IP Footnote 2 defined for the scenario and during that class time were considered and treated under the scope of this study:

If the traffic of the concerned student is communicating through one of the APs (Access Points) covering the room, then the device will be assigned a “Room network” IP;

If the student’s traffic is not communicating through one of the APs covering the room, then the device will be assigned a “Non Room network” IP;

If the student traffic does not belong to the group to be analysed and the device in question is communicating through one of the APs covering the room, then the device will be assigned an IP from a “normal eduroam network”;

In the final steps we resolved the IP’s in Wireshark (software used for the capture) and the unsolved IP’s where filtered in a PHP Footnote 3 script, through the gethostbyaddr method where the unsolved ones are incrementally added.

Finally, using an IP list, we performed a comparison to resolve any unresolved names;

This step allowed to fine tune the process and to make the final test.

Analysed cases

After performing these tests, a scenario for this final pilot was set up to validate if the data to be collected matched the outlined objectives. In this final pilot, logs were collected in a classroom so that the scenario was as close to the desired collection as possible. In this pilot, it was possible to verify that the collected data fulfilled the requirements. At this point, in addition to the HTTPS traffic packets, the packets referring to DNS Footnote 4 traffic were also included. This option made the HTTPS traffic more easily understandable. Furthermore, the researcher could conclude that all authenticated devices belonged to separate accounts.

The results show that the pre-tests/pilots and the final pilot turned out very well and in a very reliable way since they allowed to verify the main problems that could occur and helped to certify that the traffic anonymity condition was respected. In fact, only the HTTPS was considered, and all other communication was encrypted with no risk of corruption of private data. Moreover, this option had an important justification: the fact that HTTPS traffic could be more easily understandable and the fact that it allowed certifying that all the authenticated devices of the wireless network belonged to separate accounts.

To process and create output visualization of the data, the choice was an integrated solution, both for the processing stage and for creating visualisations. Given the variety of tools available, several were tried out and Tableau Software® (Tableau Prep® and Tableau Desktop®) was chosen. Tableau Software is an interactive data processing and visualisation tool that belongs to the Salesforce company and, although it is paid software, it allows for an academic licence that was used in this project.

This solution, besides allowing working with a large amount of data, also allows for a very interactive data treatment and visualisation. This software also allows the importation of data from various sources, which in the case of this study was also an advantage.

This solution allowed us to work with large amounts of data but it also allowed for a very interactive data treatment and visualization. In the case of Tableau Prep, the file with the logs was imported in a CSV format Footnote 5 and treated iteratively in a dynamic way, being refined to the desired data in a second stage. As an example, we can mention the separation of the field “time duration” in hours, minutes and seconds fields; all the IPs were converted to a generic name “student”; all the destinations visited by the students were grouped in main categories, as for instance “Facebook”, as each application had numerous distinct destinations.

About 30 changes in data treatment and in data flow “cleaning” were performed, which were, later, exported to Tableau Desktop. Each file imported to Tableau Prep, in addition to the changes applied to the previous file, was refined with more changes, in an iterative process.

After treating the data on Tableau prep the generated data flow was imported to Tableau Desktop so that dynamic data visualizations were created. At this stage, dimensions, measurements, and filters were created according to the desired data visualization. The software has the big advantage of creating dynamic visualizations of the logs’ data which allows for a different and richer perspective on the data obtained, in order to deepen further studies about the same topic.

Discussion and conclusions

This paper aimed to describe the process of a pilot to carry out a larger study where we wanted to cross-reference actual usage data (logs) of mobile applications in the classroom with data from student questionnaires. In this article we also present the main results of this pilot, both from the point of view of the process of the pilot and from the point of view of the data of use of mobile applications by students in the classroom.

From the preliminary data analysis of this pilot, we can infer that the most used apps are Facebook, Google and Instagram, as we can see in Fig.  2 and Fig.  3 , although some variations between the attendees of the courses were registered when it comes to other apps. For example, in the case of the Design course, there are alternative apps being used such as YouTube or Vimeo.

General use of applications in Scriptwriting class

General use of applications in Aesthetics classe

Another noticeable preliminary result is that students use Facebook more at the beginning of classes and Instagram is used more at the end, as we can see in Fig.  4 and Fig.  5 .

Use of Facebook per hour in Scriptwriting class

Use of Instagram per hour in Scriptwriting class

In addition, the developed model was used in the main study with a bigger convenience sampling approach, which may provide a more accurate representation of the population of mobile-phone-users in the study field.

The visualizations created in a dynamic way during this study showed that the use of logs as a complementary data provider to other instruments, such as questionnaires, can be an added value for this research field.

On the other hand, this pilot contradicts (sometimes slightly, others considerably) the results of the questionnaires answered by the students and whose logs were collected and analysed. Logs show that:

there is a common use of mobile applications during the classes;

the purpose of the access is different: participants report that they use mobile applications mostly for academic reasons, but it can be noted that there is a general use of other mobile applications such as social networks and Youtube;

the usage time is much longer than what participants reported;

the frequency is also different: students stated that they use mobile applications in classes only 1–3 days a week, but we found that, in the analysed classes, there is an almost constant use of them, and finally

students report that they do not use social networks much in class, but the use is, in fact, massive.

The students’ perception of the “use of mobile devices and applications during lessons”, and as already mentioned, during a teaching activity - 70% of the students refer using the applications between 1 to 5 times, 22% between 6 to 10 times and 4% more than 10 times. It should also be noted, as previously mentioned, that only 4% mention not using them. With regards to the use during the week, 56% of the students refer using them between 4 to 5 days per week and 39% between 1 to 3 days per week. There is also a relatively low percentage of students mentioning that they use the devices during class more than ten times (4%).

However, analysis of the logs shows that this use appears to be much more intensive. We performed a calculation based on the average number of accesses, from which we removed 40% of potential automatic accesses and divided by the average number of accesses each application had in the initial test. The results present 6.6 accesses to the device per class/student in the class with the fewest accesses, and for the highest case, 313 accesses to the device per class/student.

This result is reinforced by results from other studies, such as the Mobile Survey Report, which states that students make regular use of laptops and smartphones during lessons (Seilhamer et al., 2018 ).

These conclusions lead us to some very serious insights on this subject. Apparently, even older students have a misperception of their use of online applications during classes. There is a serious discrepancy and incongruency between the behaviours that they claim to adopt and those they actually engage in during the classes. There are authors, who argue for the need for other types of studies that support this type of approach (Gerpott & Thomas, 2014 ), because the perception reported by users may not correspond to the actual use. It means that this gap deserves a deeper reflection. Why does it happen? Are students not motivated in higher education? Is the world offered online more interesting than the one in the physical campus? We will try to answer these questions in the main study.

Availability of data and materials

Some of the visualizations created are publicly available at https://public.tableau.com/profile/davidoliveiraua

HTTPS It is a protocol used for secure communication over a computer network, and is widely used on the Internet

IP is the s a numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication

PHP is a general-purpose scripting language especially suited to web development

DNS is naming system for computers, services, or other resources connected to the Internet

Unformatted file where values are separated by commas

Abbreviations

Higher Education Institutions

Access Points

Hypertext Transfer Protocol Secure

Internet Protocol

Hypertext Preprocessor

Domain Name System

Comma-separated values

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DO wrote the introduction and discussion, and saw to the article structure, wrote the method section and conducted the data analysis. LP and CS conducted the literature review. All authors contributed to the discussion and conclusion sections, and the overall flow of the article. All authors read and approved the final manuscript.

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Oliveira, D.M.D., Pedro, L. & Santos, C. The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage. Smart Learn. Environ. 8 , 14 (2021). https://doi.org/10.1186/s40561-021-00159-6

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DOI : https://doi.org/10.1186/s40561-021-00159-6

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