quantitative research about homework

ORIGINAL RESEARCH article

“homework should be…but we do not live in an ideal world”: mathematics teachers’ perspectives on quality homework and on homework assigned in elementary and middle schools.

• 1 Departamento de Psicologia Aplicada, Escola de Psicologia, Universidade do Minho, Braga, Portugal
• 2 Departamento de Psicología, Universidad de Oviedo, Oviedo, Spain

Existing literature has analyzed homework characteristics associated with academic results. Researchers and educators defend the need to provide quality homework, but there is still much to be learned about the characteristics of quality homework (e.g., purposes, type). Acknowledging that teachers play an important role in designing and assigning homework, this study explored teachers’ perspectives regarding: (i) the characteristics of quality homework and (ii) the characteristics of the homework tasks assigned. In the current study, mathematics teachers from elementary and middle schools ( N = 78) participated in focus group discussions. To enhance the trustworthiness of the findings, homework tasks assigned by 25% of the participants were analyzed for triangulation of data purposes. Data were analyzed using thematic analysis for elementary and middle school separately. Teachers discussed the various characteristics of quality homework (e.g., short assignments, adjusted to the availability of students) and shared the characteristics of the homework tasks typically assigned, highlighting a few differences (e.g., degree of individualization of homework, purposes) between these two topics. Globally, data on the homework tasks assigned were consistent with teachers’ reports about the characteristics of the homework tasks they usually assigned. Findings provide valuable insights for research and practice aimed to promote the quality of homework and consequently students’ learning and progress.

Introduction

The extensive literature on homework suggests the importance of completing homework tasks to foster students’ academic achievement (e.g., Trautwein and Lüdtke, 2009 ; Hagger et al., 2015 ; Núñez et al., 2015a ; Valle et al., 2016 ; Fernández-Alonso et al., 2017 ). However, existing research also indicate that the amount of homework assigned is not always related to high academic achievement ( Epstein and Van Voorhis, 2001 ; Epstein and Van Voorhis, 2012 ). In the words of Dettmers et al. (2010) “homework works if quality is high” (p. 467). However, further research is needed to answer the question “What is quality homework?”.

Teachers are responsible for designing and assigning homework, thus our knowledge on their perspectives about this topic and the characteristics of the homework typically assigned is expected to be a relevant contribution to the literature on the quality of homework. Moreover, data on the characteristics of homework could provide valuable information to unveil the complex network of relationships between homework and academic achievement (e.g., Cooper, 2001 ; Trautwein and Köller, 2003 ; Trautwein et al., 2009a ; Xu, 2010 ).

Thus, focusing on the perspective of mathematics teachers from elementary and middle school, the aims of the present study are twofold: to explore the characteristics of quality homework, and to identify the characteristics of the homework tasks typically assigned at these school levels. Findings may help deepen our understanding of why homework may impact differently the mathematics achievement of elementary and middle school students (see Fan et al., 2017 ).

Research Background on Homework Characteristics

Homework is a complex educational process involving a diverse set of variables that each may influence students’ academic outcomes (e.g., Corno, 2000 ; Trautwein and Köller, 2003 ; Cooper et al., 2006 ; Epstein and Van Voorhis, 2012 ). Cooper (1989 , 2001 ) presented a model outlining the factors that may potentially influence the effect of homework at the three stages of the homework process (i.e., design of the homework assignment, completion of homework and homework follow-up practices). At the first stage teachers are expected to consider class characteristics (e.g., students’ prior knowledge, grade level, number of students per class), and also variables that may influence the impact of homework on students’ outcomes, such as homework assignment characteristics. In 1989, Cooper (see also Cooper et al., 2006 ) presented a list of the characteristics of homework assignments as follows: amount (comprising homework frequency and length), purpose, skill area targeted, degree of individualization, student degree of choice, completion deadlines, and social context. Based on existing literature, Trautwein et al. (2006b) proposed a distinct organization for the assignment characteristics. The proposal included: homework frequency (i.e., how often homework assignments are prescribed to students), quality, control, and adaptivity. “Homework frequency” and “adaptivity” are similar to “amount” and “degree of individualization” in Cooper’s model, respectively. Both homework models provide a relevant theoretical framework for the present study.

Prior research has analyzed the relationship between homework variables, students’ behaviors and academic achievement, and found different results depending on the variables examined (see Trautwein et al., 2009b ; Fan et al., 2017 ). For example, while homework frequency consistently and positively predicted students’ academic achievement (e.g., Trautwein et al., 2002 ; Trautwein, 2007 ; Fernández-Alonso et al., 2015 ), findings regarding the amount of homework assigned (usually assessed by the time spent on homework) have shown mixed results (e.g., Trautwein, 2007 ; Dettmers et al., 2009 ; Núñez et al., 2015a ). Data indicated a positive association between the amount of homework and students’ academic achievement in high school (e.g., OECD, 2014a ); however, this relationship is almost null in elementary school (e.g., Cooper et al., 2006 ; Rosário et al., 2009 ). Finally, other studies reported a negative association between time spent on homework and students’ academic achievement at different school levels (e.g., Trautwein et al., 2009b ; Rosário et al., 2011 ; Núñez et al., 2015a ).

Homework purposes are among the factors that may influence the effect of homework on students’ homework behaviors and academic achievement ( Cooper, 2001 ; Trautwein et al., 2009a ; Epstein and Van Voorhis, 2012 ; Rosário et al., 2015 ). In his model Cooper (1989 , 2001 ) reported instructional purposes (i.e., practicing or reviewing, preparation, integration and extension) and non-instructional purposes (i.e., parent-child communication, fulfilling directives, punishment, and community relations). Depending on their nature, homework instructional purposes may vary throughout schooling ( Muhlenbruck et al., 2000 ; Epstein and Van Voorhis, 2001 ). For example, in elementary school, teachers are likely to use homework as an opportunity to review the content taught in class, while in secondary school (6th–12th grade), teachers are prone to use homework to prepare students for the content to be learned in subsequent classes ( Muhlenbruck et al., 2000 ). Still, studies have recently shown that practicing the content learned is the homework purpose most frequently used throughout schooling (e.g., Xu and Yuan, 2003 ; Danielson et al., 2011 ; Kaur, 2011 ; Bang, 2012 ; Kukliansky et al., 2014 ). Studies using quantitative methodologies have analyzed the role played by homework purposes in students’ effort and achievement ( Trautwein et al., 2009a ; Rosário et al., 2015 , 2018 ), and reported distinct results depending on the subject analyzed. For example, Foyle et al. (1990) found that homework assignments with the purposes of practice and preparation improved the performance of 5th-grade students’ social studies when compared with the no-homework group. However, no statistical difference was found between the two types of homework purposes analyzed (i.e., practice and preparation). When examining the homework purposes reported by 8th-grade teachers of French as a Second Language (e.g., drilling and practicing, motivating, linking school and home), Trautwein et al. (2009a) found that students in classes assigned tasks with high emphasis on motivation displayed more effort and achieved higher outcomes than their peers. On the contrary, students in classes assigned tasks with high drill and practice reported less homework effort and achievement ( Trautwein et al., 2009a ). A recent study by Rosário et al. (2015) analyzed the relationship between homework assignments with various types of purposes (i.e., practice, preparation and extension) and 6th-grade mathematics achievement. These authors reported that homework with the purpose of “extension” impacted positively on students’ academic achievement while the other two homework purposes did not.

Cooper (1989 , 2001 ) identified the “degree of individualization” as a characteristic of homework focused on the need to design homework addressing different levels of performance. For example, some students need to be assigned practice exercises with a low level of difficulty to help them reach school goals, while others need to be assigned exercises with high levels of complexity to foster their motivation for homework ( Trautwein et al., 2002 ). When there is a disparity between the level of difficulty of homework assignments and students’ skills level, students may have to spend long hours doing homework, and they may experience negative emotions or even avoid doing homework ( Corno, 2000 ). On the contrary, when homework assignments meet students’ learning needs (e.g., Bang, 2012 ; Kukliansky et al., 2014 ), both students’ homework effort and academic achievement increase (e.g., Trautwein et al., 2006a ; Zakharov et al., 2014 ). Teachers may also decide on the time given to students to complete their homework ( Cooper, 1989 ; Cooper et al., 2006 ). For example, homework may be assigned to be delivered in the following class (e.g., Kaur et al., 2004 ) or within a week (e.g., Kaur, 2011 ). However, research on the beneficial effects of each practice is still limited.

Trautwein et al. (2006b) investigated homework characteristics other than those previously reported. Their line of research analyzed students’ perception of homework quality and homework control (e.g., Trautwein et al., 2006b ; Dettmers et al., 2010 ). Findings on homework quality (e.g., level of difficulty of the mathematics exercises, Trautwein et al., 2002 ; homework “cognitively activating” and “well prepared”, Trautwein et al., 2006b , p. 448; homework selection and level of challenge, Dettmers et al., 2010 ; Rosário et al., 2018 ) varied regarding the various measures and levels of analysis considered. For example, focusing on mathematics, Trautwein et al. (2002) concluded that “demanding” exercises improved 7th-grade students’ achievement at student and class levels, while “repetitive exercises” impacted negatively on students’ achievement. Dettmers et al. (2010) found that homework assignments perceived by students as “well-prepared and interesting” (p. 471) positively predicted 9th- and 10th-grade students’ homework motivation (expectancy and value beliefs) and behavior (effort and time) at student and class level, and mathematics achievement at class level only. These authors also reported that “cognitively challenging” homework (p. 471), as perceived by students, negatively predicted students’ expectancy beliefs at both levels, and students’ homework effort at student level ( Dettmers et al., 2010 ). Moreover, this study showed that “challenging homework” significantly and positively impacted on students’ mathematics achievement at class level ( Dettmers et al., 2010 ). At elementary school, homework quality (assessed through homework selection) predicted positively 6th-grade students’ homework effort, homework performance, and mathematics achievement ( Rosário et al., 2018 ).

Finally, Trautwein and colleagues investigated the variable “homework control” perceived by middle school students and found mixed results. The works by Trautwein and Lüdtke (2007 , 2009 ) found that “homework control” predicted positively students’ homework effort in mathematics, but other studies (e.g., Trautwein et al., 2002 , 2006b ) did not predict homework effort and mathematics achievement.

The Present Study

A vast body of research indicates that homework enhances students’ academic achievement [see the meta-analysis conducted by Fan et al. (2017) ], however, maladaptive homework behaviors of students (e.g., procrastination, lack of interest in homework, failure to complete homework) may affect homework benefits ( Bembenutty, 2011a ; Hong et al., 2011 ; Rosário et al., 2019 ). These behaviors may be related to the characteristics of the homework assigned (e.g., large amount of homework, disconnect between the type and level of difficulty of homework assignments and students’ needs and abilities, see Margolis and McCabe, 2004 ; Trautwein, 2007 ).

Homework is only valuable to students’ learning when its quality is perceived by students ( Dettmers et al., 2010 ). Nevertheless, little is known about the meaning of homework quality for teachers who are responsible for assigning homework. What do teachers understand to be quality homework? To our knowledge, the previous studies exploring teachers’ perspectives on their homework practices did not relate data with quality homework (e.g., Xu and Yuan, 2003 ; Danielson et al., 2011 ; Kaur, 2011 ; Bang, 2012 ; Kukliansky et al., 2014 ). For example, Kukliansky et al. (2014) found a disconnect between middle school science teachers’ perspectives about their homework practices and their actual homework practices observed in class. However, results were not further explained.

The current study aims to explore teachers’ perspectives on the characteristics of quality homework, and on the characteristics underlying the homework tasks assigned. Findings are expected to shed some light on the role of teachers in the homework process and contribute to maximize the benefits of homework. Our results may be useful for either homework research (e.g., by informing new quantitative studies grounded on data from teachers’ perspectives) or educational practice (e.g., by identifying new avenues for teacher training and the defining of guidelines for homework practices).

This study is particularly important in mathematics for the following reasons: mathematics is among the school subjects where teachers assign the largest amount of homework (e.g., Rønning, 2011 ; Xu, 2015 ), while students continue to yield worrying school results in the subject, especially in middle and high school ( Gottfried et al., 2007 ; OECD, 2014b ). Moreover, a recent meta-analysis focused on mathematics and science homework showed that the relationship between homework and academic achievement in middle school is weaker than in elementary school ( Fan et al., 2017 ). Thus, we collected data through focus group discussions with elementary and middle school mathematics teachers in order to analyze any potential variations in their perspectives on the characteristics of quality homework, and on the characteristics of homework tasks they typically assign. Regarding the latter topic, we also collected photos of homework tasks assigned by 25% of the participating teachers in order to triangulate data and enhance the trustworthiness of our findings.

Our exploratory study was guided by the following research questions:

(1) How do elementary and middle school mathematics teachers perceive quality homework?

(2) How do elementary and middle school mathematics teachers describe the homework tasks they typically assign to students?

Materials and Methods

The study context.

Despite recommendations of the need for clear homework policies (e.g., Cooper et al., 2006 ; Bembenutty, 2011b ), Portugal has no formal guidelines for homework (e.g., concerning the frequency, length, type of tasks). Still, many teachers usually include homework as part of students’ overall grade and ask parents to monitor their children’s homework completion. Moreover, according to participants there is no specific training on homework practices for pre-service or in-service teachers.

The Portuguese educational system is organized as follows: the last two years of elementary school encompass 5th and 6th grade (10 and 11 years old), while middle school encompasses 7th, 8th, and 9th grade (12 to 14 years old). At the two school levels mentioned, mathematics is a compulsory subject and students attend three to five mathematics lessons per week depending on the duration of each class (270 min per week for Grades 5 and 6, and 225 min per week for Grades 7–9). All students are assessed by their mathematics teacher (through continuous assessment tests), and at the end of elementary and middle school levels (6th and 9th grade) students are assessed externally through a national exam that counts for 30% of the overall grade. In Portuguese schools assigning homework is a frequently used educational practice, mostly in mathematics, and usually counts toward the overall grade, ranging between 2% and 5% depending on school boards ( Rosário et al., 2018 ).

Participants

In the current study, all participants were involved in focus groups and 25% of them, randomly selected, were asked to submit photos of homework tasks assigned.

According to Morgan (1997) , to maximize the discussion among participants it is important that they share some characteristics and experiences related to the aims of the study in question. In the current study, teachers were eligible to participate when the following criteria were met: (i) they had been teaching mathematics at elementary or middle school levels for at least two years; and (ii) they would assign homework regularly, at least twice a week, in order to have enough experiences to share in the focus group.

All mathematics teachers ( N = 130) from 25 elementary and middle schools in Northern Portugal were contacted by email. The email informed teachers of the purposes and procedures of the study (e.g., inclusion criteria, duration of the session, session videotaping, selection of teachers to send photos of homework tasks assigned), and invited them to participate in the study. To facilitate recruitment, researchers scheduled focus group discussions considering participants’ availability. Of the volunteer teachers, all participants met the inclusion criteria. The research team did not allocate teachers with hierarchical relationships in the same group, as this might limit freedom of responses, affect the dynamics of the discussion, and, consequently, the outcomes ( Kitzinger, 1995 ).

Initially we conducted four focus groups with elementary school teachers (5th and 6th grade, 10 and 11 years old) and four focus groups with middle school teachers (7th, 8th, and 9th grade, 12, 13 and 14 years old). Subsequently, two additional focus group discussions (one for each school level) were conducted to ensure the saturation of data. Finally, seventy-eight mathematics teachers (61 females and 17 males; an acceptance rate of 60%) from 16 schools participated in our study (see Table 1 ). The teachers enrolled in 10 focus groups comprised of seven to nine teachers per group. Twenty teachers were randomly selected and asked to participate in the second data collection; all answered positively to our invitation (15 females and 5 males).

Table 1. Participants’ demographic information.

According to our participants, in the school context, mathematics teachers may teach one to eight classes of different grade levels. In the current research, participants were teaching one to five classes of two or three grade levels at schools in urban or near urban contexts. The participants practiced the mandatory nationwide curriculum and a continuous assessment policy.

Data Collection

We carried out this study following the recommendations of the ethics committee of the University of Minho. All teachers gave written informed consent to participate in the research in accordance with the Declaration of Helsinki. The collaboration involved participating in one focus group discussion, and, for 25% of the participants, submitting photos by email of the homework tasks assigned.

In the current study, aiming to deepen our comprehension of the research questions, focus group interviews were conducted to capture participants’ thoughts about a particular topic ( Kitzinger, 1995 ; Morgan, 1997 ). The focus groups were conducted by two members of the research team (a moderator and a field note-taker) in the first term of the school year and followed the procedure described by Krueger and Casey (2000) . To prevent mishandling the discussions and to encourage teachers to participate in the sessions, the two facilitators attended a course on qualitative research offered at their home institution specifically targeting focus group methodology.

All focus group interviews were videotaped. The sessions were held in a meeting room at the University of Minho facilities, and lasted 90 to 105 min. Before starting the discussion, teachers filled in a questionnaire with sociodemographic information, and were invited to read and sign a written informed consent form. Researchers introduced themselves, and read out the information regarding the study purpose and the focus group ground rules. Participants were ensured of the confidentiality of their responses (e.g., names and researchers’ personal notes that might link participants to their schools were deleted). Then, the investigators initiated the discussion (see Table 2 ). At the end of each focus group discussion, participants were given the opportunity to ask questions or make further contributions.

Table 2. Focus group questions.

After the focus group discussions, we randomly selected 25% of the participating teachers (i.e., 10 teachers from each school level), each asked to submit photos of the homework tasks assigned by email over the course of three weeks (period between two mathematics assessment tests). This data collection aimed to triangulate data from focus groups regarding the characteristics of homework usually assigned. To encourage participation, the research team sent teachers a friendly reminder email every evening throughout the period of data collection. In total, we received 125 photos (51% were from middle school teachers).

Data Analysis

Videotapes were used to assist the verbatim transcription of focus group data. Both focus group data and photos of the homework assignments were analyzed using thematic analysis ( Braun and Clarke, 2006 ), assisted by QSR International’s NVivo 10 software ( Richards, 2005 ). In this analysis there are no rigid guidelines on how to determine themes; to assure that the analysis is rigorous, researchers are expected to follow a consistent procedure throughout the analysis process ( Braun and Clarke, 2006 ). For the current study, to identify themes and sub-themes, we used the extensiveness of comments criterion (number of participants who express a theme, Krueger and Casey, 2000 ).

Firstly, following an inductive process one member of the research team read the first eight focus group transcriptions several times, took notes on the overall ideas of the data, and made a list of possible codes for data at a semantic level ( Braun and Clarke, 2006 ). Using a cluster analysis by word similarity procedure in Nvivo, all codes were grouped in order to identify sub-themes and themes posteriorly. All the themes and sub-themes were independently and iteratively identified and compared with the literature on homework ( Peterson and Irving, 2008 ). Then, the themes and sub-themes were compared with the homework characteristics already reported in the literature (e.g., Cooper, 1989 ; Epstein and Van Voorhis, 2001 ; Trautwein et al., 2006b ). New sub-themes emerged from participants’ discourses (i.e., “adjusted to the availability of students,” “teachers diagnose learning”), and were grouped in the themes reported in the literature. After, all themes and sub-themes were organized in a coding scheme (for an example see Table 3 ). Finally, the researcher coded the two other focus group discussions, no new information was added related to the research questions. Given that the generated patterns of data were not changed, the researcher concluded that thematic saturation was reached.

Table 3. Examples of the coding scheme.

An external auditor, trained on the coding scheme, revised all transcriptions, the coding scheme and the coding process in order to minimize researchers’ biases and increase the trustworthiness of the study ( Lincoln and Guba, 1985 ). The first author and the external auditor examined the final categorization of data and reached consensus.

Two other members of the research team coded independently the photos of the homework assignments using the same coding scheme of the focus groups. To analyze data, the researchers had to define the sub-themes “short assignments” (i.e., up to three exercises) and “long assignments” (i.e., more than three exercises). In the end, the two researchers reviewed the coding process and discussed the differences found (e.g., some exercises had several sub questions, so one of the researchers coded it as “long assignments”; see the homework sample 4 of the Supplementary Material ). However, the researchers reached consensus, deciding not to count the number of sub questions of each exercise individually, because these types of questions are related and do not require a significant amount of additional time.

Inter-rater reliability (Cohen’s Kappa) was calculated. The Cohen’s Kappa was 0.86 for the data analysis of the focus groups and 0.85 for data analysis of the photos of homework assignments, which is considered very good according to Landis and Koch (1977) . To obtain a pattern of data considering the school levels, a matrix coding query was run for each data source (i.e., focus groups and photos of homework assignments). Using the various criteria options in NVivo 10, we crossed participants’ classifications (i.e., school level attribute) and nodes and displayed the frequencies of responses for each row–column combination ( Bazeley and Jackson, 2013 ).

In the end of this process of data analysis, for establishing the trustworthiness of findings, 20 teachers (i.e., ten participants of each grade level) were randomly invited, and all agreed, to provide a member check of the findings ( Lincoln and Guba, 1985 ). Member checking involved two phases. First, teachers were asked individually to read a summary of the findings and to fill in a 5-point Likert scale (1, completely disagree; 5, completely agree) with four items: “Findings reflect my perspective regarding homework quality”; “Findings reflect my perspective regarding homework practices”; “Findings reflect what was discussed in the focus group where I participated”, and “I feel that my opinion was influenced by the other teachers during the discussion” (inverted item). Secondly, teachers were gathered by school level and asked to critically analyze and discuss whether an authentic representation was made of their perspectives regarding quality homework and homework practices ( Creswell, 2007 ).

This study explored teachers’ perspectives on the characteristics of quality homework, and on the characteristics of the homework tasks typically assigned. To report results, we used the frequency of occurrence criterion of the categories defined by Hill et al. (2005) . Each theme may be classified as “General” when all participants, or all except one, mention a particular theme; “Typical” when more than half of the cases mention a theme; “Variant” when more than 3, and less than half of the cases mention a theme; and “Rare” when the frequency is between 2 and 3 cases. In the current study, only general and typical themes were reported to discuss the most salient data.

The results section was organized by each research question. Throughout the analysis of the results, quotes from participants were presented to illustrate data. For the second research question, data from the homework assignments collected as photographs were also included.

Initial Data Screening

All participating teachers defended the importance of completing homework, arguing that homework can help students to develop their learning and to engage in school life. Furthermore, participants also agreed on the importance of delivering this message to students. Nevertheless, all teachers acknowledged that assigning homework daily present a challenge to their teaching routine because of the heavy workload faced daily (e.g., large numbers of students per class, too many classes to teach, teaching classes from different grade levels which means preparing different lessons, administrative workload).

Teachers at both school levels talked spontaneously about the nature of the tasks they usually assign, and the majority reported selecting homework tasks from a textbook. However, participants also referred to creating exercises fit to particular learning goals. Data collected from the homework assigned corroborated this information. Most of participating teachers reported that they had not received any guidance from their school board regarding homework.

How do Elementary and Middle School Teachers Perceive Quality Homework?

Three main themes were identified by elementary school teachers (i.e., instructional purposes, degree of individualization/adaptivity, and length of homework) and two were identified by middle school teachers (i.e., instructional purposes, and degree of individualization/adaptivity). Figure 1 depicts the themes and sub-themes reported by teachers in the focus groups.

Figure 1. Characteristics of quality homework reported by mathematics teachers by school level.

In all focus group discussions, all teachers from elementary and middle school mentioned “instructional purposes” as the main characteristic of quality homework. When asked to further explain the importance of this characteristic, teachers at both school levels in all focus group talked about the need for “practicing or reviewing” the content delivered in class to strengthen students’ knowledge. A teacher illustrated this idea clearly: “it is not worth teaching new content when students do not master the material previously covered” (P1 FG3). This idea was supported by participants in all focus groups; “at home they [students] have to work on the same content as those taught in class” (P1 FG7), “students have to revisit exercises and practice” (P2 FG9), “train over and over again” (P6 FG1), “practice, practice, practice” (P4 FG2).

While discussing the benefits of designing homework with the purpose of practicing the content learned, teachers at both school levels agreed on the fact that homework may be a useful tool for students to diagnose their own learning achievements while working independently. Teachers were empathetic with their peers when discussing the instrumentality of homework as a “thermometer” for students to assess their own progress. This idea was discussed in similar ways in all focus group, as the following quotation illustrates:

P2 FG1: Homework should be a bridge between class and home… students are expected to work independently, learn about their difficulties when doing homework, and check whether they understood the content.

When asked to outline other characteristics of quality homework, several elementary school teachers in all focus group mentioned that quality homework should also promote “student development” as an instructional purpose. These participants explained that homework is an instructional tool that should be designed to “foster students’ autonomy” (P9 FG4), “develop study habits and routines” (P1 FG8), and “promote organization skills and study methods” (P6 FG7). These thoughts were unanimous among participants in all focus groups. While some teachers introduced real-life examples to illustrate the ideas posited by their colleagues, others nodded their heads in agreement.

In addition, some elementary school teachers observed that homework tasks requiring transference of knowledge could help develop students’ complex thinking, a highly valued topic in the current mathematics curriculum worldwide. Teachers discussed this topic enthusiastically in two opposite directions: while some teachers defended this purpose as a characteristic of quality homework, others disagreed, as the following conversation excerpt illustrates:

P7 FG5: For me good homework would be a real challenge, like a problem-solving scenario that stimulates learning transference and develops mathematical reasoning … mathematical insight. It’s hard because it forces them [students] to think in more complex ways; still, I believe this is the type of homework with the most potential gains for them.

P3 FG5: That’s a good point, but they [students] give up easily. They just don’t do their homework. This type of homework implies competencies that the majority of students do not master…

P1 FG5: Not to mention that this type of homework takes up a lot of teaching time… explaining, checking…, and we simply don’t have time for this.

Globally, participants agreed on the potential of assigning homework with the purpose of instigating students to transfer learning to new tasks. However, participants also discussed the limitations faced daily in their teaching (e.g., number of students per class, students’ lack of prior knowledge) and concluded that homework with this purpose hinders the successful development of their lesson plans. This perspective may help explain why many participants did not perceive this purpose as a significant characteristic of quality homework. Further commenting on the characteristics of quality homework, the majority of participants at both school levels agreed that quality homework should be tailored to meet students’ learning needs. The importance of individualized homework was intensely discussed in all focus groups, and several participants suggested the need for designing homework targeted at a particular student or groups of students with common education needs. The following statements exemplifies participants’ opinions:

P3 FG3: Ideally, homework should be targeted at each student individually. For André a simple exercise, for Ana a more challenging exercise … in an ideal world homework should be tailored to students’ needs.

P6 FG6: Given the diversity of students in our classes, we may find a rainbow of levels of prior knowledge… quality homework should be as varied as our students’ needs.

As discussed in the focus groups, to foster the engagement of high-achievers in homework completion, homework tasks should be challenging enough (as reported previously by P3 FG3). However, participants at both school levels observed that their heavy daily workload prevents them from assigning individualized homework:

P1 FG1: I know it’s important to assign differentiated homework tasks, and I believe in it… but this option faces real-life barriers, such as the number of classes we have to teach, each with thirty students, tons of bureaucratic stuff we have to deal with… All this raises real-life questions, real impediments… how can we design homework tasks for individual students?

Considering this challenge, teachers from both school levels suggested that quality homework should comprise exercises with increasing levels of difficulty. This strategy would respond to the heterogeneity of students’ learning needs without assigning individualized homework tasks to each student.

While discussing individualized homework, elementary school teachers added that assignments should be designed bearing in mind students’ availability (e.g., school timetable, extracurricular activities, and exam dates). Participants noted that teachers should learn the amount of workload their students have, and should be aware about the importance of students’ well-being.

P4 FG1: If students have large amounts of homework, this could be very uncomfortable and even frustrating… They have to do homework of other subjects and add time to extracurricular activities… responding to all demands can be very stressful.

P4 FG2: I think that we have to learn about the learning context of our students, namely their limitations to complete homework in the time they have available. We all have good intentions and want them to progress, but if students do not have enough time to do their homework, this won’t work. So, quality homework would be, for example, when students have exams and the teacher gives them little or no homework at all.

The discussion about the length of homework found consensus among the elementary school teachers in all focus group in that quality homework should be “brief”. During the discussions, elementary school teachers further explained that assigning long tasks is not beneficial because “they [students] end up demotivated” (P3 FG4). Besides, “completing long homework assignments takes hours!” (P5 FG4).

How do Elementary and Middle School Teachers Describe the Homework Tasks They Typically Assign to Students?

When discussing the characteristics of the homework tasks usually assigned to their students four main themes were identified by elementary school teachers (i.e., instructional purposes, degree of individualization/adaptivity, frequency and completion deadlines), and two main themes were raised by middle school (i.e., instructional purposes, and degree of individualization/adaptivity). Figure 2 gives a general overview of the findings. Data gathered from photos added themes to findings as follows: one (i.e., length) to elementary school and two (i.e., length and completion deadlines) to middle school (see Figure 3 ).

Figure 2. Characteristics of the homework tasks usually assigned as reported by mathematics teachers.

Figure 3. Characteristics of the homework tasks assigned by mathematics teachers.

While describing the characteristics of the homework tasks usually assigned, teachers frequently felt the need to compare the quality homework characteristics previously discussed with those practices. In fact, at this stage, teachers’ discourse was often focused on the analysis of the similarities and potential discrepancies found.

The majority of teachers at both school levels in all focus group reported that they assign homework with the purpose of practicing and reviewing the materials covered earlier. Participants at both school levels highlighted the need to practice the contents covered because by the end of 6th- and 9th-grade students have to sit for a national exam for which they have to be trained. This educational context may interfere with the underlying homework purposes teachers have, as this quotation illustrates:

P3 FG3: When teaching mathematics, we set several goals, but our main focus is always the final exam they [students] have to take. I like students who think for themselves, who push themselves out of their comfort zone. However, I’m aware that they have to score high on national exams, otherwise… so, I assign homework to practice the contents covered.

Beyond assigning homework with the purpose of practicing and reviewing, middle school teachers also mentioned assigning homework with the purpose of diagnosing skills and personal development (see Figure 2 ). Many teachers reported that they use homework as a tool to diagnose students’ skills. However, several recognized that they had previously defended the importance of homework to help students to evaluate their own learning (see Figure 1 ). When discussing the latter point, participants observed the need to find out about whether students had understood the content taught in class, and to decide which changes to teaching style, homework assigned, or both may be necessary.

Participant teachers at middle school in all focus groups profusely discussed the purpose of personal development when assigning homework. In fact, not many teachers at this school level mentioned this purpose as a characteristic of quality homework (it was a variant category, so it was not reported), yet it was referred to as a cornerstone in their homework practice. Reflecting on this discrepancy, middle school teachers explained in a displeased tone that their students were expected to have developed study habits and manage their school work with autonomy and responsibility. However, this “educational scenario is rare, so I feel the need to assign homework with this aim [personal development]” (P4 FG9).

Moving further in the discussion, the majority of teachers at both school levels reported to assign whole-class homework (homework designed for the whole class with no focus on special cases). “Individualized homework requires a great amount of time to be monitored” (P1 FG6), explained several participants while recalling earlier comments. Teachers justified their position referring to the impediments already mentioned (e.g., large number of students per class, number of classes from different grade levels which means preparing different lessons). Besides, teachers discussed the challenge of coping with heterogeneous classes, as one participant noted: “the class is so diverse that it is difficult to select homework tasks to address the needs of every single student. I would like to do it…but we do not live in an ideal world” (P9 FG4).

Moreover, teachers at both school levels (see Figure 2 ) reported to assign homework according to the availability of students; still, only elementary school teachers had earlier referred to the importance of this characteristic in quality homework. When teachers were asked to elaborate on this idea, they defended the need to negotiate with students about specific homework characteristics, for example, the amount of homework and submission deadline. In some classes, matching students’ requests, teachers might assign a “weekly homework pack” (P7 FG10). This option provides students with the opportunity to complete homework according to their availability (e.g., choosing some days during the week or weekend). Teachers agreed that ‘negotiation’ fosters students’ engagement and homework compliance (e.g., “I do not agree that students do homework on weekends, but if they show their wish and actually they complete it, for me that’s okay”, P7 FG10). In addition, teachers expressed worry about their students’ often heavy workload. Many students stay in school from 8.30 am to 6.30 pm and then attend extracurricular activities (e.g., soccer training, private music lessons). These activities leave students very little free time to enjoy as they wish, as the following statement suggests:

P8 FG4: Today I talked to a group of 5th-graders which play soccer after school three times a week. They told me that sometimes they study between 10.00 and 11.00 p.m. I was astonished. How is this possible? It’s clearly too much for these kids.

Finally, elementary school teachers in all focus group referred frequency and completion deadlines as characteristics of the homework they usually assign. The majority of teachers informed that they assign homework in almost every class (i.e., teachers reported to exclude tests eves of other subjects), to be handed in the following class.

The photos of the homework assignments (see some examples in Supplementary Material ) submitted by the participating teachers served to triangulate data. The analysis showed that teachers’ discourses about the characteristics of homework assigned and the homework samples are congruent, and added information about the length of homework (elementary and middle schools) and the completion deadlines (middle school) (see Figure 3 ).

Discussion and Implications for Practice and Research

Homework research have reported teachers’ perspectives on their homework practices (e.g., Brock et al., 2007 ; Danielson et al., 2011 ; Kaur, 2011 ; Bang, 2012 ; Kukliansky et al., 2014 ), however, literature lacks research on the quality of homework. This study adds to the literature by examining the perspectives of teachers from two school levels regarding quality homework. Moreover, participants described the characteristics of the homework assignments they typically assign, which triggered the discussion about the match between the characteristics of quality homework and the tasks actually assigned. While discussing these key aspects of the homework process, the current study provides valuable information which may help deepen our understanding of the different contributions of homework to students’ learning. Furthermore, findings are expected to inform teachers and school administrators’ homework practices and, hopefully, improve the quality of students’ learning.

All teachers at both school levels valued homework as an important educational tool for their teaching practice. Consistent with the literature, participants indicated practicing or reviewing the material covered in class as the main purpose of both the homework typically assigned ( Danielson et al., 2011 ; Kaur, 2011 ) and quality homework. Despite the extended use of this homework purpose by teachers, a recent study conducted with mathematics teachers found that homework with the purpose of practicing the material covered in class did not impact significantly the academic achievement of 6th-grade students; however, homework designed with the purpose of solving problems did (extension homework) ( Rosário et al., 2015 ). Interestingly, in the current study only teachers from elementary school mentioned the homework purpose “extension” as being part of quality homework, but these teachers did not report to use it in practice (at least it was not a typical category) (see Figure 2 ). Extension homework was not referenced by middle school teachers either as quality homework or as a characteristic of homework assigned. Given that middle school students are expected to master complex math skills at this level (e.g., National Research Council and Mathematics Learning Study Committee, 2001 ), this finding may help school administrators and teachers reflect on the value and benefits of homework to students learning progress.

Moreover, teachers at both school levels stressed the use of homework as a tool to help students evaluate their own learning as a characteristic of quality homework; however, this purpose was not said to be a characteristic of the homework usually assigned. If teachers do not explicitly emphasize this homework purpose to their students, they may not perceive its importance and lose opportunities to evaluate and improve their work.

In addition, elementary school teachers identified personal development as a characteristic of quality homework. However, only middle school teachers reported assigning homework aiming to promote students’ personal development, and evaluate students’ learning (which does not imply that students evaluate their own learning). These findings are important because existing literature has highlighted the role played by homework in promoting students’ autonomy and learning throughout schooling ( Rosário et al., 2009 , 2011 ; Ramdass and Zimmerman, 2011 ; Núñez et al., 2015b ).

Globally, data show a disconnect between what teachers believe to be the characteristics of quality homework and the characteristics of the homework assigned, which should be further analyzed in depth. For example, teachers reported that middle school students lack the autonomy and responsibility expected for this school level, which translates to poor homework behaviors. In fact, contrary to what they would expect, middle school teachers reported the need to promote students’ personal development (i.e., responsibility and autonomy). This finding is consistent with the decrease of students’ engagement in academic activities found in middle school (e.g., Cleary and Chen, 2009 ; Wang and Eccles, 2012 ). This scenario may present a dilemma to middle school teachers regarding the purposes of homework. On one hand, students should have homework with more demanding purposes (e.g., extension); on another hand, students need to master work habits, responsibility and autonomy, otherwise homework may be counterproductive according to the participating teachers’ perspective.

Additionally, prior research has indicated that classes assigned challenging homework demonstrated high mathematics achievement ( Trautwein et al., 2002 ; Dettmers et al., 2010 ). Moreover, the study by Zakharov et al. (2014) found that Russian high school students from basic and advanced tracks benefited differently from two types of homework (i.e., basic short-answer questions, and open-ended questions with high level of complexity). Results showed that a high proportion of basic or complex homework exercises enhanced mathematics exam performance for students in the basic track; whereas only a high proportion of complex homework exercises enhanced mathematics exam performance for students in the advanced track. In fact, for these students, a low proportion of complex homework exercises was detrimental to their achievement. These findings, together with our own, may help explain why the relationship between homework and mathematics achievement in middle school is lower than in elementary school (see Fan et al., 2017 ). Our findings suggest the need for teachers to reflect upon the importance of assigning homework to promote students’ development in elementary school, and of assigning homework with challenging purposes as students advance in schooling to foster high academic outcomes. There is evidence that even students with poor prior knowledge need assignments with some degree of difficulty to promote their achievement (see Zakharov et al., 2014 ). It is important to note, however, the need to support the autonomy of students (e.g., providing different the types of assignments, opportunities for students to express negative feelings toward tasks, answer students’ questions) to minimize the threat that difficult homework exercises may pose to students’ sense of competence; otherwise an excessively high degree of difficulty can lead to students’ disengagement (see Patall et al., 2018 ). Moreover, teachers should consider students’ interests (e.g., which contents and types of homework tasks students like) and discuss homework purposes with their students to foster their understanding of the tasks assigned and, consequently, their engagement in homework ( Xu, 2010 , 2018 ; Epstein and Van Voorhis, 2012 ; Rosário et al., 2018 ).

We also found differences between teachers’ perspectives of quality homework and their reported homework practices concerning the degree of individualization when assigning homework. Contrary to the perspectives that quality homework stresses individual needs, teachers reported to assign homework to the whole class. In spite of the educational costs associated with assigning homework adjusted to specific students or groups of students (mentioned several times by participants), research has reported benefits for students when homework assignments match their educational needs (e.g., Cooper, 2001 ; Trautwein et al., 2006a ; Zakharov et al., 2014 ). The above-mentioned study by Zakharov et al. (2014) also shed light on this topic while supporting our participants’ suggestion to assign homework with increasing level of difficulty aiming to match the variety of students’ levels of knowledge (see also Dettmers et al., 2010 ). However, teachers did not mention this idea when discussing the characteristic of homework typically assigned. Thus, school administrators may wish to consider training teachers (e.g., using mentoring, see Núñez et al., 2013 ) to help them overcome some of the obstacles faced when designing and assigning homework targeting students’ individual characteristics and learning needs.

Another interesting finding is related to the sub-theme of homework adjusted to the availability of students. This was reported while discussing homework quality (elementary school) and characteristics of homework typically assigned (elementary and middle school). Moreover, some elementary and middle school teachers explained by email the reasons why they did not assign homework in some circumstances [e.g., eves of assessment tests of other subjects, extracurricular activities, short time between classes (last class of the day and next class in the following morning)]. These teachers’ behaviors show concern for students’ well-being, which may positively influence the relationship between students and teachers. As some participants mentioned, “students value this attitude” (P1 FG5). Thus, future research may explore how homework adjusted to the availability of students may contribute to encouraging positive behaviors, emotions and outcomes of students toward their homework.

Data gathered from the photos of the assigned homework tasks allowed a detailed analysis of the length and completion deadlines of homework. Long assignments did not match elementary school teachers’ perspectives of quality homework. However, a long homework was assigned once and aimed to help students practice the material covered for the mathematics assessment test. Here, practices diverged. Some teachers assigned this homework some weeks before and others assign it in last class before the test. For this reason, the “long term” completion deadline was not a typical category, hence not reported. Future research could consider studying the impact of this homework characteristic on students’ behaviors and academic performance.

Finally, our findings show that quality homework, according to teachers’ perspectives, requires attention to a combination of several characteristics of homework. Future studies may include measures to assess characteristics of homework other than “challenge” and “selection” already investigated ( Trautwein et al., 2006b ; Dettmers et al., 2010 ; Rosário et al., 2018 ); for example, homework adjusted to the availability of students.

Strengths and Limitations of the Study

The current study analyzed the teachers’ perspectives on the characteristics of quality homework and of the homework they typically assigned. Despite the incapability to generalize data, we believe that these findings provide important insights into the characteristics that may impact a homework assignment’s effectiveness, especially at middle school level. For example, our results showed a disconnect between teachers’ perspectives about the characteristics of quality homework and the characteristics of the homework they assign. This finding is relevant and emphasizes the need to reflect on the consistency between educational discourses and educational practices. Teachers and school administrators could consider finding opportunities to reflect on this disconnect, which may also occur in other educational practices (e.g., teacher feedback, types of questions asked in class). Present data indicate that middle school teachers reported to assign homework with the major purpose of practicing and reviewing the material, but they also aim to develop students’ responsibility and autonomy; still they neglect homework with the purpose of extension which is focused on encouraging students to display an autonomous role, solve problems and transfer the contents learned (see discussion section). Current findings also highlight the challenges and dilemmas teachers face when they assign homework, which is important to address in teachers’ training. In fact, assigning quality homework, that is, homework that works, is not an easy task for teachers and our findings provide empirical data to discuss and reflect upon its implications for research and educational practice. Although our findings cannot be generalized, still they are expected to provide important clues to enhance teachers’ homework practices in different contexts and educational settings, given that homework is among the most universal educational practices in the classroom, is a topic of public debate (e.g., some arguments against homework are related to the characteristics of the assignments, and to the malpractices in using this educational tool) and an active area of research in many countries ( Fan et al., 2017 ).

Moreover, these findings have identified some of the most common obstacles teachers struggle with; such data may be useful to school administrators when designing policies and to teacher training. The administrative obstacles (e.g., large number of students per class) reported by teachers may help understand some of the discrepancies found between teachers’ definition of quality homework and their actual homework practices (e.g., degree of individualization), and also identify which problems related to homework may require intervention. Furthermore, future research could further investigate this topic by interviewing teachers, videotaping classroom activities and discussing data in order to design new avenues of homework practices.

We share the perspective of Trautwein et al. (2006b) on the importance of mapping the characteristics of homework positively associated with students’ homework behaviors. Data from this study may inform future studies analyzing these relationships, promote adaptive homework behaviors and enhance learning.

Methodologically, this research followed rigorous procedures to increase the trustworthiness of findings, improving the validity of the study (e.g., Lincoln and Guba, 1985 ) that should be accounted for. Data from two data sources (i.e., focus groups and the homework assignments photographed) were consistent, and the member checking conducted in both phases allowed the opportunity to learn that the findings of the focus group seem to accurately reflect the overall teachers’ perspectives regarding quality homework and their homework practices.

Despite the promising contributions of this study to the body of research regarding homework practices, this specific research provides an incomplete perspective of the homework process as it has only addressed the perspectives of one of the agents involved. Future research may consider analyzing students’ perspectives about the same topic and contrast data with those of teachers. Findings are expected to help us identify the homework characteristics most highly valued by students and learn about whether they match those of teachers.

Furthermore, data from homework assignments (photos) were provided by 25% of the participating teachers and for a short period of time (i.e., three weeks in one school term). Future research may consider conducting small-scale studies by collecting data from various sources of information aiming at triangulating data (e.g., analyzing homework assignments given in class, interviewing students, conducting in-class observations) at different times of the school year. Researchers should also consider conducting similar studies in different subjects to compare data and inform teachers’ training.

Finally, our participants’ description does not include data regarding the teaching methodology followed by teachers in class. However, due to the potential interference of this variable in results, future research may consider collect and report data regarding school modality and the teaching methodology followed in class.

Homework is an instructional tool that has proved to enhance students’ learning ( Cooper et al., 2006 ; Fernández-Alonso et al., 2015 ; Valle et al., 2016 ; Fan et al., 2017 ; Rosário et al., 2018 ). Still, homework is a complex process and needs to be analyzed thoroughly. For instance, when planning and designing homework, teachers need to choose a set of homework characteristics (e.g., frequency, purposes, degree of individualization, see Cooper, 2001 ; Trautwein et al., 2006b ) considering students’ attributes (e.g., Cooper, 2001 ), which may pose a daily challenge even for experienced teachers as those of the current study. Regardless of grade level, quality homework results from the balance of a set of homework characteristics, several of which were addressed by our participants. As our data suggest, teachers need time and space to reflect on their practices and design homework tasks suited for their students. To improve the quality of homework design, school administrators may consider organizing teacher training addressing theoretical models of homework assignment and related research, discussing homework characteristics and their influence on students’ homework behaviors (e.g., amount of homework completed, homework effort), and academic achievement. We believe that this training would increase teachers’ knowledge and self-efficacy beliefs to develop homework practices best suited to their students’ needs, manage work obstacles and, hopefully, assign quality homework.

Ethics Statement

This study was reviewed and approved by the ethics committee of the University of Minho. All research participants provided written informed consent in accordance with the Declaration of Helsinki.

Author Contributions

PR and TN substantially contributed to the conception and the design of the work. TN and JC were responsible for the literature search. JC, TN, AN, and TM were responsible for the acquisition, analysis, and interpretation of data for the work. PR was also in charge of technical guidance. JN made important intellectual contribution in manuscript revision. PR, JC, and TN wrote the manuscript with valuable inputs from the remaining authors. All authors agreed for all aspects of the work and approved the version to be published.

This study was conducted at Psychology Research Centre, University of Minho, and supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science through national funds and when applicable co-financed by FEDER under the PT2020 Partnership Agreement (UID/PSI/01662/2013). PR was supported by the research projects EDU2013-44062-P (MINECO) and EDU2017-82984-P (MEIC). TN was supported by a Ph.D. fellowship (SFRH/BD/80405/2011) from the Portuguese Foundation for Science and Technology (FCT).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank Fuensanta Monroy and Connor Holmes for the English editing of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00224/full#supplementary-material

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Keywords : perceived quality homework, homework characteristics, math, teachers’ perspectives, elementary school, middle school, focus group, homework samples

Citation: Rosário P, Cunha J, Nunes T, Nunes AR, Moreira T and Núñez JC (2019) “Homework Should Be…but We Do Not Live in an Ideal World”: Mathematics Teachers’ Perspectives on Quality Homework and on Homework Assigned in Elementary and Middle Schools. Front. Psychol. 10:224. doi: 10.3389/fpsyg.2019.00224

Received: 12 October 2018; Accepted: 22 January 2019; Published: 19 February 2019.

Reviewed by:

Copyright © 2019 Rosário, Cunha, Nunes, Nunes, Moreira and Núñez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Pedro Rosário, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Time Spent on Homework and Academic Achievement: A Meta-analysis Study Related to Results of TIMSS

[el tiempo dedicado a la tarea y al rendimiento académico: un estudio metaanalítico relacionado con los resultados de timss], gulnar ozyildirim akdeniz university, konyaalti, antalya, turkey, https://doi.org/10.5093/psed2021a30.

Received 31 August 2020, Accepted 24 May 2021

Homework is a common instructional technique that requires extra time, energy, and effort apart from school time. Is homework worth these investments? The study aimed to investigate whether the amount of time spent on homework had any effect on academic achievement and to determine moderators in the relationship between these two terms by using TIMSS data through the meta-analysis method. In this meta-analysis study, data obtained from 488 independent findings from 74 countries in the seven surveys of TIMSS and a sample of 429,970 students was included. The coefficient of standardized means, based on the random effect model, was used to measure the mean effect size and the Q statistic was used to determine the significance of moderator variables. This study revealed that the students spending their time on homework at medium level had effect on their academic achievement and there were some significant moderators in this relationship.

La tarea es una técnica instructiva común que requiere tiempo extra, energía y esfuerzo aparte del horario escolar. ¿Vale la pena hacer estas inversiones? El objetivo del estudio era investigar si el tiempo dedicado a la tarea tenía algún efecto en el rendimiento académico y determinar los moderadores de la relación entre estos dos términos mediante el uso de datos TIMSS a través del método de metaanálisis. En este estudio de metaanálisis se incluyeron los datos obtenidos de 488 hallazgos independientes de 74 países en las siete encuestas de TIMSS y una muestra de 429,970 estudiantes. Se utilizó el coeficiente de medias estandarizadas, basado en el modelo de efecto aleatorio, para medir el tamaño medio del efecto y el estadístico Q para determinar la significación de las variables moderadoras. El estudio reveló el hecho de que los estudiantes que dedican su tiempo a la tarea en el nivel medio tiene efecto en su rendimiento académico y hubo algunos moderadores significativos de esta relación.

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Cite this article as: Ozyildirim, G. (2022). Time Spent on Homework and Academic Achievement: A Meta-analysis Study Related to Results of TIMSS. Psicología Educativa, 28 (1) , 13 - 21. https://doi.org/10.5093/psed2021a30

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A latent profile analysis of homework time, frequency, quality, interest, and favorability: implications for homework effort, completion, and math achievement

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• Jianzhong Xu   ORCID: orcid.org/0000-0002-0269-4590 1 , 2  

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The major objectives of our study were (a) to identify student profiles according to five homework characteristics (homework time, frequency, quality, interest, and favorability) and (b) to examine their relationship with three critical variables in the homework process—homework effort, completion, and math achievement. Latent profile analysis (LPA) was used to examine a data set with 3018 8th graders in China. Based on these characteristics, five distinct profiles were identified: Profile 1 ( Low ), Profile 2 ( Moderate Time/High With Others ), Profile 3 ( Low Frequency/Moderate With Others ), Profile 4 ( Moderate Time/High Frequency/Low With Others ), and Profile 5 ( High Time and Frequency/Moderate With Others ) . Parent education was positively associated with the two healthiest profiles (Profile 2 and Profile 5). Finally, profile membership was a significant predictor of homework effort, completion, and math achievement. Specifically, our study suggests that students can work about 30 min on math homework and achieve the same results, if they work often, with high quality, fueled by interest and favorability (compared with students who spend about 110 min on math homework). Taken together, our study provided novel insights into the combination of homework characteristics that could have significant implications for homework practice and research.

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Introduction

Commonly defined as “tasks assigned to students by school teachers that are meant to be carried out during nonschool hours” (Cooper, 1989 , p. 7), homework is a prevalent instructional activity with everyday importance for many teachers, parents, and students (Fan et al., 2017 ; Dettmers et al., 2010 ). It is frequently considered an important instructional strategy to promote study habits and academic achievement (Fan et al., 2017 ; Cooper, 1989 ; Yang & Tu, 2020 ).

Homework is a “complicated thing” (Corno, 1996 ), influenced by more factors than any instructional activities (Cooper, 2007 ). One cluster of factors that has thus far attracted the most attention in the field is homework characteristics—homework time, frequency, quality, interest, and favorability (Xu et al., 2016 ; Fan et al.,  2017 ; Cooper, 1989 ; Cooper et al., 2006 ; Fernández-Alonso et al., 2015 ; Rosário et al., 2018 ; Suárez et al., 2019 ). Yet, much of the prior studies have used a variable-centered approach, ignoring the likelihood that different combinations of homework characteristics might exist and associate with homework behavior and student achievement.

Our current investigation attempts to address this gap in homework research, by taking a person-centered approach to the study of homework characteristics. A study such as this is timely, as many students find it more challenging to complete their homework assignments during the SARS-CoV-2 pandemic (e.g., spending more time doing homework yet with limited support traditionally provided by teachers; Suárez et al., in press ; Van Lancker & Parolin, 2020 ).

Homework characteristics: theoretical models and related research

Comprehensive homework models (Xu & Corno, in press ; Cooper, 1989 ; Trautwein et al., 2006 ) were developed to capture a multitude of factors in the homework process. Drawn from synthesis of homework research, Cooper ( 1989 ) developed a process model of factors that influence the effectiveness of homework. Cooper posited that homework outcomes such as homework completion and student achievement could be affected by the following groups of factors: exogenous factors (e.g., gender and parent education), assignment characteristics (e.g., amount), initial classroom factors (e.g., proposed approaches), home-community factors (e.g., parental help), and classroom follow-up (e.g., teacher feedback).

Extending Cooper’s work, Trautwein et al. ( 2006 ) developed a complementary homework model. Specifically, Trautwein et al. posited that academic achievement may be influenced by the following groups of variables: classroom learning environment, homework characteristics (e.g., length, frequency, and quality), student background (e.g., gender), parental involvement (e.g., homework assistance), student motivation (e.g., homework expectancy), and homework behavior (e.g., homework effort and completion).

Due to the linkage to the objectives of the current investigation, we focused on homework characteristics in the above two theoretical models along with related previous homework investigation. In Cooper’s model, the amount of homework assigned or time spent on homework is conceptualized as one important homework characteristic that may influence homework completion and academic achievement. Aside from homework time, Trautwein et al. ( 2006 ) incorporated two additional homework characteristics (i.e., homework frequency and homework quality) expected to influence academic achievement (Trautwein & Köller, 2003 ). Trautwein et al.’s model further pointed to the significance of homework effort in the homework process, in that homework characteristics such as homework time, frequency, and quality may influence homework effort (i.e., in addition to homework completion and academic achievement in Cooper’s model).

Much of the previous literature on homework characteristic variables emphasizes on the influence of homework time and frequency on student achievement. In their research synthesis, Cooper et al. ( 2006 ) examined the relation between homework time and student achievement. Their analysis of 69 separate correlations from 32 studies yielded a weighted average correlation of 0.24. In another research synthesis, Fan et al. ( 2017 ) examined the prior studies on the homework–achievement association in math and science, based on 61 separate correlations from 28 studies. Results showed a weighted average correlation of 0.15 between homework time and student achievement and of 0.12 between homework frequency and student achievement.

Influenced by Trautwein et al.’s model ( 2006 ), an increasing number of studies have linked homework quality to homework behavior and achievement (Xu, 2016 ; Dettmers et al., 2010 ; Rosário et al., 2018 ). Using data from 918 middle school students, Xu ( 2016 ) reported that homework quality was positively correlated with homework effort, completion, and student achievement. Similarly, involving 4265 6th graders, Rosário et al. ( 2018 ) reported that homework quality was positively associated with homework effort and performance (including homework completion and accuracy) and that homework performance was positively related to student achievement.

Moving beyond the three homework characteristics discussed above (i.e., homework time, frequency, and quality), one emerging line of research further suggests the importance of two additional homework characteristics—homework interest and favorability (Xu, 2008 ; Xu & Corno, 1998 ; Cooper et al., 1998 ; Rosário et al., 2018 ; Suárez et al., 2019 ). Cooper et al. ( 1998 ) related student attitudes to homework completion and student achievement. In their study, student attitudes contained both interest items (e.g., the extent to which students like homework) and belief items (e.g., the extent to which homework helps them learn). For students in grades 6–12, their attitudes was positively related to homework completion, which in turn was positively associated with student achievement. Involving secondary school students, Xu ( 2011 ) examined empirical models of factors to predict homework completion and found that homework interest was positively associated with homework completion after controlling for other important variables (e.g., teacher feedback). Likewise, Suárez et al. ( 2019 ) reported that homework interest was positively associated with homework behavior engagement (including homework completion), which in turn was positively related to student achievement.

Homework favorability can be defined as students’ favorite ratings of homework compared with subjective experiences with other competing activities during after-school hours (e.g., texting and social networking; Xu et al., 2020 ). It is initially informed by qualitative research with elementary and middle school students (Xu & Corno, 1998 ; Xu & Yuan,  2003 ) and followed by cross-sectional and longitudinal studies with secondary school students (e.g., Xu, 2008 ; Xu et al., 2020 ). Relevant results indicated that homework favorability had a large positive correlation with homework interest (0.65 ≤  r  ≤ 0.72) yet empirically distinguishable from homework interest (Xu, 2008 ; Xu et al., 2016 ) and that homework favorability and homework interest were positively reciprocally related (Xu et al., 2020 ). Additionally, as students use learning strategies more in favorite than least favorite courses, and as they are more likely to obtain higher achievement in favorite courses (Ben-Eliyahu & Linnenbrink-Garcia, 2015 ), there is a need to include homework favorability as another important homework characteristic in our study.

This line of literature further suggests that these homework characteristics (time, frequency, quality, interest, and favorability) are significantly correlated with homework behavior (effort and completion) and student achievement. Even though a variable-centered perspective offers useful information about the linkages between each homework characteristic and homework behavior (or student achievement), it overlooks the likelihood that (a) different combinations of homework characteristic profiles may emerge, and (b) these profiles may associate with differences in homework behavior and student achievement.

Our justification for studying the possible combinations of homework characteristics is further alluded to by recent studies that have attempted to identify homework profiles based on homework time and homework effort (Flunger et al., 2015 , 2017 ; Shin & Sohn, 2019 ) or based on homework time and homework time management (Valle et al., 2019 ). As these studies have limited to one homework characteristic (i.e., homework time), and as “homework behavior cannot be fully captured by focusing solely on homework time” (Flunger et al., 2017 , p. 2), it would be important to identify student profiles that draw from a broad range of homework characteristics as discussed above in our current investigation.

The present study

The first objective was to investigate student profiles according to the possible combinations of the five homework characteristics—homework time, frequency, quality, interest, and favorability. Specifically, the present study focused on eighth graders with their mathematics homework for several reasons. First, math is a highly valued yet challenging subject across many countries (León et al., 2015 ; Ramirez et al., 2018 ). Additionally, teachers often assign more homework in math than in other school subjects (Xu, 2015 ; Bempechat, 2019 ). Finally, math becomes increasingly more complex and abstract at the eighth grade level, posing significant challenges for students to learn math concepts and follow through math assignments (Xu et al., 2022 ; Lee, 2009 ).

Since our study is the first to apply a person-centered approach to a broad range of homework characteristics, we have no specific hypothesis concerning the number of homework characteristic profiles that would emerge. On the other hand, congruent with previous studies adopting a person-centered approach drawing from homework time and homework effort/homework time management (Flunger et al., 2015 , 2017 ; Shin & Sohn, 2019 ; Valle et al., 2019 ), several profiles may emerge, including a profile containing high homework time, a profile containing low homework time, and profiles with varying degrees of homework time and other homework characteristics.

Although it is not the focus in our study, student gender and parent education are considered key background variables in homework models (Xu & Corno, in press ; Cooper, 1989 ; Trautwein et al., 2006 ), thereby having important implications for research and practice (Cooper et al., 2000 ; Froiland, 2021 ). Parents with higher education, for example, “are more likely to know something about what the children are being taught and thus able to help with homework” (Davis-Kean, 2005 , p. 303). As we do not have information on how gender and parent education may influence the classification of students into profiles, it would be important to control for these two variables (i.e., incorporating them as covariates) in latent profile analysis (LPA).

The second objective was to examine how profiles related to critical variables in the homework models (Xu & Corno, in press ; Cooper, 1989 ; Trautwein et al., 2006 ), including homework effort, completion, and math achievement. Congruent with previous studies in English-speaking, European, Asian, and Latin American countries (Xu, 2016 ; Fan et al., 2017 ; Ben-Eliyahu & Linnenbrink-Garcia, 2015 ; Cooper et al., 1998 , 2006 ; Fernández-Alonso et al., 2019 ; Flunger et al., 2017 ; Suárez et al., 2019 ), we expect that a profile with a high level of homework characteristics (e.g., quality and frequency) would expend more effort, complete more homework, and score higher on math achievement than a profile with a low level of homework characteristics.

Participants and procedures

The participants were 3018 8th graders (96 classes; 100% Han nationality) from several regions in China, including southeastern, southwestern, and central. Among these participants, 54.4% identified as male and 45.6% as female. Their mean age was 13.7 years ( SD  = 0.4). Education level was 11.4 years and 10.6 years for fathers and mothers. The overall student participation rate was 88.7%. A test of mean differences between participants ( n  = 3018) and non-participants ( n  = 383) indicated that there were no significant differences between these two groups regarding student gender ( p  = 0.431), mothers’ education ( p  = 0.205), and fathers’ education ( p  = 0.331).

Regarding homework practices, 76.9% participants did math homework 4 days or more a week. They spent a mean of 34 min ( SD  = 25) on math homework daily. These math homework practices are generally congruent with related studies in China (Xu et al., 2017 ).

We sought and gained permissions from schools and parents for their children to participate in our investigation. Informed consent was taken from students and parents according to the tenets of Helsinki Declaration. Specifically, students were informed that the purpose of the investigation is “to learn more about how you approach math homework so that teachers and your family can better help you.” They were further assured that their responses were confidential and they might not answer certain items or withdraw from participation anytime. The data were collected using paper–pencil questionnaire in classrooms during normal school time at the end of October 2017. Math teachers were asked to step out of their rooms while students completed the measures.

• Homework time

Students were asked about the following question: “On a typical day, how long does it usually take you to finish your math homework?” Responses included 1 ( none ), 2 ( 1–20 min ), 3 ( 21–40 min ), 4 ( 41–60 min ), 5 ( 61–80 min ), 6 ( 81–100 min ), 7 ( 101 to 120 min ), and 8 ( more than 120 min ). In line with previous work (Xu, 2010 ; Cooper et al., 1998 ), a variable relating to homework time was created by transforming each response into its midpoint (e.g., 2 = 10.5 min).

• Homework frequency

Based on extant literature (Fan et al., 2017 ; Fernández-Alonso et al., 2015 ), students were asked about the following question: “During a typical week, how often do you get math homework?” Responses included 1 ( none ), 2 ( 1 day a week ), 3 ( 2 days a week ), 4 ( 3 days a week ), 5 ( 4 days a week ), and 6 ( 5 or more days a week ).

• Homework quality

It consisted of four items to assess student perceptions of quality of homework (Xu, 2016 ). Specifically, it assessed how well math assignments were selected, prepared, and integrated into math classes (e.g., “Our math homework assignments really help us to understand our math lessons”; α  = 0.87; ω  = 0.87). Responses ranged from 1 ( strongly disagree ) to 4 ( strongly agree ).

• Homework interest

It contained four items to assess student interest in math homework, informed by existing literature on intrinsic motivation and interest (Wigfield & Cambria, 2010 ) and homework studies (Xu et al., 2016 ; Cooper et al., 1998 ). It assessed the extent to which students enjoyed doing math homework (e.g., “I look forward to math homework”; α  = 0.91; ω  = 0.91). Responses varied from 1 ( strongly disagree ) to 5 ( strongly agree ).

• Homework favorability

It consisted of three items to measure participants’ favorability of math assignments (Xu, 2008 ; Xu et al., 2020 ). It tapped into students’ favorite ratings of math assignments, compared with their experience (e.g., motivation, attention, and moods) in other after-school activities (e.g., “My motivation to do math homework is _____ other school activities”; α  = 0.83; ω  = 0.83). Responses varied from 1 ( much lower than ) to 5 ( much higher than ).

Homework effort

Three items assessed students’ homework effort, drawn from prior studies (Xu, 2018 ; Trautwein et al., 2006 ). These items tapped into students’ initiatives to follow through on math assignments (e.g., “I always try to finish my math assignments”; α  = 0.81; ω  = 0.82). Response options varied from 1 ( strongly disagree ) to 4 ( strongly agree ).

Parent education

Students were asked about the education levels of their mothers and fathers. Response choices varied from elementary school (coded 6 years) to graduate degree (coded 19 years). As education level of mothers and fathers were highly related for our participants ( r  = 0.76, p  < 001). A variable to represent level of parent education was developed by taking the mean of education level of each parent.

Homework completion

Based on related studies (Xu et al., 2019 ; Cooper et al., 2006 ), students were asked about one item regarding homework completion: “Some students often complete math homework on time, others rarely do. How much of your assigned math homework do you usually complete?” Responses were 1 ( none ), 2 ( some ), 3 ( about half ), 4 ( most ), and 5 ( all ). Regarding this measure’s concurrent and predictive evidence, Xu ( 2017 ) found that, consistent with theoretical prediction, it was positively correlated with homework expectancy, effort, and student achievement.

Math achievement

Standardized math achievement was assessed nearly 8 months following the administration of the measures. The assessment was aligned with national curriculum (Li & Li, 2018 ) to measure knowledge and skills at the grade level (e.g., fraction, linear function, triangle, parallelogram, parallelogram, quadratic radical, and data analysis). It contained multiple-choice and short-answer questions, and students were given 120 min to work on the test. The reliability estimate (coefficient alpha) was 0.88.

Data analyses

LPA was used to investigate student profiles according to five homework characteristics—homework time, frequency, quality, interest, and favorability. All analyses were conducted with robust maximum likelihood estimator in M plus 7.2. As students were nested in classes, the standard errors were adjusted by using the command “type is complex” in M plus . Our study contained very few missing data, ranging from 0.00 to 2.12% ( M  = 0.71%). We applied full information maximum likelihood (FIML) to handle with missing data, as FILM is found to produce unbiased estimates (Marsh et al., 2016 ).

The decision for selecting the optimal number of profiles was based on multiple fit indices, interpretability, and parsimony (Xu, 2022 ; Valle et al., 2019 ). These indices include the Akaike information criterion (AIC), Schwartz’s Bayesian information criterion (BIC), sample-size adjusted BIC (SSA-BIC), and Lo-Mendell-Rubin adjusted likelihood ratio test (LMRT).

Lower values on AIC, BIC, and SSA-BIC represent better fit. A significant p value for LMRT means that a k-profile model yields better fit to the data than a k-1-profile model. We generated elbow plots of the AIC, BIC, and SSA-BIC to provide a graphic summary of these indices to facilitate the model selection. The profile at the point with which the slope of the plots noticeably flattens is considered additional indicator of an appropriate solution (Morin & Marsh, 2015 ). Entropy value (varying from 0 to 1) measures classification uncertainty (> 0.70 representing adequate classification accuracy; Jung & Wickrama, 2008 ).

We applied the 3-step procedure to perform the covariate and distal outcome testing (Asparouhov & Muthén, 2014 ). In step 1, the LPA was conducted with only the five homework characteristics. In step 2, a “most likely class” variable was created based on the LPA. In step 3, the auxiliary variables were incorporated for investigation. In particular, in a multinomial logistic regression model, we incorporated two covariates—gender and parent education—as predictors of latent profiles using the R3STEP procedure. We then used the DU3STEP procedure (assuming unequal variances and means in each profile), specifying homework effort, completion, and math achievement as three distal outcome variables. A Wald chi-square test was used to assess the equality of means of the three distal outcome variables across latent profiles.

Descriptive statistics

Table 1 provides descriptive statistics. Overall, low to positive moderate relations were found among the measures used in the LPA.

Identification of student profiles

Table 2 displays the fit of profile models. As additional profiles were extracted, the AIC, BIC, and SSA-BIC kept decreasing, and the LMRT continued to show significant differences. Yet, slopes of the elbow plots seemed to flatten around the five-profile model (see Fig.  1 ). Even though the five-profile model had one profile less than 5% of the cases (4.47%), compared with the four-profile model (Fig.  3 ), this profile exhibited rather distinctive information regarding homework time (students in this profile spending 110 min on math homework, over 3 standard deviations above the mean; see Table 6 and Fig.  4 ).

Elbow plots for AIC, BIC, and SSA-BIC

The three-profile model (Fig.  2 ) had the highest entropy (0.935) among all these models, and it included one similar profile (i.e., with one profile of students spending 108 min on math homework). Yet, as shown in this model, students in three different profiles did not show much differences in homework quality ( z  =  − 0.52 to 0.12), homework interest ( z  =  − 0.37 to 0.08), and homework favorability ( z  =  − 0.30 to 0.16). This indicates that, among others, the students in the largest profile in the three-profile model ( n  = 2369; 78.5%) can be further classified into different profiles. This information, along with our previous discussion relating to the elbow plots, suggested that the five-profile solution seemed to be the optimal choice for our study.

Homework characteristics: three-profile model

Regarding the classification accuracy of our five-profile model (Table 2 ), the entropy was 0.848, thus having a high level of entropy. Table 3 presents the classification accuracy of the five-profile model and the number of students in the five profiles. The table’s main diagonal displays the coefficients relating to each profile to which students were assigned.

Five student profiles of homework characteristics

Table 4 contains the mean scores of students assigned to the five profiles. Figure  4 presents a graphic depiction of the profiles using z-scores. Profile 1 included 6.7% of the students ( n  = 202) and could be referred to Low due to the low mean scores across all five homework characteristics ( z  =  − 0.41 to − 2.72). Profile 2 consisted of 51.7% of the cases ( n  = 1559) and could be referred to Moderate Time/High With Others because this profile had high mean scores on four homework characteristics ( z  = 0.38 to 0.49) and homework time was near the overall mean ( z  =  − 0.10). Profile 3 included 16.3% of the cases ( n  = 491) and could be referred to Low Frequency/Moderate With Others because homework frequency was more than one standard deviation lower than the overall mean ( z  =  − 1.21) and the means on the other four homework characteristics were near the overall means ( z  =  − 0.07 to − 0.23). Profile 4 included 20.9% of the cases ( n  = 631) and could be referred to Moderate Time/High Frequency/Low With Others because this profile had high mean on homework frequency ( z  = 0.46), moderate mean on homework time ( z  =  − 0.21), and low means on the other three characteristics ( z  =  − 0.54 to − 0.98). Profile 5 consisted of 4.5% of the cases ( n  = 135) and could be referred to High Time and Frequency/Moderate With Others because they had high means with homework time and frequency ( z  = 0.47 to 3.09) and the mean scores on the other three characteristics were near the overall means ( z  =  − 0.07 to 0.21).

Homework characteristics: four-profile model

Homework characteristics: five-profile model

Multinomial logistic regression results by gender and parent education

Table 5 presents multinomial logistic regression results by gender and parent education. Out of the ten comparisons among the five empirically deprived profiles, we found insignificant differences among any of these comparisons by gender. These findings indicated that gender was not significantly associated with profile membership.

As also shown in Table 5 , students with higher parental education were less likely to be in Profile 1 ( Low ; b  =  − 0.16, SE = 0.04, p  < 0.001, OR = 0.85), in Profile 3 ( Low Frequency/Moderate With Others ; b  =  − 0.13, SE = 0.03, p  < 0.001, OR = 0.88), and in Profile 4 ( Moderate Time/High Frequency/Low With Others ; b  =  − 0.08, SE = 0.03, p  = 0.001, OR = 0.92) in reference to Profile 2 ( Moderate Time/High With Others ). Additionally, students with higher parental education were more likely to be in Profile 5 ( High Time and Frequency/Moderate With Others ) than in Profile 1 ( Low ; b  = 0.12, SE = 0.06, p  = 0.027, OR = 1.13) and in Profile 3 ( Low Frequency/Moderate With Others ; b  = 0.09, SE = 0.05, p  = 0.046, OR = 1.09).

Differences among profiles on the distal outcomes

We investigated equality of the means on the distal outcomes (i.e., homework effort, completion, and math achievement) across profiles. Table 6 shows the means of the distal outcomes across the profiles. Table 7 includes chi-square tests of pairwise comparisons between the profiles.

Our findings revealed that profile membership was significantly related to homework effort, completion, and math achievement. The effect size was of medium magnitude for homework effort ( d  = 0.55) and homework completion ( d  = 0.42) and between medium and large for math achievement ( d  = 0.70). Concerning homework effort, Profile 2 and Profile 5 had higher scores than Profile 4 and Profile 3, which in turn had significantly higher scores than Profile 1. This pattern of results held for homework completion, except that Profile 2 had higher scores than Profile 5 and that there were no significant differences among Profile 3, Profile 4, and Profile 5. Finally, this pattern of results held for math achievement, except that Profile 4 had higher scores than Profile 3. Taken together, these findings provided additional empirical support for the validity of the five-profile model.

Summary of findings

The present study makes a significant contribution in understanding homework characteristics by applying a person-centered approach to generate profiles of homework time, frequency, quality, interest, and favorability. Our results provide clear empirical support for meaningful differences in homework characteristics between subgroups of students, as the findings revealed five distinct homework characteristics profiles, as parent education was significantly related to profile membership, and as profile membership was a significant predictor of homework effort, completion, and math achievement.

To sum up, we identified different combinations of five homework characteristics as meaningful profiles relative to key homework behavior and outcomes based on comprehensive homework models (Xu & Corno, in press ; Cooper, 1989 ; Trautwein et al., 2006 ) and related prior studies (e.g., Xu et al., 2016 ; Rosário et al., 2018 ; Suárez et al., 2019 ). Particularly, findings revealed these homework characteristics profiles: Profile 1 ( Low ; 6.7%), Profile 2 ( Moderate Time/High With Others ; 51.7%), Profile 3 ( Low Frequency/Moderate With Others ; 16.3%), Profile 4 ( Moderate Time/High Frequency/Low With Others ; 20.9%), and Profile 5 ( High Time and Frequency/Moderate With Others ; 4.5%). As can be seen from the above five profiles, a vast majority of students in our study (93.3%) had uneven profiles on homework characteristics, as marked by relatively high scores on some homework characteristics and low scores on others. This pattern of results suggests that these homework characteristics (time, frequency, quality, interest, and favorability) provide meaningful profile differentiation.

Interpretation

Because this is the first investigation that applied the LPA to a broad spectrum of homework characteristics, we have no specific hypothesis concerning the number of homework characteristics profiles that would exist. On the other hand, in line with our general expectation as informed by prior studies (Flunger et al., 2015 , 2017 ; Shin & Sohn, 2019 ; Valle et al., 2019 ), we identified profiles containing high homework time (i.e., High Time and Frequency/Moderate With Others ), low homework time (i.e., Low ), and moderate homework time (i.e., Moderate Time/High With Others, Low Frequency/Moderate With Others, Moderate Time/High Frequency/Low With Others ).

Following that, we examined whether gender and parent education would be associated with profile membership, something that has not been tapped into in prior homework research. The results that students with higher parent education were more likely to be in two healthiest profiles (Profile 2 and Profile 5) are congruent with previous studies. For example, Froiland and Davison ( 2016 ) reported that parent education predicted long-term math achievement via parent expectations, student intrinsic motivation to learn, and engaging with challenging courses over 3 years.

We further examined differences among these profiles relating to homework effort, completion, and student achievement. Congruent with our general expectation based on prior research (Xu, 2016 ; Fan et al., 2017 ; Ben-Eliyahu & Linnenbrink-Garcia, 2015 ; Flunger et al., 2017 ; Suárez et al., 2019 ), we found that students in Profile 2 (i.e., Moderate Time/High With Others ) exerted more homework effort, completed more homework, and obtained higher scores on math achievement test than students in Profile 1 (i.e., Low ). Given these results, the distribution of students among these five profiles appears optimal in that the least desirable profile (i.e., Low ) had the least percentage of students (6.7%) and that the most desirable profile (i.e., Moderate Time/High With Others ) had the most percentage of students (51.7%).

It is worth noting that, along with students in Profile 5 (i.e., Moderate Time/High With Others , a small group of students (4.5%) in Profile 5 (i.e., High Time and Frequency/Moderate With Others ) had comparable means in two out of the three distal outcomes (i.e., homework effort and math achievement; Table 6 ). This implies that, for students in this profile, high homework time may compensate for a moderate level of homework quality, interest, and favorability. For a large group of students (51.7%) in Moderate Time/High With Others , this also suggests that a high level of homework frequency, quality, interest, and favorability may compensate for a moderate level of homework time—one possible explanation that we did not find a profile with a high level of homework characteristics across homework time, frequency, quality, interest, and favorability. Given there appeared to be a very large difference between moderate homework time ( M  = 31.83 min; SD  = 0.51) and high homework time ( M  = 110.33 min; SD  = 2.12) in the above two profiles (Profiles 2 and 5; see Table 4 ), it suggests that one can accomplish more in less time, in part due to greater attention, engagement, and flow when autonomously motivated (e.g., fueled by interest and favorability). Consequently, it would be more beneficial and cost-effective for teachers to design math homework assignments of high frequency, quality, interest, and favorability rather than high quantity.

Regarding students in Profile 3 ( Low Frequency/Moderate With Others ) and Profile 4 ( Moderate Time/High Frequency/Low With Others ), it appeared that both two profiles functioned quite comparably; they had similar means in two out of three distal outcomes (homework effort and completion) and with math achievement favoring Profile 4. This suggests that high homework frequency may compensate for moderate homework time and for low quality, interest, and favorability, whereas low homework frequency may undermine a moderate level of homework time, quality, interest, and favorability (Fig.  4 ).

Previous studies adopting a variable-centered perspective find that homework frequency (in comparison with homework time) plays a more significant role in student achievement (e.g., Fernández-Alonso et al., 2015 ; Trautwein, 2007 ). Applying a person-centered approach, our present investigation offers novel insights into one possible explanation for this finding in that high homework frequency may compensate for moderate homework time and for low homework quality, interest, and favorability (Profile 4), whereas low homework frequency may undermine moderate homework time, quality, interest, and favorability (Profile 3). On the other hand, high homework time in Profile 5 ( High Time and Frequency/Moderate With Others ) may not necessarily lead to higher student achievement as compared with moderate homework time in Profile 2 ( Moderate Time/High With Others ).

Recent research indicates the important role of homework quality, interest, and favorability in homework behavior and student achievement (Xu, 2008 , 2016 ; Cooper et al., 1998 ; Fernández-Alonso et al., 2015 ; Rosário et al., 2018 ; Suárez et al., 2019 ). Instead of focusing on the contribution of each of these separate homework characteristics to homework behavior and student achievement, we examined the likelihood that distinct combinations of homework characteristic profiles may emerge and relate differences in homework behavior and student achievement. Our results relating to the five profiles indicated that homework quality, interest, and favorability tended to function together, and that they exerted a powerful and positive influence on homework behavior and student achievement (Table 6 ). This is vividly illustrated in Fig.  4 relating to the two largest profiles— Moderate Time/High With Others (Profile 2; 51.1% of the sample) and Moderate Time / High Frequency/Low With Others (Profile 4; 20.1%)—in which both profiles had a moderate level homework time and a high level homework frequency. Yet, students in a profile with a high level of homework quality, interest, and favorability ( Moderate Time/High With Others ) were more likely to exert homework effort, to complete more homework, and score higher in math achievement than students in a profile with a low level of homework quality, interest, and favorability ( Moderate Time / High Frequency/Low With Others ).

Implications for practice

Provided the most desirable profile is Profile 2 ( Moderate Time/High With Others ) in that a high level of homework frequency, quality, interest, and favorability may compensate for a moderate level of homework time, it would be beneficial to put more emphasis on homework frequency, quality, interest, and favorability when designing homework assignments. First, it would be beneficial to assign more frequent and high quality homework. Specifically, consistent with previous research on autonomy supportive teaching in math (e.g., promoting intrinsic motivation to learn math; Froiland et al., 2016 ), it would be beneficial for teachers to make close linkage between math homework assignments and math materials covered in the lessons and to help students see and understand this linkage from their perspectives. It would also be helpful to encourage students to share their perspectives on what constitutes high quality homework assignments, which could provide teachers with a better understanding of how to design and modify homework assignments according to the needs, concerns, and expectations of their students.

Furthermore, it would be important to pay close attention to student interest when teachers design homework assignments (e.g., content interest and activity interest), in line with the call from researchers over the last two decades (Corno & Xu, 2004 ; Xu et al.,  2020 ; Epstein & Van Voorhis, 2001 ). At the same time, it would be equally important to pay close attention to students’ homework favorability when they do homework during after-school hours, particularly as (a) homework is frequently viewed by students as one of the least favorable activities in their life (e.g., compared with schoolwork, maintenance, or leisure activities; Xu et al., 2016 ; Verma et al., 2002 ) and as (b) there are positive reciprocal influences between homework favorability and interest (Xu et al., 2020 ). Because both homework and other attractive activities often occur in home settings, parents are in a prime position to assist students develop a more favorable attitude towards homework. Consistent with research-based intervention studies (e.g., promoting homework autonomous motivation and engagement; Froiland, 2021 ; Moè et al, 2018 ), it would be especially helpful for parents to help students to develop a more favorable approach towards math homework, by encouraging them to take homework initiatives such as managing time spending on homework and its attractive alternatives. “If adolescents realize that they still have opportunities for other attractive activities during the week, they may be less conflicted and sidetracked by thoughts of competing activities while doing daily homework, thereby viewing homework tasks in a less unfavorable light” (Xu, 2008 , pp. 1199–1200).

Given our findings regarding five distinct profiles and their differential linkages to homework effort, completion, and student achievement, teachers need to pay close attention to the specific needs of students in each profile when approaching homework. In particular, teachers need to devote more close attention to students in Profile 1 (i.e., Low ) across these homework characteristics (time, frequency, quality, interest, and favorability). Additionally, whereas teachers may want to pay more attention to homework frequency for students in Profile 3 ( Low Frequency/Moderate With Others ), it would be more beneficial to devote special attention to homework quality, interest, and favorability for students in Profile 4 ( Moderate Time/High Frequency/Low With Others ). Furthermore, the above recommendations are applicable to both boys and girls, given our result that gender was not significantly related to profile membership. On the other hand, as students with higher parent education were more likely to be in the more desirable profiles (i.e., Moderate Time/High With Others ), it would be helpful for teachers to play more close attention to students with lower parent education (e.g., relating to homework frequency, quality, and interest).

Limitations and further investigation

Several possible limitations need to be acknowledged when interpreting our results. First, the current investigation is limited to a cross-sectional analysis. Although math achievement was assessed approximately 8 months later, we do not have data with repeated measures of the five homework characteristics. Second, certain homework characteristics (e.g., homework frequency) included in the PLA may also function as class variables. Hence, it is likely that the selected profile model, a consequence of the LPA at the student level, is not fully replicated if these variables are considered both at the student and class levels. Third, although our current study incorporated gender and parent education as covariates, it would be beneficial to study other important covariates (e.g., student ability or prior achievement) in further investigation.

Because this is the first investigation that used the LPA to examine a broad range of homework characteristics (time, frequency, quality, interest, and favorability), it would be informative to replicate our study in diverse settings. In particular, it would be beneficial to carry out a study such as this in cross-cultural settings, as some homework characteristics such as homework time and interest may be shaped by cultural differences (Xu et al., 2016 ). In addition, it would be beneficial to pursue this line of research at the elementary and high school levels, as the effect of homework on academic achievement can be moderated by school level (Fan et al., 2017 ; Cooper et al., 2006 ). Furthermore, as certain homework characteristics such as homework frequency may play a more important role in math achievement (e.g., short and frequent assignments rather than few and long assignments; Fan et al., 2017 ; Cooper, 2007 ), it would be vital to replicate our study in other achievement domains.

Moreover, as parent education was associated with healthiest profiles in our sample, as the quality of parental homework involvement (e.g., autonomy support) plays a more vital role in student achievement (Xu et al., 2017 ; Dettmers et al., 2019 ; Moroni et al., 2015 ), there is a need to link the quality of parental homework involvement to students’ homework profiles in future investigation (e.g., to include both autonomy support and parent education as covariates). This line of investigation is especially significant, as (a) recent studies have identified two major issues relating to homework during the SARS-CoV-2 pandemic—the ambiguity in homework assignments and the decrease of student interest in homework (Cui et al., 2021 ; Zaccoletti et al., 2020 )—and as (b) the role of parental homework involvement has become increasingly important during the pandemic (e.g., parental support and supervision; Suárez et al., in press ; Xia, 2020 ). Finally, given our findings regarding the two largest profiles (i.e., students in Moderate Time/High With Others putting forth more effort, completing more homework, and scoring higher in math than students in Moderate Time / High Frequency/Low With Others ), it would be intriguing to conduct qualitative research with students in these two profiles in particular, to better understand their perspectives concerning how homework quality, interest, and favorability function along with homework frequency and time.

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Xu, J. (2022). A profile analysis of online assignment motivation: Combining achievement goal and expectancy-value perspectives. Computers & Education, 177 , 104367. https://doi.org/10.1016/j.compedu.2021.104367

Xu, J., & Corno, L. (in press). Extending a model of homework: A multilevel analysis with Chinese middle school students. Metacognition and Learning . https://doi.org/10.1007/s11409-022-09296-w

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Fan, H., Xu, J., Cai, Z., He, J., & Fan, X. (2017). Homework and students' achievement in math and science: A 30-year meta-analysis, 1986-2015. Educational Research Review, 20 , 35−54. https://doi.org/10.1016/j.edurev.2016.11.003

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Xu, J., Du, J., Wang, C., Liu, F., Huang, B., Zhang, M., & Xie, J. (2020). Intrinsic motivation, favorability, time management, and achievement: A cross-lagged panel analysis. Learning and Motivation, 72 , 101677. https://doi.org/10.1016/j.lmot.2020.101677

Xu, J. (2008). Models of secondary students’ interest in homework: A multilevel analysis. American Educational Research Journal, 45 , 1180–1205. https://doi.org/10.3102/0002831208323276

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Jianzhong Xu. Department of Counseling, Educational Psychology, and Foundations, Mississippi State University, P.O. Box 9727, Mississippi State, MS, 39762, USA. Faculty of Education, University of Macau, Macau, China. Email: [email protected].

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Teaching and learning in the school, home, and online settings, in home-school relationships, and in partnerships with families from diverse cultural backgrounds.

Most relevant publications in the field of Psychology of Education:

Fan, H., Xu, J., Cai, Z., He, J., & Fan, X. (2017). Homework and students' achievement in math and science: A 30-year meta-analysis, 1986-2015. Educational Research Review , 20 , 35-54. https://doi.org/10.1016/j.edurev.2016.11.003 .

Xu, J. (2008). Models of secondary students’ interest in homework: A multilevel analysis. American Educational Research Journal , 45 , 1180-1205. https://doi.org/10.3102/0002831208323276 .

Xu, J. (2022). A profile analysis of online assignment motivation: Combining achievement goal and expectancy-value perspectives. Computers & Education , 177 , 104367. https://doi.org/10.1016/j.compedu.2021.104367 .

Xu, J. (2018). Reciprocal effects of homework self-concept, interest, effort, and math achievement. Contemporary Educational Psychology , 55 , 42-52. https://doi.org/10.1016/j.cedpsych.2018.09.002 .

Xu, J. (2021). Homework goal orientation, interest, and achievement: Testing models of reciprocal effects. European Journal of Psychology of Education , 36 (2), 359-378. https://doi.org/10.1007/s10212-020-00472-7 .

Xu, J., Du, J., Cunha, J., & Rosário, P. (2021). Student perceptions of homework quality, autonomy support, effort, and math achievement: Testing models of reciprocal effects. Teaching and Teacher Education , 108 , 103508. https://doi.org/10.1016/j.tate.2021.103508 .

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Xu, J. A latent profile analysis of homework time, frequency, quality, interest, and favorability: implications for homework effort, completion, and math achievement. Eur J Psychol Educ 38 , 751–775 (2023). https://doi.org/10.1007/s10212-022-00627-8

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Quantitative Study on the Usefulness of Homework in Primary Education

• Horaţiu CATALANO PhD. associate professor, Babes Bolyai University, Cluj Napoca, Romania, Mihail Kogălniceanu 1 Street, Cluj-Napoca 400084, Tel: 0040744790372.
• Cristina CATALANO PhD. student, Babes Bolyai University, Cluj Napoca, Romania, Mihail Kogălniceanu 1 Street, Cluj-Napoca 400084, Tel: 0040745898083.

Homework is the final stage of the traditional lesson of knowledge transfer and assimilation defined as a task set by teachers to give students the opportunity to study outside of classroom lessons. Although there are persons who criticize homework, in school practice these are seen as facilitators of learning and achieving school performance by most teachers and parents. In this study we aim to analyze the advantages and limitations of homework, based on questionnaires survey that measure teachers' perception of the importance, volume, typology, purposes, degree of difficulty, time spent and parental involvement of homework in primary education. We considered significant for this study our own didactic experiences, peer group discussions and the studies that focus on the positive and negative influences produced by homework on academic performance and school results of primary school pupils.

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2018, LUMEN Proceedings

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NIKOS CHANIOTAKIS

By reviewing the literature about homework we come to realize that homework is very often the epicentre of interest in education science and educational policy, while also drawing attention of the mass media. This situation has led to the division of two “camps”: on the one side are those who believe in the value and importance of homework, while on the other, there are those who reject homework and propose its integration in everyday schoolwork. In this study we investigated the conditions under which elementary school teachers assign and appraise the homework of their students. We also tried to find the reasons and the consequences that (could) explain and influ-ence the attitude and practices of teachers and students in this field. Moving in this direction we used on the one side, the method of observation in the school classroom and on the other side we used the method of interviewing the pupils and handing out questionnaires to teachers, so that we could form a more complete picture. The obser-vation was conducted in 52 classrooms of elementary schools in Greece (in a total of 104 teaching hours) and the interviews took place with 260 pupils, while the ques-tionnaires were answered by all of the teachers of these classrooms. We conclude that the practices of teachers concerning homework are not in accordance with the modern pedagogic opinion and are rather moved in accordance to a traditional picture. The findings of this study do not differ a lot from other research on an international level and the problems seem common.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.