classroom environment research paper

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• Published: 03 April 2023

The effect of classroom environment on literacy development

• Gary Rance 1 ,
• Richard C. Dowell 1 &
• Dani Tomlin 1  

npj Science of Learning volume  8 , Article number:  9 ( 2023 ) Cite this article

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The physical characteristics of a child’s learning environment can affect health, wellbeing and educational progress. Here we investigate the effect of classroom setting on academic progress in 7–10-year-old students comparing reading development in “open-plan” (multiple class groups located within one physical space) and “enclosed-plan” (one class group per space) environments. All learning conditions (class group, teaching personnel, etc.) were held constant throughout, while physical environment was alternated term-by-term using a portable, sound-treated dividing wall. One hundred and ninety-six students underwent academic, cognitive and auditory assessment at baseline and 146 of these were available for repeat assessment at the completion of 3 school terms, allowing within-child changes across an academic year to be calculated. Reading fluency development (change in words read-per-minute) was greater for the enclosed-classroom phases ( P  < 0.001; 95%CI 3.7, 10.0) and the children who showed the greatest condition difference (i.e. slower rate of development in the open-plan) were those with the worst speech perception in noise and/or poorest attention skills. These findings highlight the important role classroom setting plays in the academic development of young students.

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The move away from didactic teaching pedagogies for Primary School-aged children in the 1960s and 1970s led to the implementation of “open-plan” learning environments in schools around the world. The potential advantages of this classroom configuration (where multiple grades are located within a single physical space) are that they may create a less authoritarian environment 1 and support a greater range of learning methodologies and group sizes 2 , 3 . High levels of background noise and lack of acoustic privacy (created by greater numbers of students engaged in a variety of activities) within the same physical space have, however, been consistently identified by teachers and students as undesirable aspects of open-plan settings 4 . Furthermore, studies of these classroom spaces over the past four decades have consistently suggested that intrusive noise from adjacent class bases reduce speech intelligibility and increase distraction 4 . These in turn, create a significant educational risk as children spend much of their school time (45–60%) actively listening 5 and it is crucial that the classroom allows them to comfortably hear and understand both teachers and classmates.

Speech perception in the classroom is affected by a range of factors including room geometry, teacher voice characteristics, reverberation time and background noise. Of these, background noise exerts the greatest influence on intelligibility by masking and distorting the target signal 6 . Auditory masking essentially takes two forms (“energetic” and “informational”), both of which are relevant to the classroom environment 7 . Energetic masking (EM) occurs when the background noise corresponds in time and frequency content to the target, leading to an overlap of excitation in the auditory periphery 8 . This, in turn, reduces the audibility of the target rendering it unavailable for processing at higher levels. Energetic masking is a particular issue in open-plan settings as the level of ambient noise (resulting from movement of desks and chairs, computers etc) is directly related to the number of students within a physical space 9 . Informational masking (IM) is centred at higher levels in the auditory pathway and is the result of perceptual interference caused by meaningful noise sources such as speech 10 . It occurs as a consequence of either degraded “object formation” i.e. segregation of the target from extraneous speech (such as intrusive voices from a second class-base in an open-plan setting) or impaired “object selection” – where the listener is required to direct his/her attention to the target speech and while ignoring other voices 11 .

Background noise also affects non-auditory (cognitive) functions. Importantly, the nature or content of the competing signal plays a significant role in the degree of disruption. For example, studies with adults have consistently shown that serial-recall of visually presented items is impeded by “task-irrelevant” sounds such as single taker speech, or even meaningless speech sounds (for a review see Schlittmeier et al. 12 .) In children, this effect on short term memory is even more pronounced and has been attributed to two distinct mechanisms. Firstly, competing sounds that change over time may interfere with the ordering of remembered information. Secondly, irrelevant sounds may capture the listener’s attention if the signal is particularly salient such as significant words (e.g. a person’s name) or an unexpected sound (e.g. a slamming door) 13 , 14 . This latter mechanism is thought to be the more important in primary-school aged children who are particularly susceptible to sound-related distraction as a result of immature attention control processes 15 .

The presence of background noise can also have marked effects on the performance of academic tasks in school-aged children—especially when the masker involves meaningful speech. Klatte et al. 15 reviewed a series of studies evaluating the deleterious effect of noise on academic tasks including reading, spelling and arithmetic and found that most demonstrated impairments when the masker was a meaningful noise. Particularly affected were activities involving reading and writing, where the competing speech was thought to engage semantic functions which directly compete with the semantic processes involved in the task 16 . As such, we might expect performance on these core academic tasks to be adversely affected in the open-plan classroom setting, where irrelevant (but meaningful) speech from multiple class bases are a common occurrence.

Chronic exposure to high levels of background noise affects all aspects of classroom performance. Negative correlations have been demonstrated between classroom noise levels and the development of cognitive skills such as attention, concentration and memory 4 , 17 , 18 and, as a result, overall academic progress may be impacted. Shield and Dockrell 19 for example, in their large-scale study of classroom noise in UK primary schools found that Standardized Assessment Test (SAT) failure rates for mathematics, literacy and science in children aged 7- and 11 years increased by ≈5% for every 2 dB increase in classroom noise. Similarly, Puglisi et al. (2018) 20 found a correlation between classroom acoustics and reading speed in normally developing 7–8 year-old students. Reading acquisition seems to be particularly susceptible to sustained noise exposure 14 , 21 , 22 which may be a reflection of the fact that both speech perception and short-term memory are adversely affected by background noise and both play important roles in reading acquisition.

Despite the recent proliferation of open-plan classrooms there has been little research exploring the efficacy of these environments as learning spaces. In the current longitudinal study we measured within-child changes in reading fluency development as a function of classroom environment (open- vs enclosed-plan). Reading fluency was selected as primary outcome measure as we considered it likely to be affected by the sub-optimal acoustic characteristics of the open-plan setting and because it has been shown to be reflective of overall academic progress 23 , 24 . We also evaluated a range of cognitive and listening abilities to explore which learner characteristics might predispose a child towards a particular classroom setting.

Reading ability at baseline

Baseline reading ability across the whole cohort was normal. Mean fluency rate was 111.3 (SD = 39.6) words per minute which is consistent with published norms and 9/196 participants (4.6%) showed WARP scores outside age-corrected normative values 25 .

Correlations were calculated between baseline reading scores and participant demographic data, audiometric and cognitive assessments. Reading scores were significantly associated with IQ ( r  = 0.261, p  < 0.001), attention scores ( r  = 0.272, p  < 0.001), and working memory scores ( r  = 0.219, p  < 0.01). Baseline reading scores were not significantly correlated with participant age ( p  = 0.910) nor with speech recognition in noise ability ( p  = 0.709). There were significant correlations across the three cognitive assessments (Table 1 ).

The significant variables and “School” were combined in a general linear model to ascertain the independent predictors of baseline reading scores. This showed significant results for School ( F  = 4.93, p  < 0.001), IQ ( F  = 6.72, p  < 0.05) and Attention score ( F  = 7.47, p  < 0.01). Working memory score was not a significant independent predictor of reading ability in this analysis. Tukey post hoc comparisons of the scores for different schools showed that HA had a significantly lower mean reading score than FL, KI, and EN (Fig. 1 ). School FL also had a significantly better mean reading score than BR and PA.

The centre line of each boxplot represents the data median and the bounds of the box show the interquartile range. The whiskers represent the bottom 25% and top 25% of the data range—excluding outliers which are represented by an asterisk.

Reading fluency across the data collection period

A mixed effect analysis was undertaken for all children who completed baseline, open- and enclosed classroom reading assessments. Individual participants were considered as a random variable with fixed factors of classroom condition, order of assessment (enclosed first or open first), and year of assessment. Note that “School” could not be included in this analysis as it is confounded with “year” and “order”. That is, each participant group in a particular school and year had the same order of testing. IQ and attention scores were included as covariates based on the initial analysis of baseline reading scores. The condition factor was significant ( F  = 108.3, p  < 0.001) but “order” ( F  = 0.28, p  = 0.597) and “year” ( F  = 1.50, p  = 0.215) were not. IQ ( F  = 16.2, p  < 0.001) and attention score ( F  = 20.76, p  < 0.001) were both highly significant.

Tukey post hoc comparisons for the condition factor showed that the mean reading scores for open classroom ( M  = 128.3, p  < 0.001) and enclosed classroom ( M  = 132.1, p  < 0.001) were significantly higher than the baseline measurement ( M  = 111.3), indicating general improvement in reading fluency over the course of the study. The mean for enclosed classroom assessments was significantly higher than for open classroom ( p  < 0.05) in this analysis. The effect size for the difference between enclosed and open classroom scores based on the pooled standard deviation ( S  = 14.3) of the mixed effect model was 0.26 (weak).

Analysis of within-child changes across classroom conditions indicated higher rates of reading development during the enclosed study phases. Mean change in WARP score for enclosed school terms was 14.0 (SD = 12.4) words/min and for the open-plan terms was 7.2 (SD = 12.9) words/min ( t  = 4.24, p  < 0.001; 95% CI for paired difference: 3.7, 10.0 words/min). This difference is reflected in Fig. 2 which shows mean change-in-WARP scores calculated term-by-term for schools following a “Closed/Open/Closed” condition sequence (Panel a) and schools following an “Open/Closed/Open” (Panel B) schedule.

Panel ( a ) shows classes following the Enclosed/Open/Enclosed sequence and Panel (b) shows classes following the Open/Enclosed/Open sequence. The centre line of each boxplot represents the data median and the bounds of the box show the interquartile range. The whiskers represent the bottom 25% and top 25% of the data range—excluding outliers which are represented by an asterisk.

Factors affecting reading fluency development

A single score reflecting the reading development bias towards one or other of the classroom conditions was calculated for each student who completed the study protocol. This value, termed the “environment score”, was the difference between reading development rate for the enclosed and the open classroom conditions and was determined as follows:

Ninety-four of the 146 participants (64.4%) showed a positive ES indicating a higher rate of reading fluency development in the enclosed-plan study phases. Fig. 3 shows the distribution of ES values. The data were normally distributed with mean (6.70 words per minute) and median (7.0 words per minute) significantly above zero, that is, the enclosed classroom reading scores were significantly higher than for open classroom across the study. The effect size for the improvement in reading fluency for enclosed compared with open classroom, based on the standard deviation of the distribution (19.6) is 0.34. This is slightly different to the effect size derived from the mixed effects model, as that model includes all data whereas the ES distribution includes one score for each child completing the study protocol.

The centre line of the boxplot represents the data median and the bounds of the box show the interquartile range. The whiskers represent the bottom 25% and top 25% of the data range—excluding outliers which are represented by an asterisk.

A general linear model analysis with ES for each participant as the dependent variable was undertaken including School, baseline reading score, order of testing, age, IQ, attention scores, working memory, and speech recognition in noise scores as independent variables. Order of testing, age, working memory and IQ were not significant in the analysis. School ( F  = 3.24, p  < 0.01), Baseline reading score ( F  = 5.33, p  < 0.05), attention score ( F  = 10.52, p  < 0.01), and speech recognition in noise ( F  = 4.92, p  < 0.05) were significant independent predictors of the environment score (Fig. 4 ). Fig. 5 shows ES differences across schools. Results were broadly similar across sites, although Tukey post hoc comparisons did indicate a significant difference between school FL (which had the highest Environment Score) and the three schools with the lowest Environment Scores—PA, EN and HA.

Panel ( a ) shows ES versus baseline reading fluency, Panel ( b ) shows ES versus speech perception in noise and Panel ( c ) shows ES versus Attention.

The centre line of each boxplot represents the data median, and the bounds of the box show the interquartile range. The whiskers represent the bottom 25% and top 25% of the data range.

In this study, we explored the potential impact of learning environment on academic progress comparing the effect of open- and enclosed-plan classrooms on normally developing children aged 7–10 years. Overall, reading fluency development was greater in the enclosed classroom and the children who showed the greatest environment effect (i.e. bias towards the enclosed classroom) were those with the poorest attention and listening skills.

A child’s rate of academic progress is influenced by a range of factors, some of which are inherent “learner characteristics” and some environmental. Consistent with the literature, baseline reading fluency in our cohort was correlated with a number of intrinsic, cognitive features including non-verbal IQ, working memory and attention 24 , 26 , 27 . These factors were, however, highly correlated with each other, and contrary to previous studies, working memory was not a significant independent predictor of reading ability. Listening capacity also showed no relation to baseline reading fluency, suggesting that the participants’ ability to perceive speech in the presence of background noise had not been a factor in overall literacy development prior to the study.

Baseline reading ability varied across the school sites. This was likely associated with socio-economic factors as the two schools with the lowest mean baseline WARP scores (HA and PA) were in regional locations and had the lowest levels of socio-educational advantage (Table 2 ). It is well established that on average, a student attending a school with lower peer socioeconomic status (SES) will show poorer educational outcomes (including reading) than one attending a school with a higher SES 28 , 29 .

Classroom configuration had a significant effect on rate of literacy development. Day-to-day teaching pedagogies were not prescribed as part of the study, but as many environmental factors as possible (teaching staff, class groups, curricula etc) were held constant through the test period while the only change to the physical classroom environment was the term-by-term deployment of the portable, sound-treated dividing wall. Manipulation of this single variable was associated with clear differences in academic progress with 64% of students showing a higher rate of reading fluency development in the enclosed-classroom condition. Mean Δ WARP fluency score was 6.8 words/min lower for each school term spent in the open-plan condition. When extrapolated across a whole year this corresponds to a 27 word/min delay which is approaching a 1 standard deviation difference in overall reading performance for children in this age group 25 . What the long-term impact of delays of this order may be, and whether they would resolve spontanteously after a period in a more conducive learning environment is unclear, but it is well established that reading and academic deficits in primary school can persist into adolescence/adulthood and can cause psychosocial and behavioural issues as children become disengaged at school 30 , 31 .

The masking effect of increased noise is one possible explanation for diminished reading fluency development in the open-plan classroom configuration. Average background noise levels (recorded with class groups engaged in a range of quiet learning activities), were broadly similar to those reported previously for open-plan classrooms 4 and were higher (5.4 dB) than for the enclosed-plan configuration. In normally developing 7–10 year old children, a noise level difference of this order typically represents a decline in classroom speech intelligibility of ≈10–15% 32 raising the possibility that students would require a significantly higher degree of listening effort to hear and understand what is said in this more challenging acoustic environment 33 .

In addition to the level of background noise, the type of noise present in the open-plan classroom is likely to impede speech understanding and communication. Previous studies have reported high levels of disturbance and distraction in open settings even when background noise levels have been relatively low 4 , suggesting that the “informational masking” effects of meaningful noise (i.e. student and teacher voices from other class bases) limit how well a child can hear their own teacher 4 , 19 . Furthermore, visual distraction from movement in adjacent classes is also thought to affect a child’s ability to understand speech in the open-plan setting 19 .

The link between more challenging listening conditions in the open-plan classroom and restricted academic development may, in part, be explained by the theory of cognitive resource allocation. This theory proposes that a finite, interactive pool of cognitive resources, including memory and attention, are flexibly allocated to an activity depending on the demands of the task. If these resources are channelled elsewhere, task failure may occur. When an incoming auditory signal is masked or degraded, the listener can compensate, filling in the perceptual gaps with knowledge and context 34 . The greater the signal degradation, the greater the shift to predominantly top-down (knowledge based) listening to compensate and the greater the cognitive load 35 . Listening effort and resulting fatigue has been demonstrated in primary school children in typical classroom conditions 36 . Comprehension in such circumstances may be restricted if the resource limits are exceeded as the demands of auditory processing become more effortful 37 . Results of the current study provide support for this theory with students demonstrating the poorest listening in noise skills tending to be those with the greatest negative academic impact in the (noisier) open-plan study phases. i.e. with poorer access to the speech signal requiring creating greater listening effort in the classroom.

The lack of significant interaction between working memory and reading development in the different classroom environments suggests that more than cognitive resource limitations may underly the observed effect. An alternate explanation to listening effort needs to be considered. Although working memory was not a predictive factor, attention capacity was strongly correlated with academic performance bias. Students with the weakest attention showed relatively slower progress in the open-classroom setting. Whilst the masking effect of meaningful noise has been shown to disrupt short-term memory and auditory tasks (which aligns with cognitive resource theory) a recent review by Klatte and colleagues 15 , has shown that meaningful noise can also impact non-auditory tasks such as reading. This phenomenon has been termed the Irrelevant Sound Effect (ISE). The ISE has been proposed to be due to an increased attention burden when trying to ignore the competing signal 15 . The ISE effect of noise on non-auditory performance is greater the younger the children are, with an age effect proposed as further support for the influence of attention in the tasks as younger children have less attentional control. This effect of noise on non—auditory task performance is not reduced when non-meaningful sounds are utilised, further supporting the theory that it is disruption to attention that impacts learning.

The increased attention burden due to meaningful noise creates an increase in cognitive, rather than listening effort. This increased cognitive effort to supress the distraction in turn creates additional working memory load and thereby impacting on the learning occurring 38 . The ISE cognitive effort theory aligns with results of this study, in so far as children with poorer attention skills (and therefore at greater risk from the increased burden on attention) experienced the greatest learning impact.

Overall reading ability (baseline fluency score) was also a factor in classroom environment preference, with good readers typically showing greater reading development in the enclosed-plan condition. This outcome is unexpected, given the positive correlation between baseline reading ability and attention and warrants further investigation.

The extent to which meaningful noise will impact an individual is determined by the unique combination of intrinsic factors the child brings into the classroom. This was borne out by the findings of the current study where participants were not equally affected by classroom environment. While most showed a performance bias towards the enclosed plan setting, some were unaffected by the change in physical environment and small proportion even showed a significantly higher rate of academic development in the open-plan classroom. This latter group (typically comprising students with superior listening skills and/or better command of attention) may have been relatively unaffected by the extra acoustic challenges posed by the open classroom, allowing them to benefit from the pedagogical flexibility afforded by the setting. Children with poorer speech-in-noise or attention skills were, however, found to be at increased risk of either spending more time disengaged from educational activities in the open-plan environment or requiring more cognitive resources to maintain attention leaving fewer to facilitate their learning.

There were a number of study limitations. A more detailed analysis of acoustic conditions in the different classroom settings may have provided specific insights into the impact of background noise on learning in the open- and enclosed classroom settings. For example, to minimise classroom intrusion we took 10 min noise samples during reasonably consistent classroom activities (i.e. group work with minimal movement) and found that background noise levels were higher in the open-plan configuration. Sound-level recordings over a longer time-period (perhaps 8 h) would have provided more accurate noise estimates, taking into account level fluctuations over the course of the entire school day. Similarly, the A-weighted sound measures (which filter low-frequency energy) used in this study are likely to have underestimated the levels of background noise present in each classroom condition. We used the same weighting in both open- and enclosed-classroom recordings so the relative difference may not have been affected, but it is possible that one classroom condition had more low-frequency noise than the other. This is potentially important as low-frequency energy plays a critical role in listening effort and fatigue. As such, adding a measurement with the more linear dB(C) weighting could provide extra information about the degree to which low-frequency noise is an issue in different settings.

The findings of this study suggest a link between increased listening effort in the noisier/more distracting open-plan setting and the development of reading fluency. The present work cannot, however, be taken as proof of this relationship as there were no direct measures of listening effort. Future studies might include behavioural (response time on psychophysical tasks) and/or physiologic (pupil dilation) measures as evidence of a causal relationship 36 , 39 .

This study only considered reading fluency as a measure of academic progress and other aspects of learning development may be unaffected (or even augmented) by the open-plan classroom configuration. WARP reading fluency scores have, however, been strongly correlated in Australian students with each of the reading, writing, spelling, grammar and numeracy metrics from the National Assessment Program of Literacy and Numeracy (NAPLAN) assessment, suggesting that WARP findings are a strong indicator of overall academic progress 24 .

Only children 7–10 years were enroled in the study and the data cannot be directly extrapolated to other age groups. It is, however, likely that younger students whose auditory neural systems are still developing and whose lower levels of linguistic knowledge would restrict their ability to compensate for missing information 32 , 40 , 41 , 42 , 43 , would show even greater energetic and informational masking effects and greater learning consequences in the open-plan classroom. Furthermore, cognitive skill development occurs across childhood with the steepest rate of development between seven and nine years of age 44 , 45 , 46 . Younger students as a group, are therefore less likely to have the requisite cognitive resource pool to navigate the increased listening and attention challenges posed by the open-plan learning environment.

Participants in this study were all audiometrically normal throughout the data collection period and had no known cognitive or learning difficulties. Groups of children who are particularly vulnerable to the effects of noise on speech understanding including those who are hearing impaired 47 , those with auditory processing difficulties, those with language/learning disorders and those who are non-native speakers 4 , 24 are likely to show even greater learning delays in the open-plan classroom.

In summary, the results of this study highlight the important role classroom setting plays in the academic development of young students. Exposure to the open-plan classroom environment resulted in considerably slower rates of reading fluency development across the whole cohort and particularly in those children with relatively poor attention and/or speech in noise skills. This finding is likely associated with increased levels of background noise occurring as a result of higher student numbers and multiple class activities in the one physical space.

The results of this study further suggest that care must be taken if open-plan spaces continue to be utilised. Whilst positive learning and social development opportunities can be provided by open-plan classrooms, appropriate and adequate measures to facilitate speech access should be applied. These include acoustic treatment to maximise sound absorption of ceilings/wallsand lowered ceilings to optimise listening conditions 4 , 42 . Consideration should also be given to visual barriers or operable walls to minimise visual distractions. Careful intentional design of learning spaces to ensure that conditions are optimal for all students will likely have direct positive outcomes on the academic development of young students.

Ethical Approval

This study was approved by the Ethics Committee of the Royal Victorian Eye & Ear Hospital and by the Research in Victorian Schools and Early Intervention Services office, Melbourne Australia and conformed to the tenets of the Declaration of Helsinki. Participation was voluntary and written consent was obtained from each child’s parent/guardian prior to study commencement.

Participating Schools

Data collection was carried out in 6 mainstream Primary Schools (four metropolitan and 2 regional) over a 4 year period (2016–2019) (Table 2 ). All of the schools were in residential areas with no local industrial activity. The teachers were asked to indicate if there had been any changes in the local environment (construction work, changes to aircraft flight paths etc) that had produced a noticeable change in environmental noise levels over the course of the study period. No changes were reported. Four sites participated for a single year, one for 2 years and one for 3 years. For schools participating over multiple calendar years, teaching staff and class locations were held constant, but different groups of students were evaluated each year. Schools were selected based on the availability of open-plan classrooms able to accommodate two separate class groups within a single physical space. As part of the study, each classroom was fit with a portable, dividing wall (HUFCOR Series 2700 Acoustic Accordion Door) allowing the space to be bisected. An “enclosed” environment could therefore be created with one class group on either side of the partition. Removal of the dividing wall created the “open plan” environment. The partition was sound-treated with a Weighted Sound Reduction Index (R W ) rating of 27 dB.

The class groups participating in this study were typical of those in Government Schools across the State of Victoria. Average class size (July 2021) reported for Year 3–6 classes was 23.2 students, and approximately 30% of schools were using open-plan spaces with two (or more) discrete class groups ( https://www.education.vic.gov.au/Documents/about/department/brochurejuly.pdf ). In our study, individual class sizes ranged from 22 to 25 students. For the open-plan condition, two discrete class groups (each with their own teacher), were based in the same room which meant that at full attendance, between 44 and 50 children were physically located within the open-plan space. Over the course of each week in the open-plan condition there were some joint learning sessions (involving both teachers), but for the most part the two class groups worked independently—each managed by their own teacher. As such, when the class groups were separated for the enclosed classroom condition, there was no change to the teacher/student ratio.

Classroom acoustics

Acoustic sampling was undertaken at each site in both open and enclosed configurations. The classrooms were occupied in both conditions and the children were engaged in group work (with talking allowed) but minimal movement. Samples were taken at approximately the same time of day (45 min into the morning session). Recordings were obtained from the centre of each room using a SVAN971 (Class 1) sound level metre. Ten-minute noise samples (recorded in dBA) were obtained for each classroom configuration. Reverberation time (RT) was determined using the integrated impulse response technique according to the ISO 3382 measurement standard. Reverberation time was defined as the time taken for the level of a brief, broad-band stimulus (hand-clap) to decay by 60 decibels [RT(60)] and was the average of recordings at octave frequencies from 125 Hz to 8 kHz. As the acoustic spectra generated by a clap is somewhat variable, we maintained a regular hand configuration (cupped and at an angle) to optimise the low-frequency spectrum and minimise inconsistency. Noise level and RT(60) findings for each test site are shown in Table 2 . The rooms were typically well acoustically treated with carpeted floors, sound-absorbent pin-boards on walls and few exposed hard surfaces. As such, reverberation times were relatively low (i.e. within the range recommended for typically developing children) 48 and showed no difference between open and enclosed classroom conditions (Enclosed: mean=0.42, SD = 0.09 s; Open: mean=0.45, SD = 0.09 s, Paired-T: p  = 0.289, 95%CI: −0.03, 0.09). Background noise levels were also relatively low in the enclosed classroom condition, but showed a significant increase (5.4 dB) in the open-plan configuration (Enclosed: mean = 56.7, SD = 5.2 dB L Aeq, ; Open: mean = 62.1, SD = 4.2 dB L Aeq, , Paired-T: p  = 0.021, 95%CI: 1.20, 9.57). This difference is unlikely to have been the result of a change in the acoustic properties of the classrooms. Bisection of the space (by the accordion door) halves the volume of each room and splits the sound absorption area, but the overall sound power in each enclosed room is reduced as the number of students (the primary noise source) per classroom is also halved. As such, the noise level in open and enclosed conditions would be expected to be similar if only the physical properties of the spaces had changed. The reason for the measured difference (which was reasonably consistent across test sites [Table 2]), is unclear, but may reflect an increase in the activity noise made by students in the open-plan configuration. This phenomenon (known as the Lombard Effect) occurs when pupils feel they need to increase their vocal effort to both hear themselves and be heard in noisy situations. Increases in vocal output of approximately 6–7 dB have been reported for children in background noise levels equivalent to those observed for open-plan classrooms in this study (60–65 dBA) 49 .

Study design and participants

Over the course of one academic year, room configuration (open versus enclosed) was alternated term by term. Condition order was randomised across schools for the first year of participation. For those schools who took part across multiple years, condition order was alternated year by year. Four student groups followed an Open/Enclosed/Open condition sequence across Terms 2, 3 and 4, and five groups followed an Enclosed/Open/Enclosed schedule (Table 2 ).

Each child whose class was located within the room(s) undergoing condition change was invited to participate in the study. Only those students whose parent/guardian consented to have them take part in the data collection were included. The participation rate (across all test sites) was approximately 45%.

One hundred and ninety-six students (88 girls) aged between 7.0 years and 10.4 years (mean=8.6, SD = 0.5 years) underwent baseline evaluation. A breakdown of participant age range for each school is shown in Table 2 . One-hundred and forty-six children completed the longitudinal protocol allowing within-child comparison of development across open- and enclosed-plan study phases. Of the 146 evaluated across both open and enclosed terms, 73 were in classes undergoing the Open/Enclosed/Open schedule and 73 were in the Enclosed/Open/Enclosed (Table 2 ). All had normal sound detection levels (screened at 20 dBHL) for pure tones at octave frequencies between 250 Hz and 8 kHz. Each participant was considered by the primary classroom teacher to be typically developing and was enroled in Grade 3 or 4 at the time of the study.

Baseline data collection was undertaken at the beginning of Term 2 (3 months into the academic year [late March/early April]) and then repeated in the final week of Terms 2 (June), 3 (September) and 4 (December). Each school Term lasted 10 ± 1 weeks. Change values representing the difference in test score across each Term were determined. Where development across two terms with the same classroom condition was measured (eg. the Open-plan phases for class groups following Open/Enclosed/Open schedule) an average of the change values for the two terms was used.

For behavioural data collection each participant was removed from class and individually assessed in a quiet room with low levels of background noise (<40 dBA). Each child was evaluated one-on-one by an experienced study researcher.

Primary academic outcome measure for the study was reading fluency which is a strong predictor of educational outcomes in primary school children 23 , 24 . Furthermore, reading fluency has been demonstrated to be influenced by the acoustic environment with poorer classroom signal-to-noise ratio correlated with poorer performance 20 . Reading fluency was assessed using the Wheldall Assessment of Reading Passages (WARP) 25 . Participants were required to read three × 200 word passages and an average number of words correctly read per minute was calculated. Reported performance ranges (mean ± SD) for participant ages represented in the study were as follows: 7 years: 84 ± 37 words/min; 8 years: 109 ± 40 words/min; 9 years: 118 ± 39 words/min and 10 years: 135 ± 39 words/min. The WARP has been shown to have both high parallel form reliability (0.94–0.96) and internal consistency (0.97 to 0.99) 50 , 51 .

A range of participant characteristics thought likely to impact reading development were also evaluated at baseline and at each subsequent data collection point to explore interactions between cognitive and listening variables. Reading fluency is a complex skill relying on the integration of various higher-level processes including attention and working memory 52 , 53 . Similarly links have been found between attention and working memory with performance on auditory listening tasks 24 , 54 .

General cognitive ability was assessed using the Test of Non-Verbal Intelligence (TONI-4) 55 . This task required the completion of 10 visual patterns using multiple choice options which increased in complexity. The results provide information about a child’s intelligence with minimal linguistic influence and are compared to age-specific normative data to produce normalised IQ scores. The TONI has demonstrated high test-retest reliability with high correlation coefficients (0.89) and limited random measurement error 56 , 57 .

Auditory working memory was evaluated using the Digit Span (reversed) subtest of the Clinical Evaluation of Language Fundamentals 4 (CELF-4) 58 . This test requires the repetition (in reverse order) of a series of numbers of increasing length and reflects auditory working memory, executive function and attentional control 59 , 60 . An age-corrected (scaled) digit span score was calculated for each child. Measures of reliability and validity of the CELF-4 are provided in the examiner’s manual with an internal consistency reliability coefficient of 0.78 and standard error of measurement of 1.41 provided.

Binaural speech perception ability was evaluated using the Listening in Spatialized Noise (LiSN-S) test. This assessment measures the participant’s capacity to segregate a target speech signal from competing speech noise 61 . The test stimuli (both target and noise) are administered under headphones, but a 3-dimensional auditory environment is created by synthesising the auditory signals with head-related transfer functions. Speech reception threshold ([SRT] signal-to-noise ratio required to identify 50% of the words in target sentences) was established for the DV90 listening configuration, where target speech and noise were different voices and presented from different directions. That is, the target signal was presented from 0 0 azimuth while the competing speech was presented from a 90 0 azimuth. Raw SRTs were age-corrected to produce a Z-score which was used in the analyses. This test has a demonstrated test-retest reliability coefficient of 0.7 62 .

Auditory and visual attention was assessed using the Integrated Visual and Auditory Continuous

Performance Test (IVA-CPT) 63 . Each child was presented with 500 trials of ‘1’s and ‘2’s in a pseudo-random pattern to assess sustained visual and auditory attention. Participants are required to click a computer mouse when the number “one” is seen or heard but to ignore any number “two” stimuli. The child’s scaled scores were calculated and compared with age and gender norms by the IVA-CPT software. An “Attention Quotient” based on both auditory and visual attention findings including measures of vigilance (omission errors), focus (variability in processing speed) and speed (reaction time) was used in the analyses. Average Attention Quotient score is 100 and the standard deviation 15. As reported in the Interpretation Manual, the IVA-CPT Attention Quotient has a test-retest r value of 0.74.

Statistical analysis

Data were analysed using the MINITAB 19 statistical package. All assumptions for parametric analyses were met. Normality of data distribution was assessed using Anderson-Darling Normality tests. Correlations were calculated for baseline reading scores against participant demographic data, audiometric and cognitive assessments and the significant factors were included as independent variables in a general linear modelling analysis with baseline reading score as the dependent variable. Mixed effect linear modelling was used to analyse the complete data set. This analysis included participant as a random variable, timing (year), order and classroom condition as categorical factors and the significant cognitive and audiometric measures as covariates. Finally, a general linear model analysis was conducted on the Environment Scores (difference for each child between reading development scores in open and enclosed classrooms) including demographic, cognitive and audiometric measures and baseline reading score as independent variables. In all multivariate analyses Tukey post-hoc tests were used to assess pairwise significant differences for categorical variables where appropriate.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study has been made available through the OSF Home data storage repository (Hyperlink: osf.io/5mn2b). Further information will be provided to suitably qualified researchers by the Corresponding Author upon request.

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Acknowledgements

This work was supported a series of research grants provided by the Victorian Department of Education and Training (2016-17; 2018-19) and by a grant from the Collier Foundation (2017–18). G.R. received funding support from the HEARing CRC (Centre for Research Cooperation) and from the Graeme Clark Chair in Audiology & Speech Science. The funders had no role in study design, data collection/analysis, decision to publish or preparation of the manuscript. We express our gratitude to the students who took part in the project, to the University of Melbourne staff (Anna Dobbyn Terrell, Jocelyn Phillips, Grace Nixon) and to the 20+ Master of Clinical Audiology candidates who contributed to data collection. Thanks also to Mary Mavrias from the Department of Education and Training who assisted with the classroom hardware (acoustic partitions) and to the teachers and schools who accommodated the project in their facilities.

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Rance, G., Dowell, R.C. & Tomlin, D. The effect of classroom environment on literacy development. npj Sci. Learn. 8 , 9 (2023). https://doi.org/10.1038/s41539-023-00157-y

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Learning environments’ influence on students’ learning experience in an Australian Faculty of Business and Economics

Lisiane closs.

1 Programa de Pós-Graduação em Administração, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil

Marian Mahat

2 Melbourne Graduate School of Education, The University of Melbourne, Melbourne, Australia

Wesley Imms

We investigated how learning environments–involving their physical, pedagogical, and psychosocial dimensions–influence students learning experiences in an Australian Faculty of Business and Economics. Qualitative data collection involved observations of eight classrooms over a semester, four focus groups with 21 students and interviews with six educators. The study provided deeper understanding of the dynamic and complex intrinsic interrelations of learning environment dimensions over time, addressing previous gaps in research. It identified and analysed spaces and practices, educational activities, and students’ subjective experiences in different learning environments to illustrate how these multiple elements intersect and influence on the students’ experience. The mixed methods used in the research helped to uncover a broader view of the learning environment and its interdependent influences over time on students’ learning experiences. One practical implication is that any strategies to support a more holistic student learning experience through more effective use of learning environments should be developed at an institutional level.

Introduction

Higher education has been receiving growing attention worldwide in the literature resonating with its responsibility for preparing skilled people in the complex modern knowledge society (OECD 2019 ). Amongst learning needs reported in previous studies are nonroutine analytical and interpersonal skills, complex ways of thinking and doing (OECD 2019 ), flexibility, independence, responsibility, creativity, cooperation (Illeris 2009 ), self-directed learning and entrepreneurship (Fisher 2019 ). In particular, the recent financial, socio-environmental and health global crisis have fuelled the debate over the relevance of business schools and the importance of corporate social responsibility, ethics and leadership considering the positive and negative influences of organizations in society (Thomas and Cornuel 2011 ).

Although research should inform and help to improve educational practices, it does not seem to be supporting changes in higher-education teaching and learning (Acton 2018 ; OECD 2019 ). In relation to learning environments, most existing research focuses predominantly on their physical characteristics rather than on the alignment of spaces and practices, desired educational activities, behaviours, and student opinions (Acton 2018 ; Cleveland and Fisher 2014 ). There is a lack of holistic studies involving dynamic interactions and processes over time (Haggis 2009 ), particularly within classroom settings in higher education (Skordi and Fraser 2019 ), and their influence on students’ learning experience (Chambliss and Taracs 2014 ; Tan et al. 2016 ). There is also a call for research into the interrelationship between the different dimensions of learning environments such as spaces, pedagogy, and learning (Acton 2018 ; McNeil and Borg 2018 ).

Based on the previous literature gaps identified, the research question that we addressed was: how do learning environments influence students learning experiences in higher education? Because learning is contextualised (Lave and Wenger 1991 ) in particular situations and places, this qualitative study was conducted in a single, large, renowned Australian university where international students represented 42% of enrolments in 2018 according to the university website. The research involved the Faculty of Business and Economics which is located in a modern purpose-built building. This site was selected because of its potential to provide students with a favourable learning environment.

As the quality of university students’ learning environments has been positively associated with student learning and experience at universities (Dorman 2014 ), our findings can support academics and educational managers to foster the development and improvement of higher-education learning environments.

The dimensions of a learning environment

Literature on learning environment research presents different concepts, understandings, and dimensions based on diverse epistemological and ontological perspectives. In this study, learning environment was conceived as the “social, physical, psychological, and pedagogical contexts in which learning occurs and which affect student achievement and attitudes” (Learning Environments Research 2019) and which allow an organic understanding of the students’ learning experience in higher education. While elements such as financial resources, structure, people, and time are associated with organizational and government rules, processes or priorities might affect educators and students in the learning environments (Day 2009 ), their physical, pedagogical, and psychosocial dimensions play a central role in the learning process (Merriam and Brockett 2007 ).

Physical dimension

The physical dimension of a learning environment encompasses the physical structure, including technologies, tools, and furniture (Hannafin and Land 1997 ). The classroom physical space and its affordances–the learning activities allowed by furniture, technology, arrangement of rooms and so on–can stimulate or inhibit different teaching strategies (Beckers 2019 ; Marmot 2014 ). Research has also shown that colour, texture, views, light, acoustics, temperature and air quality are important elements of the physical learning environment (Marmot 2014 ), while aesthetic aspects are perceived as less relevant (Beckers 2019 ).

Millennial students need experiential and active learning spaces (Fisher 2019 ) which involve more participation and collaboration from students and require furniture that enables flexible classroom settings (Asino and Pulay 2019 ) where students can see and hear each other and their teacher, see all screens, and use suitable tables and chairs (Marmot 2014 ). In such spaces, teachers and students assume more agentive and active behaviour, relations of power are more balanced and fluid, and the teacher functions as a nucleus and students act as satellites in a dynamic way as the teacher moves around the room (Ravelli 2018 ).

The learning space signals to teachers and students to adopt a particular mode of teaching and learning and they tend to respond to the space consciously and subconsciously (Ramsay et al. 2017 ; Ravelli 2018 ). However, teachers can use new spaces in traditional lecture forms, on one hand, and lecture theatres in new innovative ways, on the other (Ravelli 2018 ). But, when the use of a familiar space deviates from previous experience, it often seems strange to students (Graetz 2006 ).

Pedagogical dimension

The pedagogical dimension of the learning environment (Skordi and Fraser 2019 ) relates to the activities, tools, resources, methods, strategies, and structures involved in facilitating student learning (Hannafin and Land 1997 ). Among the components that stand out in literature reviews on adult learning are: the voluntary nature of learning; self-directedness; the practical or experiential nature of learning; the collaborative and participatory nature of education; and the influence of self-concept on learning (Cranton 2006 ).

Contemporary learning environments are usually based on constructivist learning approaches and are student-centred. They encourage knowledge creation, consider the educator as a facilitator and coach, use cooperative work, adopt authentic assignments, and provide opportunities for self-regulated learning (Baeten et al. 2016 ; Stefanou et al. 2013 ). Although online quizzes and polling allow instant and faster feedback than was otherwise possible (Henderson et al. 2017 ), the massive use of digital technologies by students is mainly outcome-focussed, instead of having a more active, participatory or creative purpose (Henderson et al. 2017 ). Furthermore, tradition, national requirements, accreditation, teacher evaluation, and high-stakes testing restrict learning opportunities, and also can negatively influence students’ learning experiences (Mishra et al. 2013 ).

Psychosocial dimension

Because the psychological and social dimensions are closely connected in a learning environment, these dimensions–aptly known as psychosocial–refer to the origins or outcomes of human behaviour. This dimension involves the ambiance or climate of a particular setting (Dorman 2014 ) and is a predictor of student affective and cognitive outcomes (Fraser 2012 ). Factors that characterise psychosocial environments include: personalization; involvement; student cohesiveness; satisfaction; task orientation; innovation; individualization; investigation; cooperation; equity; and teacher support (Dorman 2014 ; Skordi and Fraser 2019 ), and can also be categorised into the three general dimensions of relationship, personal development, and system maintenance and change (Moss 1974 ).

The social aspects of the learning environment are increasingly acknowledged as central in the university student experience (Childers et al. 2014 ). Relationships with a friend, tutor or lecturer who cares can hinder or foster the motivation to learn and have a deep impact on student outcomes (Chamliss and Taracs 2014 ). Sharing of emotions between students and teachers (Merriam and Brockett 2007 ) also have been highlighted as positive aspects of a learning environment. Feelings of isolation, prejudice, and challenges in establishing relationships with domestic students, on the other hand, have been detrimental to students’ learning experience, particularly for international students (Arkoudis et al. 2019 ).

Our study addressed all the previous learning environments dimensions simultaneously in order to deepen understanding of their influence on students learning experiences.

This qualitative study used mixed methods–observations, focus groups and semi-structured interviews–to provide triangulation of data collected. The participants were students, lecturers and tutors involved in two undergraduate and one Master subjects.

Observations

Observations were conducted in eight different learning spaces: two large lecture theatres, one theatrette and five tutorial classrooms. Observations focused on the dynamic interactions and processes occurring in the learning environments over time (Haggis 2009 ; Skordi and Fraser 2019 ). One of the researchers observed the classes during a 12-week semester from August to October 2019. The physical, pedagogical and psychosocial aspects of the embodied learning environments observed—including verbal and non-verbal communication—were described in a field notebook (Thanen and Knights 2019 ).

Focus groups

Four focus groups involving 21 students were conducted by a single researcher following Liamputtong’s ( 2011 ) procedures. Students self-selected voluntarily to participate in the study. Although student participation (see Table ​ Table1) 1 ) was quite dispersed between the three subjects, there were almost equal numbers of undergraduate and graduate students as well as males and females, which provides a balanced perspective. Focus groups were conducted in private comfortable rooms within the university campus right after the last day of the classes. Students were offered lunch or afternoon tea, depending on the time scheduled. The focus groups were audio-recorded and transcribed. Thematic analysis was performed involving initial and axial coding of data (Liamputtong 2011 ).

Breakdown of numbers of student participants in focus groups

Semi-structured interviews

A single interviewer conducted semi-structured interviews with two tutors (T1 and T2), one tutor coordinator (TC), two undergraduate subject coordinators (SC1 and SC2), and a graduate subject coordinator (SC3). The face-to-face interviews integrated open questions in a pre-defined script based on the objective of the study and previous data. The audio-recorded interviews were transcribed and data were analysed using content analysis through an interpretive approach: printing, sorting, and then organising the data (Bardin 2011 ). The analysis was guided by the theoretical framework. Each author identified and grouped the findings into a priori established categories (learning environment dimensions) and into micro-categories that emerged a posteriori. The interpretations were compared, discussed, and then categorized. A semantic criterion was the basis of category constructions. Prior theory was used as a criterion in analysing and selecting the final categories presented in this article.

Results: students’ experience of learning environment

We investigated various aspects of the students embodied experience in the learning environments. We have included the responses from focus groups and interviews that most accurately illustrate our findings, as well as aspects observed by the researchers. A synthesis of the overall research results is presented and discussed.

Physical environment

The undergraduate students in the study had classes in two lecture theatres and in five different tutorial rooms. The graduate students attended classes in a theatrette. The themes related to the learning environment dimension involved these specific spaces.

The lecture theatres had similar sizes (capacity for 502–506 students) and infrastructure: data projector, hearing aid loop, document camera, and lapel and lectern microphone for the lecturer (see Figs.  1 and ​ and2). 2 ). They were both generally perceived as modern and comfortable by undergraduate students participating in focus groups. The theatres were poorly illuminated to enhance the visibility of the screen(s). The comfort of the chairs and the temperature of the room had a sleepy effect on some students as observed by the researcher and illustrated in the following comment:

They turned the heaters on, and it’s just more comfortable than with my bed at home... you can’t help but fall asleep. (FG1)

Lecture theatre for 506 students

Lecture theatre for 502 students

These elements, reported as important in student learning (Marmot 2014 ), in addition to a non-engaging lecture, influenced students’ embodied learning experience. By the middle of the lecture (around 20–25 min from its start), students would increasingly lean their heads on the back of the chair, yawn, or rub their eyes, among other physical signs of tiredness and disengagement in the class. Also, students would get distracted on their mobile devices when bored, as discussed in various focus groups and exemplified by a student quote:

I pick up my phone when I am really bored in a lecture or something. (FG2)

The size of the theatres, considered too big by students and lecturers, generated in students a sense of invisibility, also observed by Chambliss and Taracs ( 2014 ), that ‘allowed’ student behaviours illustrated by this comment:

We are spread out in this huge lecture theatre and everybody’s just sitting in their own island and the lecturer is also kind of just wandering around not interacting so much… Everybody’s looking at their computer [and], in my laptop, you can access so many things and people can’t see what you’re doing. So it’s so easy to get distracted. (FG3)

One lecturer (SC2) mentioned the difficulty in physically connecting with students apart from those in the first three or four first rows. Possibly associated with the lack of interaction and engagement, some students, especially domestic ones who lived far away from campus, claimed that they would not miss anything when not physically attending a lecture and therefore they watched its recording online at home (FG3).

Students were generally satisfied with their tutorial rooms according to the undergraduate focus-group discussions (FG1, FG2, FG3). Despite a few comments such as “being clinically like” or “not having an incredible look or colours” (FG3), the most important aspect for students was the functionality of the rooms (FG1; FG2; FG3) which corroborates Beckers’ ( 2019 ) findings. White boards, large projector screens, chairs (and tables in one tutorial room) that can move, TV screens (in one tutorial) and good wi-fi connectivity were important affordances mentioned by students. These rooms provided a collaborative, interactive, and safe space which students enjoyed, therefore reinforcing millennials’ preference for experiential and active learning spaces (Fisher 2019 ), as exemplified:

I enjoyed the tutorials a lot more than the lectures because it’s a collaborative space where I feel a bit safer, I guess, to ask questions and get that immediate feedback. When the questions were asked in the group, people would answer. (FG3)

Despite being considered a bit too small by one student (FG1), the best tutorial room for students according to T2 was tutorial room (A), shown in different angles and arrangements in Fig.  3 . It had wide windows providing natural light, white boards on two walls, a computer and a projector (although students and the tutor presenting slides had to face the wall at the back of the room). It was the only tutorial room that had moveable tables and chairs. In this room, students organised the setting by joining tables before the tutorials started when they were not already in a group-work format, generating a sense of agency and active behaviour (Ravelli 2018 ). The room offered enough space for the tutor to move around and support students in class. This moving away from a focal point is characteristic in active learning spaces (Leonard et al. 2017 ; Ramsay et al. 2017 ). Students were close enough to work as a “whole class” (T2) which created a safe environment for students for participating, sharing knowledge, and hearing each other in class. The space also enabled students to write on boards and move around during exercises. This tutorial room provided sufficient flexible settings for experiential and active learning, and conditions for students to see and hear each other adequately (Asino and Pulay 2019 ; Marmot 2014 ).

Tutorial classroom (A) with different furniture arrangements

The theatrette (Fig.  4 ), a smaller version of a lecture theatre, had a traditional configuration with rows of students facing the front (Marmot 2014 ; Thomas 2010 ). This space was “more about the teacher talking and the students at the receiving end” (SC3). The lecturer would like to have round tables so that students could look at each other and have a conversation instead of being in individual seats which don’t allow it to happen.

In the focus groups, students shared feelings that their seats in the theatrette were “too tight, too narrow” and “hard to move” (FG4). On the other hand, compared with other bigger lecture theatres, students thought that the seating provided more intimacy and that everyone could hear the lecturer and other classmates adequately. Participants reinforced the millennial learners’ preference for spaces that allow them to interact and collaborate (Asino and Pulay 2019 ; Fisher 2019 ). They also appreciated the comfortable chairs, the visibility of screens and the lecture recordings. The combination of colours, air quality and natural light had a positive influence on students’ well-being, which is corroborated by Marmot’s ( 2014 ) findings and illustrated by the following comment:

I like the combination of colours in the room… not very popped up, not very dull. So it is a quite good balance that keeps you calm […] plus every lecture has windows. So you know, it’s not intoxicating. You feel the kind of air around always. (FG4)

Our findings show that not only the type of space (e.g. more or less student-centred) but also the combination of factors such as room sizes, furniture, and technology (un)reliability, influence students’ learning experience.

Pedagogical environment

The most appreciated learning strategies observed by the researcher and expressed by students in the focus groups in general where the more hands-on, interactive, and collaborative ones, as identified in adult learning literature (Cranton 2006 ). Students valued being actively engaged in answering questions and “sharing experiences and knowledge” (FG4) with other students, which allowed them to learn from each other, especially in the graduate subject. In the Master subject, the role of facilitator adopted by the teacher (SC3) encouraged knowledge creation and cooperative work–key aspects in a student-centred learning environment (Baeten et al. 2016 ; Stefanou et al. 2013 ). Students also pointed out how discussions in class motivated them to be physically present instead of watching the lecture capture from home (FG4).

Undergraduate students preferred learning experiences that encompassed whole-body activities, as discussed in focus groups (FG1, FG2, FG3), such as working on case studies, writing an analysis on white boards, and rotating to read or add on classmates’ work. The researchers also observed students’ embodied joy in their gestures and movements, pointing proudly at their ‘results’, dancing, clapping and cheering each other’s work. According to one student:

It’s quite a physical activity; it’s not just you on a laptop doing that. You go in and you write on boards, you know, you speak to people… I enjoy. It’s different, you know, and I think it is better. (FG1)

Simulating a real-life production and supplying experience with Lego was another learning task that was appreciated by students and that “allowed things to come together” from theory to practice (FG2). Even though graduate students did not have this kind of experience in their subject, they also mentioned how meaningful it was for them to participate in experiential learning activities that they had in another subject (FG4), which corroborates previous higher-education literature (Baten et al. 2016 ; Stefanou et al. 2013 ).

Despite the criticism of the traditional lecture theatre delivery mode (Marmot 2014 ; Thomas 2010 ), a few strategies for breaking away from that mode were observed in the study. Asking students to discuss a question in pairs seemed to work well when students were already seated together as observed by the researcher. But when there were only around 80 students spread out in a 506-seat lecture theatre—which often happened – students who were isolated would not move their seats to share thoughts with a classmate. Trying to engage students leaving the ‘stage’ and walking around the theatre asking questions was not successful either because students felt embarrassed and threatened by answering in front of a huge audience (FG1, FG2) as exemplified in the following comment:

In lectures, it’s very daunting. You don’t want to make a fool of yourself if you get the answer wrong. So that’s probably why everyone stays quiet, especially because it’s recorded. (FG1)

Also, a non-traditional experiential learning activity involving a game of cards to connect theory and practice was perceived as one of the “awkward moments in the lectures” (FG1). This is consistent with the feeling of strangeness that students feel when the use of a space differs from their familiar experiences (Graetz 2006 ).

One engaging strategy for lectures mentioned by students in all focus groups was the use of online quizzes and polls. But students also pointed out some of its limitations, such as not having enough time to analyse questions and its excessive use (FG1, FG2, FG3). Additionally, technical issues were a consistent problem. Frequent failures experienced during the semester generated time pressure for lecturer SC2, as also found by Marmot ( 2014 ), and signs of impatience in students.

Another aspect mentioned by students was class time management (FG1, FG3, FG4). Students would lose interest when academics spent too much time introducing a class, explaining a concept already known, or allowing long discussions, and then had to rush with the theory at the end of the class. A student quote illustrates this:

When that’s happening, I just sort of lose interest […] I feel like he could have already delivered [the content] in a couple of minutes and very clearly, but it’s just stretched out. I’m just like thinking what am I doing here exactly? (FG3)

Students would maintain their attention longer when academics, among other dynamic activities, walked around the room, asked questions, and presented attractive slides with limited content, videos, current cases, and examples relevant to the young and multicultural audience. This reinforces the importance of student-centred approaches (Baeten et al. 2016 ) and varied activities for students’ learning experience, but it also underlines the short attention span that students verbally and physically demonstrated in the study.

Group assignment was a controversial topic that generated different learning experiences and feelings. Students discussed some tutors’ unnecessary negative expectations set for group work as exemplified by a student quote:

[The tutor] kept saying that it was going to be so difficult to work in a group. There was so much emphasis on the fact that we would have conflict and like it will be so difficult. Like genuinely my group had no conflicts. (FG3)

Peer assessment as a way of punishing “free riders” in groups was also discussed. A typical question was “why should the person who hasn’t worked get the marks for something that I have done for them?” (FG4). When planned as a video assignment with tasks that required students to work as a team, though, it contributed to team building and communication skills, providing an appreciated learning experience as exemplified by the following comment:

I like the communication aspect because you get to hear from other people […] You get to understand how they think, and you learn from other people. (FG1)

Despite the benefits of the cooperative work (Baeten et al. 2016 ; Stefanou et al. 2013 ) offered by group assignments, managing them was one of the most-difficult challenges mentioned by teachers (T1, T2, TC, SC3).

Another salient aspect observed in the study was concern about examinations . Academics mentioned that subjects were often more geared to examinations than to learning outcomes, even though it was not considered the best way to assess students learning (SC2, SC3, T1). While examinations are required as part of course accreditation and/or universities policies (Mishra et al. 2013 ), too much emphasis seems to have been placed on them. Particularly for international students—for whom English is not their first language—a time limit to answer questions is a hurdle and ‘closed book’ examinations were difficult and caused pressure and fear, which might decrease students’ cognitive capabilities (FG4). Examinations also stimulated rote learning and the pursuit of “right answers” (FG1, FG2, FG3, FG4, SC2, T2), which does not foster the critical and creative thinking (Marmot 2014 ) demanded by modern society (OECD 2019 ).

Students also valued well-structured subjects with clear plans, assignments, rubrics, assessments, applicable knowledge, and organized online learning management system (FG1, FG2, FG3)—aspects involved in effective higher education teaching (Ramsden 2003 ). Receiving assignments in advance allowed some students to work autonomously (FG1). Students also appreciated different kinds of assessments (individual and in groups) (FG1, FG3, FG4) because they enable testing of different skills and support different learning styles (Cranton 2006 ). Graduate students shared a negative perception about the overload of concepts and contents, which might limit their curiosity and pursuit of their own interests (Marmot 2014 ). But starting every class by recapitulating the last one and providing images synthesising ideas into a single slide were beneficial for their learning (FG4).

In all focus groups, students emphasised how important the support and solutions provided by academics were when they had difficulties with learning the subject matter, with classmates in group assignments, or specific personal and professional issues. The following quote exemplifies how academics handled such unique situations:

The group issue just came in week 10. And everyone was crying, they were not talking to each other, and it sort of was just a different moment for me. And then I had to look at their assignments individually and write detailed feedback. (SC3)

To express this distinguishing aspect in their learning experience, we borrow from Van Manen ( 1991 ) the concept of pedagogical tact which requires an academic “to see a situation calling for sensitivity, to understand the meaning of what is seen, to sense the significance of the situation, to know how and what to do, and to actually do something right” (p. 146). It requires empathy and sensibility to support real-time understanding of students and take pedagogically-tactful action accordingly (Van Manen 2015 ).

Psychosocial environment

Closely related to pedagogical tact is the perception that academics care about students and want them to succeed in the subject that emerged; this was perceived as a very significant element for students’ learning experience (FG1, FG2, FG3, FG4) which involves the relationship dimension of the learning environment (Moos 1974). Most students were concerned about giving the ‘wrong answers’, with the ability of educators to deal with that being crucial for maintaining students’ participation in class. Thus, teacher support (Fraser et al. 1996 ), especially from tutors who teach in smaller groups and can get closer to students, was key in establishing a safe learning environment. This result corroborates with Chambliss and Taracs’ ( 2014 ) findings regarding the influence of caring relationships on students’ motivation to learn:

You’re so much more willing to participate if you can tell they [teachers] care about your learning and want you to succeed […] they’re really encouraging, they don’t say no, that’s wrong. They offer an alternative, an example. […] It’s a nurturing environment. It depends a lot on the tutors. (FG1)

Students in all focus groups expressed their satisfaction when teachers called them by their names. Such findings corroborate elements involved in the personalisation scale, which is related to concern about students’ personal welfare (Fraser et al. 1996 ). Respecting the identity of students with non-English names was another relevant aspect, particularly in the multicultural context of the study. After realising that students could give her a name that she could pronounce, Tutor 2, for example, reported that she would tell her students:

You don’t have to give me an English name that you don’t really identify with to make it easier for me… If your original name is what you’re comfortable with, stick with that. (T2)

Some language and communication barriers presented interaction challenges for students from different nationalities . International students from the same country tended to sit together in class and speak in their own language when it was over. These behaviours might result in difficulties for international students in improving their English fluency and in establishing relationships with domestic students. Common quotes from international students were: “It is not that easy [to make local friends] (FG3) and “Most of my friends are international students” (FG2). Domestic students also faced problems related to group work with international students as exemplified by the following comment:

My group has had a little bit of difficulty and some people don’t really understand. […] I just feel like we’re not on the same level and I’m not sure if it’s a communication thing because of language or if it’s communication thing because they are just personally not good at communicating. (FG4)

In order to promote student cohesiveness (Skordi and Fraser 2019 ), one subject coordinator used a template in the graduate subject to organize students in cultural, gender, work experience, and other diversity criteria. This enabled students to get to know each other and was appreciated by students. According to the lecturer:

That kind of package has worked for us where we've asked for diversity. […] I think that template is a star. It's one of the best things ever in this class. And I think that also lets students share with each other. That came out in the reports as well. (SC3)

Another subtle aspect influencing the psychosocial learning environment was associated with student gender and ethnicity . A group of domestic white male students, for example, had a negative influence on other students’ participation in one of the tutorials. Whispers, gazes, and laughs from this group generated a tense learning environment and mitigated the involvement (Fraser et al. 1996 ) of other students in class. Similar situations, detrimental to the classroom climate (Dorman 2014 ), arose in other classes according to tutors to present difficult challenges to overcome (when they could) in order to build trust and students cohesiveness in class (Dorman 2014 ; Skordi and Fraser 2019 ). Another situation that illustrated these aspects occurred in a tutorial during a hands-on activity when the researcher observed a female Asian student trying to participate in an exercise with a group of domestic male Caucasian students, but was ‘invisible’ to them. Such invisibility was equally noticed by an academic:

Some Asian girls were probably sitting right in front of me and I completely ignored them in the class, not intentionally, but it just apparently happened to be that they weren't vocal. So, in a class that is so noisy and talkative, sometimes a lot of people get missed out. (SC3)

Although perceived by the researcher during observations, such situations were silenced by students in the focus groups. Previous research, though, has reported how Australian higher education permits men to dominate discussion, as well as physical and discursive spaces (Gray and Nicholas 2019 ). Such aspects of the psychosocial learning environment, involving teacher support and student interaction and learning from each other, were among the most salient in the students’ learning experience in this study, corroborating previous research (Chamliss and Tarac 2014 ; Childers et al. 2014 ).

Our results shed light on how physical, pedagogical, and psychosocial dimensions of the learning environment are closely interconnected and have an impact on the students’ learning experiences. Specifically, physical spaces facilitated or hindered different pedagogies and influenced the psychosocial learning environment. Flexible spaces, such as tutorials classrooms, for example, supported students and teachers in agentive and active behaviours (Ravelli 2018 ), cooperative work, and knowledge creation. Such a student-centred physical and pedagogical learning environment dimensions (Baeten et al. 2016 ; Stefanou et al. 2013 ) stimulated student cohesiveness and satisfaction-elements from the psychosocial dimension (Dorman 2014 ; Skordi and Fraser 2019 ) which all influenced the students’ learning experience.

On the other hand, teachers have shown that more-interactive and collaborative pedagogies (Ravelli 2018 ) could engage students in higher-order learning (French et al. 2019 ) even in more traditional teacher-centred classrooms such as the theatrette classroom. The pedagogy adopted motivated students to be physically present in class, providing more personalization, involvement, cooperation, equity, and satisfaction, influencing the psychosocial learning environment (Dorman 2014 ; Skordi and Fraser 2019 ) and the overall student learning experience. Furthermore, pedagogical tact, subject organisation, amount of content, time management, assignment planning, and an excessive focus on assessments by the university all influenced the psychosocial dimension of the students learning environment.

Results for the psychosocial dimension also call attention to the interrelated influence of learning environment dimensions on one another. Teacher support, for example, would stimulate willingness to participate in class and interfere with the pedagogical dimension. Additionally, the mix of national and international students in a class would interfere with different uses of the classroom spaces.

The importance of the psychosocial learning environment dimension elements such as sharing emotions between students and teachers (Merriam and Brockett 2007 ), and supportive relationships (Chambliss and Taracs 2014 ; Childers et al. 2014 ), especially teachers’ influence on this, have been previously discussed in the literature. The role of students, however, has gained less attention. This study illuminates how students’ nationalities, genders, and ethnicities influenced different uses of spaces in the classroom physical environment, as well as the effectiveness (or not) of the learning activities proposed by tutors and lecturers. Being aware of cultural differences and learning how to treat students equally (Skordi and Fraser 2019 ), helping to avoid isolation and prejudice, and supporting diverse relationships are relevant elements in improving the quality of students’ learning experience (Arkoudis et al. 2019 ), but they still represent a challenge for educators and universities.

The massive use of technology by students and teachers has been mainly outcomes-focused and does not seem to support more participatory or creative activities, as observed by Henderson et al. ( 2017 ). Technology should be able to recreate learning “spaces” that allow the interaction and collaboration required by students (Asino and Pulay 2019 ; Fisher 2019 ). Furthermore, the short attention span of students, incentivized by the immediatism that information and communication technologies generate, might highlight a need to emphasize in education the importance of stopping, analysing, and reflecting before giving immediate responses to the ever more complex solutions to the problems that the world is facing (Coll and Monereo 2010 ).

In this study, we sought understanding of how learning environments–involving physical, pedagogical, and psychosocial dimensions–influence students’ learning experiences in an Australian Faculty of Business and Economics. The study has deepened understanding of the dynamic and complex intrinsic interrelations of learning environment dimensions over time, addressing previous gaps in research (Acton 2018 ; Chambliss and Taracs 2014 ; Cleveland and Fisher 2014 ; Haggis 2009 ; Skordi and Fraser 2019 ). We also identified and analysed spaces and practices, educational activities, and students’ subjective experiences in different learning environments to illustrate how these multiple elements intersect and influence students’ experience. Also, the protocol of mixed methods used in the research contributed to uncovering a broader view of the learning environment and its interdependent influences over time on the students’ learning experiences.

The importance of learning environments in higher education continues to gain momentum. One implication that is clear is that any strategies to support a more holistic student learning experience through more effective use of learning environments should be developed at an institutional level (Day 2009 ). Constraints, such as tight subject organisation and high-stakes examinations stimulate rote learning and anxiety, which are detrimental to the student learning experience (Mishra et al. 2013 ). Flexibility, independence, responsibility, creativity (Illeris 2009 ), and self-directed learning (Fisher 2019 ), among other skills demanded by modern society (OECD 2019 ), are equally hindered by those institutional powers. This discussion goes beyond learning environments, but considering the relevance of business schools for preparing socially-responsible and ethical organisational leaders for society (Thomas and Cornuel 2011 ), especially in face of the COVID-19 crisis, we highlight the relevance of this debate.

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ORIGINAL RESEARCH article

Creating a supportive classroom environment through effective feedback: effects on students’ school identification and behavioral engagement.

• 1 Centro de Investigação em Educação, ISPA – Instituto Universitário, Lisboa, Portugal
• 2 UIDEF, Instituto de Educação da Universidade de Lisboa, Lisboa, Portugal

Previous research revealed the connection between students’ behavioral and emotional engagement and a supportive classroom environment. One of the primary tools teachers have to create a supportive classroom environment is effective feedback. In this study, we assessed the supportive classroom environment using the perception shared by all students from the same classroom of teachers’ use of effective feedback. We aimed to explore the effect of such an environment on students’ behavioral engagement and school identification. Using a probabilistic sample of 1,188 students from 75 classrooms across 6th, 7th, 9th, and 10th grades, we employed multilevel regression modeling with random intercept and fixed slopes. We explored the effects of both individual perceptions of teachers’ use of effective feedback and the supportive classroom environment on student engagement. The analyses identified that students who perceived that their teachers use more effective feedback had a higher level of behavioral engagement and school identification. Once we controlled the effects of these individual perceptions of teachers’ effective feedback, we still observed the effect of a supportive classroom environment on student engagement. So, in classrooms where teachers used more effective feedback creating a supportive classroom environment, students had higher school identification and behavioral engagement levels, regardless of their individual perceptions of teachers’ feedback. The association between variables remained significant even after controlling students’ characteristics (gender, nationality, mother’s level of education, history of grade retention) and classroom characteristics (grade level, type of school, number of students at grade level). Our findings support the potential of teachers’ feedback practices to foster students’ school identification and behavioral engagement to build a more inclusive school environment and value students’ diversity.

Introduction

Students’ behavioral engagement and school identification are considered a critical catalyst for their learning and performance ( Korpershoek et al., 2019 ). Students who value school and feel that they belong there are more likely to behaviorally engage in school activities, experience more in-depth learning, and improve their academic achievement ( Voelkl, 2012 ). These feelings can contribute to reducing school dropout and social exclusion. According to Voelkl (2012) , the development of a sense of identification is mediated by contextual factors–namely, perception of teacher support. These factors can be modified to improve school outcomes. According to Voelkl (2012) , a caring, supportive teacher can impact students’ identification with school. If students feel that they are cared for and are allowed to participate actively in classroom activities, they believe that the school climate is positive, supportive and it promotes the sense of belonging and value of the school ( Adomnik, 2012 ). Therefore, understanding what teachers can do to support and foster students’ engagement is vital. In the present study, we investigated one factor identified as having critical effects on students’ achievement and students’ engagement: teachers’ feedback ( Wisniewski et al., 2020 ). When performing learning tasks and activities, feedback is a relevant aspect present in the teacher-student relationship that can create a positive and supportive classroom environment ( Black and Wiliam, 1998 ; Black et al., 2004 ; Voelkl, 2012 ). Feedback may have consequences on students’ school experience, subsequently improving or impairing their school identification and behavioral engagement and, in turn, affecting their academic achievement ( Reeve, 2012 ; Reschly and Christenson, 2012 ; Voelkl, 2012 ; Burns et al., 2019 ; Wang and Zhang, 2020 ). Previous research has demonstrated that students’ perception of teachers’ use of feedback plays a significant role in student engagement ( Koka and Hein, 2005 , Koka and Hein, 2006 ; Price et al., 2011 ; Leh et al., 2014 ; Conboy et al., 2015 ; Burns et al., 2019 ; Kyaruzi et al., 2019 ; Wang and Zhang, 2020 ). Most of this research had investigated perceived teacher feedback at the individual level (e.g., Koka and Hein, 2005 : Koka and Hein, 2006 ; Leh et al., 2014 ; Conboy et al., 2015 ; Vattøy and Smith, 2019 ; Wang and Zhang, 2020 ). This means that the effectiveness of teacher feedback can promote learning, increase achievement and foster student motivation and engagement.

Thus, as mentioned before, students’ perception of teacher “feedback has individual effects on students” engagement and on their school identification ( Pianta et al., 2012 ; Voelkl, 2012 ). However, the teaching and learning process is not only a simple relationship between the teacher and students, but also among students themselves. In this interrelation, teachers’ behaviors are fundamental in promoting positive interactions in the classroom ( Conroy et al., 2009 ). As teachers and students share several learning environments and experiences, they build perceptions about the teaching-learning process that allows them to make interpretations about the interactive dynamics in the classroom in a very consistent way. In these interactions, teachers can help model constructive feedback and can help develop the group’s competence to give effective feedback and create a positive classroom climate, increasing students’ engagement.

Consequently, it is relevant to understand how the context created by teachers’ feedback are likely to impact on students’ behavioral engagement and on their school identification. Based on previous studies (e.g., Burns et al., 2019 ; Kyaruzi et al., 2019 ), we suggest that the use of effective feedback (assessed by the shared perceptions among students of the same classroom about their teachers’ feedback) create a supportive classroom environment that will positively influence of students’ school identification.

The majority of research regarding students’ perceived feedback and their engagement has focused on the student-level characteristics with less consideration for the contexts in which they are taught ( Burns et al., 2019 ). Therefore, in the present study, we used a multilevel design to investigate how these factors function at both the student and classroom level. We studied the link between perceived teachers’ use of effective feedback and students’ levels of school identification and behavioral engagement at the individual and classroom levels. The central question is whether the supportive classroom environment created by the teachers' use of effective feedback affects students’ behavior after controlling their individual perceptions and the differences at the individual level and at classroom-level.

Teachers’ Feedback

One of the primary tools teachers have to create this supportive class environment is feedback ( Price et al., 2011 ; Reeve, 2012 ; Reschly and Christenson, 2012 ). Feedback is conceptualized as information students receive about their performance or understanding ( Hattie and Timperley, 2007 ) that reduces the discrepancy between what the student knows and what is aimed to be known. Students must also make sense of that information and use it to enhance their learning (Carless and Bound, 2018).

Much has been studied about the effectiveness of feedback, but there is much more to learn about how to optimize its power in the classroom. As Janosz (2012) indicated, the feedback information that students receive and interpret from their schooling experience plays a crucial role in assisting students in improving their motivation and engagement and is a decisive factor implicated in academic achievement ( Hattie and Timperley, 2007 ). Nevertheless, we also know that the variability of feedback effectiveness is vast and that there are certain types of feedback that are more effective than others ( Hattie and Yates, 2014 ). Thus, different types of feedback allow the student to close the gap between current knowledge and a more desirable level of achievement with different levels of effectiveness. Hattie and Timperley (2007) specified some forms it should take; The authors use three feedback questions such as where am I going (feeding up), how am I going (feeding back) and where to next (feeding forward) to clarify the goals and criteria for students. For feedback to be effective, these questions must be answered by the student and feedback needs to work at different levels of cognitive complexity: Task and product level, i.e., corrective feedback; Process level, i.e., providing task processing strategies and cues for information search so students can develop their own learning strategies; Self-regulation level, i.e., providing students with information that allows them to improve their competence to monitor their own learning and progress. According to the authors ( Wisniewski et al., 2020 ), feedback is more effective the more information it contains. So high-information feedback contains information on task, process and (sometimes) self-regulation.

Hattie and Timperley (2007) considered that the feedback needs to focus on the appropriate question and level of cognitive complexity. If not, it risks being ignored and misunderstood and never used by the student. Generally, it has been shown that feedback at the process and self-regulation levels seems to be more effective in enhancing deeper learning, improving task confidence and self-efficacy, and leading to more internal attributions about success or failure ( Hattie and Yates, 2014 ). Furthermore, the meta-analyses of Wisniewski et al. (2020) also suggest that feedback is more effective the more information it contains, while simple forms of reinforcement and punishment have low effects.

The literature also suggests that feedback is related to a positive student-teacher relationship, which is an essential aspect of a positive classroom environment (e.g., Burnett, 2002 ; Gutierrez and Buckley, 2019 ). Burnett (2002) observed that students who perceived receiving feedback focused on their effort were more likely to report a positive teacher-student relationship. The author also reported that students who perceived receiving frequent ability feedback from their teachers were also more likely to perceive the classroom environment in a positive way. On the contrary, teacher praise was not related to students’ perception of the classroom environment or their relationships with their teachers.

Therefore, teachers’ feedback is crucial in improving this supportive class environment by establishing good relationships with students and offering both personal and academic support ( Allen et al., 2018 ). Studies have also determined that a supportive class environment could improve students’ school identification and behavioral engagement ( Voelkl, 2012 ; Allen et al., 2018 ; Olivier et al., 2020 ). Students need to be supported and cared for by teachers to develop and maintain a sense of identification with the school that reinforces their behavioral engagement with the school’s activities ( Voelkl, 2012 ). So, Burnett (2002) recommends that teachers should be careful when providing feedback to students as their relationships with students can influence how students perceive the classroom environment.

In sum, feedback is more effective if it helps students understand what mistakes they made, why they made these mistakes, and what they can do to avoid them in future ( Wisniewski et al., 2020 ). Therefore, the effective feedback sets clear standards and expectations that promote a supportive classroom environment, encouraging students’ autonomy, school identification and engagement ( Pianta et al., 2012 ; Voelkl, 2012 ).

Behavioral Engagement and School Identification

The role of student engagement has been considered to be relevant in the literature since authors identified that it improves achievement and persistence in secondary school ( Finn and Zimmer, 2012 ; Korpershoek et al., 2019 ). Engagement is a complex multidimensional construct defined as

the energy and effort that students employ within their learning community, observable via any number of behavioral, cognitive or affective indicators across a continuum. It is shaped by a range of structural and internal influences, including the complex interplay of relationships, learning activities and the learning environment ( Bond et al., 2020 , p. 3).

Similarly, well supported by research, school identification has become an important educational goal (e.g., Christenson et al., 2008 ; Christenson et al., 2008 ; Reschly and Christenson, 2012 ; Voelkl, 2012 ). School identification can be defined as students’ attitudes about their school, and it is an affective form of student engagement comprising two needs: Belongingness and Valuing. Belongingness refers to “feelings that one is a significant member of the school community, is accepted and respected in school, has a sense of inclusion in school, and includes school as part of one’s self-definition.” ( Voelkl, 1996 , p. 762). On the other hand, Valuing has been defined as students “feeling that school and school outcomes have personal importance and/or practical importance” ( Voelkl, 2012 , p. 198).

School identification, also referred to in the literature as affective engagement ( Christenson et al., 2008 ; Reschly and Christenson, 2012 ), is strongly related to behavioral engagement ( Voelkl, 2012 ; Korpershoek et al., 2019 ); the latter is associated with students' active participation and involvement in school and classroom activities, their effort, attendance, active classroom participation, paying attention and homework completion ( Appleton et al., 2006 ; Fredricks et al., 2011 ). Students who identify with school tend to engage in classroom activities more than others. Research shows that students’ behavioral engagement mediates the relation between school identification and students’ academic trajectories ( Reschly and Christenson, 2006 ; Voelkl, 2012 ). Students who develop a sense of identification with the school are more involved in classroom work, actively participating in their learning and autonomously developing new activities, improving their academic achievement ( Korpershoek et al., 2019 ). As indicated by Voelkl (2012) , “classroom participation is the most proximal outcome of identification” (p. 208). Contrarily, students who do not have a sense of belonging or value their school are more likely to disengage or withdraw, and soon drop out ( Voelkl, 2012 ; Lovelace et al., 2014 ; Lovelace et al., 2017 ).

Teachers’ Feedback, School Identification and Engagement

Although recent meta-analyses had found that feedback that contains information on task, process and self-regulation levels is more effective for cognitive outcomes, like students’ achievement ( Wisniewski et al., 2020 ), research also supports that it enhances academic engagement and motivational outcomes ( Gettinger and Ball, 2007 ; Valente et al., 2015 ; Wisniewski et al., 2020 ). In addition, according to Wang and Zhang (2020) , learning engagement had a mediating effect on the relationship between teachers’ feedback and students’ academic performance. The association between teachers’ feedback and students’ engagement seems to exist regardless of the students liking or disliking the learning subject ( Valente et al., 2015 ), although the utility of the feedback depends on how students perceive it ( Handley et al., 2011 ; Kyaruzi et al., 2019 ; Wang and Zhang, 2020 ). Feedback that “draws attention away from the task and toward self-esteem can have a negative effect on attitudes and performance” ( Black and Wiliam, 1998 , p. 13). Hattie (2009) indicates that feedback directed to the self or at the self-level, even if it is positive, like praise, often directs attention away from the task, diluting the power of feedback. Negative and uninformative feedback has the most evident negative influences, because it reduces the experience of autonomy and self-efficacy and because students need to feel that they belong in learning and that there is a trusting relationship between them, their teachers and their peers ( Hattie, 2009 ; Wisniewski et al., 2020 ). For example, Strambler and Weinstein (2010) observed that students who perceive teachers’ feedback as negative or unsupportive respond by devaluing the importance of school, which was negatively related to students’ academic achievement.

The types of interactions teachers have with their students can promote or inhibit student engagement in the classroom. If teachers offer challenging and fun learning activities, encourage students’ participation and provide feedback about how to reach their goals, they are promoting students’ engagement ( Pianta et al., 2012 ). Authors like Voelkl (2012) believe that school identification has its roots in earlier school grades and becomes stronger over time due to the interactions and school experiences. Consequently, if students feel accepted by their peers and supported by teachers, it is expected that they develop an identification with school. According to this author, the development of identification is mediated by contextual factors, namely perceptions of teacher support. Supportive interactions with teachers contribute to positive self-perceptions such as identification with the school, promoting student engagement with academic activities.

High-quality or effective feedback provides students with rich information about the quality of the student answer but principally about the ways to get the right answer and be sure that students use that information to promote learning. This process implies frequent exchanges of information between the student and the teacher. Teachers’ feedback to students’ responses are critical in their engagement in the learning activities ( Pianta et al., 2012 ). Therefore, supportive class environments are essential to develop and maintain students engagement. The use of high-quality feedback by the teacher over time contributes to progressively increase the sense of belongingness and the value the students attribute to school. This development of school identification can facilitate and promote students’ engagement ( Voelkl, 2012 ).

In sum, previous research suggests that students’ perceptions of teachers’ feedback play an important role in creating a supportive classroom environment ( Price et al., 2011 ; Reeve, 2012 ; Reschly and Christenson, 2012 ). Furthermore, supportive classroom environments have been found to significantly impact students’ engagement ( Voelkl, 2012 ; Allen et al., 2018 ). Therefore, we suggest that students’ shared perceptions of teachers’ use of feedback will positively influence students’ engagement and school identification.

Present Study–The Contextual Effect of Teachers’ Feedback

Previous research had explored the link between students’ individual perceptions of teachers’ feedback, students’ behavioral engagement and school identification at the individual level (e.g., Conboy et al., 2015 ; Carvalho et al., 2020 ). Results confirmed that students’ perceptions about teachers’ use of effective feedback were associated with increased behavioral engagement via increased school identification. In the present study we started by confirming that students’ individual perception of teachers’ use of effective feedback was positively related to their school identification and behavioral engagement.

The second purpose of the present study was to expand previous research by analyzing the effects of teachers’ use of effective feedback as an indicator of a supportive classroom environment that influences students’ school identification and behavioral engagement. We considered that a classroom where students shared the perception that their teachers use effective feedback frequently was a classroom with supportive environment. We hypothesized that in a supportive classroom environment students would have greater levels of school identification and behavioral engagement, even after controlling for the effect of their individual perceptions of teachers feedback (if confirmed in our first hypothesis) and after controlling other differences at the individual and at the classroom-level. This means that if two students perceived that their teacher used little effective feedback, the student that is in a classroom with a highly supportive environment will still present higher levels of behavioral engagement and school identification than the student that is in a classroom with lower supportive environment.

Previous studies have reported that when teachers’ behavior or characteristics are assessed via students’ reports, they should be studied as classroom or school level constructs from a multilevel perspective (e.g. Marsh et al., 2012 ). As a result, we implement multilevel analyses to examine the climate effect of a supportive classroom environment created by the use of effective feedback.

Climate studies evaluate whether school, classroom, or teacher characteristics contribute to predicting students’ outcomes beyond what can be explained by students’ individual characteristics ( Marsh et al., 2012 ). A climate analysis model includes the same variable at both the individual and group levels. Such analyses represent an effort to explain dependent variables (in this case, students’ school identification and behavioral engagement) using a combination of individual and group level independent variables (in this case, students’ perceptions about teachers’ use of effective feedback) ( Blalock, 1984 ). These models allow researchers to investigate the climate effects that teachers’ feedback is presumed to have on the individual students over and above the effect of any individual-level variable that may be operating ( Blalock, 1984 ).

Participant and Procedures

Data collected for this study were part of a broader research project ( Carvalho and Conboy, 2015 ), the main aim of which was to understand the dynamics of teacher feedback in developing students’ identity and the consequences of this dynamic on students’ school trajectories. This project’s target population consisted of middle school and early secondary education students from Portuguese public schools. In Portugal, basic education level is divided into three cycles: first (1st to 4th grades), second (5th to 6th grades), and third cycle (7th to 9th grades). The project focuses on students attending the transitional years between study cycles (6th, 7th, 9th, and 10th grades). In these grade levels, students have several teachers, each one teaching a different subject (Eurydice, 2019).

The sample was selected through a probabilistic, multi-stage sampling procedure in continental Portugal, based on the number of students enrolled in the chosen grades by each Territorial Unit for Purposes Statistics (NUTS II–with five regions). Schools were randomly selected for each grade level. Only one or two classrooms of the same grade were collected in each school.

The final sample consisted of 1,188 students spread over 75 classrooms in 48 schools in continental Portugal. The average number of students by classroom was 16. The sample presented similar patterns of population distribution for grade level and NUTS II region, which indicated that the sample was representative of the Portuguese population. Overall sample characteristics are illustrated in Table 1 .

TABLE 1 . Sample characteristics.

The students responded to a paper-and-pencil questionnaire that included a first section intended to measure students’ school identification, a second section focused on behavioral engagement and a third section that assessed student perception of teacher feedback. The instrument also included a demographic section: gender (0 = girls; 1 = boys), age, nationality (0 = Portuguese; 1 = other nationalities), year of schooling (6th, 7th, 9th or 10th grade), and mother/stepmother’s and father/stepfather’s level of education (1 = 1st cycle of basic education, 2 = 2nd cycle, 3 = 3rd cycle, 4 = secondary education, 5 = higher education).

Students’ Perceptions of Teachers’ Use of Effective Feedback

To measure students’ perceptions of their teachers’ feedback practices, we used eight items from the Teachers’ Feedback Scale, developed by Carvalho et al. (2015) . Students reported their perceptions about teachers’ use of effective feedback in a subject they like. The instruction stated, “Think of a subject that you like”. The reason for including this instruction was to avoid negative experiences associated with a discipline that could interfere with their perceptions of the feedback. The questionnaire included items questioning the feedback at the process level (e.g., “Teachers clearly describe what is not correct and make suggestions for improvement”) or self-regulation level (e.g., “The teachers ask questions that help us reflect on the quality of our work”). Items were answered on a four-point scale (0 = never and 3 = always).

To confirm that the design on the survey did not cause raters to bias their response, we assessed the common method variance (CMV) through the Harman Single Factor technique, as described by Eichhorn (2014). The common latent factor explained less than 50% of the variance (47.22%), indicating that common method bias was not present (Eichhorn, 2014). We conducted confirmatory factor analyses (CFA) to verify the measure’s structural validity in our sample, using the Weighted Least Square Mean and Variance (WLSMV) estimator. Good fit index values were adequate (χ 2 (18) = 61.30, p < 0.001; comparative fit index (CFI) = 0.992; Tucker-Lewis index (TLI) = 0.987; root mean square error of approximation (RMSEA) = 0.045, 90% IC = [0.033, 0.058], p = 0.716). The measure presented adequate levels of reliability (Composite Reliability, CR = 89) (complete results are presented in the Supplementary Material ).

Students’ perceptions of teachers’ effective feedback were aggregated at the classroom level to create a climate variable that reflects the supportive classroom environment. Climate variables are classroom aggregations of ratings by students in which each student is asked to rate a particular classroom characteristic (in this case, the frequency of effective feedback used by the teacher of the discipline they like) that is common to all students ( Marsh et al., 2012 ). Since students like different disciplines, the aggregation of the ratings provides an indicator of the frequency of effective feedback received by students during the time they are in the school. Students’ rates of teachers’ use of effective feedback were aggregated at the classroom level using a manifest measurement–latent aggregation approach ( Marsh et al., 2009 ). The manifest-latent approach uses multilevel models to aggregated student-level responses (the manifest observed variable) to form an unobserved latent variable as an indicator of the climate construct. This procedure permitted correct sampling errors in the aggregation of individual-level constructs to form classroom level climate variables ( Marsh et al., 2009 ). Hence, our supportive classroom environment construct was a latent variable at the classroom level based on shared perceptions among different students with the same teachers. Differences among students within the same classroom (the variable at the student level) do not reflect the classroom environment, representing each student’s unique perceptions that are not explained by the shared perception of different students ( Marsh et al., 2012 ). If there was no significant agreement among students from the same classroom about teachers’ use of feedback, then it could be argued that the classroom level variable did not reflect the classroom environment ( Marsh et al., 2012 ). Consequently, we test the agreement between students in the same classroom using intraclass correlation (ICC2, Lüdtke et al., 2009 ) to indicate the reliability of our classroom environment latent variable. The measure presented an ICC2 of 0.77, which falls within the acceptable threshold of 0.70 and 0.85 recommended by Lüdtke et al. (2009) .

Students’ Behavioral Engagement

A nine-item scale authored by Carvalho et al. (2016) was used to assess the behavioral engagement of the students in the school. The scale assesses two dimensions: academic work, with six items (e.g., “I study the material given in class”) and class participation, with three items (e.g., “I raise my hand to answer a question”). Students answered each on a four-point Likert scale (0 = never and 3 = always). Students were asked to think of a subject they liked. We only used the global measure composed by these dimensions.

We also assessed the CMV of this scale through the Harman Single Factor technique. There was no evidence that common method bias was present (the common latent factor explained only 39.21% of the variance). To confirm the validity of the two-dimensional hierarchical structure of the measure in our sample, we conducted a CFA using the WLSMV estimator. The results indicated that there was also evidence of structure validity (χ 2 (27) = 60.38, p = 0.002; CFI = 0.992; TLI = 0.990; RMSEA = 0.032, 90% IC = [0.021, 0.043], p = 0.996). Composite reliability was also adequate for the global measure (CR = 0.88) (complete results are presented in the Supplementary Material ).

Students’ behavioral engagement outcome variable was aggregated at the classroom level. Once again, we used the manifest-latent approach and calculated the ICC2 as an indicator of reliability ( Lüdtke et al., 2009 ; Marsh et al., 2012 ). The value of ICC2 was 0.67, just below the 0.70 value recommended by Lüdtke et al. (2009) .

Students’ School Identification

The School Identification Scale, authored by Carvalho et al. (2015) , was used to measure students’ school identification. The scale assesses three dimensions of school identification. Three items assess students’ perceptions about their school’s practical value (e.g., “My future depends on what I do in school”). Three items question their feelings of belonging and well-being in school (e.g., “I am happy in this school”). Finally, four items assess students’ perceptions of their capacity and will (e.g., “My skills make me confident about my future”). Items were answered on a four-point Likert scale (0 = completely disagree to 3 = completely agree). In the present study, we only used the global measure composed by these dimensions to avoid multicollinearity problems.

The Harman Single Factor test indicates there was no evidence that common method bias was present in this scale either (the common latent factor explained only 32.25% of the variance). We conducted a CFA to confirm the validity of the three-dimensional hierarchical structure of the measure in our sample using the WLSMV estimator. Good fit index values were adequate (χ 2 (31) = 177.35, p < 0.001; CFI = 0.969; TLI = 0.955; RMSEA = 0.063, 90% IC = [0.054, 0.072], p = 0.008). The global measure presented good levels of reliability (CR = 0.84) (complete results are presented in the Supplementary Material ).

Students’ school identification outcome variable was also aggregated at the classroom level, again using the manifest-latent approach. We tested the ICC2 ( Lüdtke et al., 2009 ) to assess the classroom-average identification level latent variable's reliability. The value of ICC2 was 0.77, indicating adequate reliability levels ( Lüdtke et al., 2009 ).

Data Analyses

All models were estimated using Mplus 8.4. Missing data (1.6% of all data) was handled by allowing missingness to be a function of the observed covariates but not the observed outcomes, the default Mplus procedure ( Muthén and Muthén, 2017 ). Factor scores of the measures were saved and used as observed manifest variables to make the models more parsimonious, reducing the number of variables involved ( Wang and Wang, 2020 ).

We employ multilevel regression modeling with random intercept and fixed slopes using the robust maximum likelihood (MLR) estimator. Respondents (level 1) were “nested” within the classroom (level 2) to account for classroom-level baselines in students’ perceptions. We ran an intercept-only model to examine ICC2 that indicated the proportion of the total variance explained by differences between schools. Next, we estimated two models to evaluate the supportive classroom environment created by teachers’ use of effective feedback. For all the models tested, the predictor variables, except the dichotomous variables, were grand-mean-centred.

In Model 1, we assess a model already tested in previous publications ( Conboy et al., 2015 ; Carvalho et al., 2020 ) based on Voelkl (2012) theory. At the individual level, students’ perceptions of teachers’ feedback contribute to students’ school identification and behavioral engagement. At the classroom level model, the supportive classroom environment contributed to students’ school identification and behavioral levels. We also propose that school identification (both at the individual and classroom levels) contribute to students’ behavioral engagement (see Figure 1 ).

FIGURE 1 . Conceptual model. Latent classroom-level constructs are represented as circles, and student-level indicators of these latent variables are represented as squares.

In Model 2, we incorporated the control variables. It was important to consider and neutralize individual and group variables that could explain our outcome variables (students’ engagement and school identification) ( Creswell, 2012 ). This will allow us to assess more accurately the relationship between teachers’ feedback and our outcomes because of a reduction in the number of errors ( Creswell, 2012 ). At the individual level, we control gender, mother’s and father’s education level, history of grade retention and nationality. These variables had previously been shown to be related to students’ engagement and school identification ( Allen et al., 2018 ; Bear et al., 2019 ; Cunha et al., 2019 ; Olivier et al., 2020 ). At the classroom level, we control grade level and the number of students at the grade level in the school. Previous studies indicated that the odds of a student having low levels of engagement and school identification increased in classrooms in schools with a large number of students ( Finn and Voelkl, 1993 ; Willms, 2003 ; Weiss et al., 2010 ). Moreover, students in the lower grades tend to perceive that their teachers use more effective feedback ( Carvalho et al., 2020 ) and present higher engagement levels ( Eccles et al., 1993 ; Mahatmya et al., 2012 ). We also control for classrooms in schools that were part of the Portuguese TEIP Program for priority intervention educational areas, whose aim was to promote educational inclusion in schools located in socially and economically disadvantaged areas ( European Commission, 2013 ).

Model fit was assessed using the indices and cut-off points suggested by Hu and Bentler (1999) : non-significant values of chi-square (χ 2 ) or less than three times the degrees of freedom; values higher than 0.95 of CFI and TLI; and values lower than 0.08 of RMSEA and Standardized Root Mean Square Residual (SRMR).

Preliminary Analyses

The unconditional “null” model showed that the ICC2 was between 0.111 and 0.179; in other words, between approximately 11.1 and 17.9% of the total variance in the target variables was associated with classroom characteristics (see Table 2 ). Still, the largest proportion of the variance was associated with individual characteristics. Considering that the average cluster size was 16 students, the design effects were between 2.66 and 3.68. Muthén and Satorra (1995) indicated that design effects higher than 2.00 suggest systematic variation between groups that deviate from simple random sampling. Therefore, we confirm that multilevel modeling was necessary ( Heck and Thomas, 2015 ). In Table 2 , we also present the correlation between variables at the student and classroom levels.

TABLE 2 . Classroom Level Intraclass Correlations (ICC) and intercorrelations at students and classroom level.

Teachers’ Feedback Effects on School Identification and Behavioral Engagement

The multilevel analysis results indicate that students’ individual perceptions about teachers’ use of effective feedback were positively related to both students’ school identification and behavioral engagement (see Model 1 in Table 3 ). Students who perceived that their teachers used more effective feedback presented a higher level of school identification and behavioral engagement. More importantly, the results indicated that, after controlling the individual effect, the supportive classroom environment had a significant effect on school identification and behavioral engagement levels. These results indicated that students in classrooms where teachers used more effective feedback, thus creating a supportive classroom environment, had higher levels of school identification and behavioral engagement, regardless of their individual perceptions of teachers’ use of effective feedback.

TABLE 3 . Coefficients of the multilevel models tested.

Students’ school identification also predicted students’ behavioral engagement, but only at the individual level. We observed that students in classrooms with more students with higher school identification levels did not present higher engagement levels as expected. Indeed, individual levels of school identification were more relevant in explaining students’ behavioral engagement.

In model 2, we added the control variables at the individual level (gender, nationality, mother’s education level and history of grade retention) and classroom level (grade-level, number of students in the grade-level, TEIP school). To make the model parsimonious, we removed all non-significant paths that did not affect the fit or the predictive power of the model. The final model results are presented in Table 3 and Figure 2 .

FIGURE 2 . Standardize coefficients of the multilevel Model 2 tested (with MLR estimator). Latent classroom-level constructs are represented as circles and student-level indicators of these latent variables are represented as squares. Dotted lines represent non-significant relations.

At the individual level, besides students’ perceptions of teachers’ feedback, mother’s education level also contributed to students’ school identification and behavioral engagement. The fathers’ educational level only contributed to students’ school identification but not to their behavioral engagement. Gender explained students’ behavioral engagement and school identification, while grade retention explained only school identification. Male students, non-retained students, students whose mother and father had a higher level of education and students who perceived that their teachers used more effective feedback presented higher school identification levels. Female students, students whose mother had a higher level of education, students with a higher level of school identification and students who perceived that their teachers used more effective feedback had higher behavioral engagement levels. Students’ nationality was not related to any variable under study.

Results also indicated that students’ perception of teachers’ feedback was related to students’ history of grade retention. Retained students perceived that their teachers used less effective feedback than non-retained students. Despite this, the relation was very week.

At the classroom level, we observed that the classroom environment effect on school identification and behavioral engagement levels remained significant, with considerable size effects. Students in classrooms with higher levels of supportive environments (i.e., where teachers used more effective feedback) had higher school identification and behavioral engagement levels. Additionally, students in classrooms from schools with fewer students also had higher school identification and behavioral engagement levels. The number of students was not related to the supportive classroom environment.

Table 3 shows that students in classes from lower grade levels presented higher levels of school identification and indicated a more supportive environment where teachers used more effective feedback. There was a less supportive environment in classrooms from higher grade levels. Belonging to a TEIP school was not related to the supportive classroom environment, school identification or behavioral engagement levels.

The final model presented very good indicators of model fit: χ 2 (8) = 6.53, p = 0.588; CFI = 1.000; TLI = 1.004; RMSEA <0.001; SRMR = 0.009 (within), 0.029 (between). The model clearly explained the variance, both at the individual and classroom levels, in students’ behavioral engagement (37.3 and 75.0%, respectively) and school identification (19.6 and 52.8%, respectively). Teachers’ feedback variance is only distinctly explained by the variables at the classroom level (1.1 and 49.2%, respectively).

In this study, we aimed to understand if a supportive learning environment generated by teachers’ use of effective feedback can boost students’ school identification and behavioral engagement. We used teachers’ feedback as an indicator of a supportive classroom environment. Our results confirm previous studies that indicated that students’ perceptions about teacher feedback are positively related with their school identification and behavioral engagement (e.g., Koka and Hein, 2005 , Koka and Hein, 2006 ; Leh et al., 2014 ; Conboy et al., 2015 ; Vattøy and Smith, 2019 ; Carvalho et al., 2020 ; Wang and Zhang, 2020 ). The feedback directly experienced by students enhance their sense of autonomy and self-efficacy by offering information about where they are going, how they are going there and how to reach their goals ( Hattie, 2009 ; Wisniewski et al., 2020 ). Therefore, by offering effective feedback, the teacher is communicating to the student (and, by extension, to all students in the classroom) that learning is essential and relevant to students’ personal goals (where they are going), that they can succeed and are valued by the teacher (by caring about how they are going) and informing them about the behaviors they need to exhibit to better meet expectations in the future (where to next). In other words, effective feedback reinforces the value of school for the students, their feelings of belongingness and their behavioral engagement in school activities, avoiding dropout and social exclusion.

Our results also indicated that other individual variables like mother’s and father’s educational level, gender and grade retention were related to students’ school identification and behavioral engagement, all of which is consistent with previous studies (e.g., Allen et al., 2018 ; Bear et al., 2019 ; Cunha et al., 2019 ; Olivier et al., 2020 ). We found that mothers’ educational level was positively related to students’ school identification and behavioral engagement levels, while the fathers’ educational level was only positively related to students’ school identification. Parents’ educational attitudes and beliefs are considered to be significant influences on their children’s educational attitudes. Mothers with higher education levels are more intellectually involved in school activities, providing intellectual resources and helping with schoolwork, thus creating a positive environment in which students develop their school identification and behavioral engagement ( Bempechat and Shernoff, 2012 ). According to Vieira (2013) , recent research on families and family dynamics in Portugal confirm that mainly mothers are the ones that help children with schoolwork, take them to school, and talk with them about school and their studies. Therefore, behavioral engagement seems to be more affected by the mothers’ level of education than for the fathers’ level of education.

Previous studies have also indicated that female students score higher in all engagement dimensions, especially in the behavioral engagement (e.g., Lietaert et al., 2015 ). Still, our results were not completely consistent with previous studies. In the present study, girls presented higher levels of behavioral engagement, as expected, while males presented higher levels of school identification. It is possible that our results differ from previous research because of the dimensions that were assessed by the school identification measure. Research indicates that males have higher levels of academic self-efficacy (e.g., Huang, 2013 ). In the present study, the school identification measure included a dimension that assesses students’ perceptions of their capacity and will, which contribute greatly to the school identification latent factor (see Supplementary Figure S2 in the Supplementary Material). Therefore, males’ higher levels of school identification might be related to a higher sense of self-efficacy.

Researchers suggest that girls scored higher in their behavioral engagement because activities are focused on language and verbal learning, competences stereotypically related to girls ( Lietaert et al., 2015 ). Still, Lietaert et al. (2015) observed in their study that teachers offered less support to male students, which was related to their lower engagement compared to girls. Authors suggest that teachers offer more support to girls because they are less tolerant of negative behaviors from boys. In contrast, they associated more positive behaviors (more compliance, better organization skills, etc.) to girls. Portuguese teachers also described boys as being disconnected and irresponsible and girls as more focused and responsible ( Wall et al., 2017 ). Although we only find a marginally significant effect of gender on students’ perception of teachers’ feedback, our results seem to correspond to Lietaert et al. (2015) findings: boys perceive that teachers used less effective feedback than girls. Nevertheless, given that the gender bias in feedback observed in this study was small, it could be argued that these students believe that their teachers do not make much difference between genders in the use of effective feedback. Additionally, Lietaert et al. (2015) indicated that it is possible that these lower levels of engagement from boys could explain why teachers are less supportive. Consequently, future research may need to consider the reciprocal effect between teacher support through effective feedback and engagement to better understand this classroom dynamic.

Regarding the effect of grade retention, previous studies indicated that retention seems to leave a significant mark on students that lead them to develop a more negative attitude toward school, associating school with negative experiences (e.g., Martin, 2011 , Santos et al., submitted). A highly interesting finding from our study was that retained students also perceived that their teachers used less effective feedback. Although the effect was small, it could indicate that teachers had lower expectations about retained students (e.g., OECD, 2012 ), using less effective feedback with students with lower achievements. For example, Gentrup et al. (2020) observed that teachers communicate their expectations through different feedback practices, giving less positive feedback and more negative feedback to students for whom they have low expectations. Furthermore, Monteiro et al. (2019) observed that teachers gave less feedback at the process and self-regulation levels to students with low achievement. In Santana’s study ( Santana, 2019 ), a number of Portuguese school directors admitted that teachers tend to ignore retained students.

However, the central question of the present study is whether the aggregated classroom characteristic of teachers’ feedback, as a measure of a supportive classroom environment, affects students’ school identification and behavioral engagement after controlling for other inter-individual differences at the individual level. The answer was positive: Our findings demonstrated that the supportive environment created by teachers’ feedback varies across schools and that students in classrooms where, on average, teachers used more effective feedback and created a supportive classroom environment, had higher levels of school identification and behavioral engagement than students in classrooms without this supportive environment. This was true regardless of students’ individual perceptions about teachers’ feedback.

These findings represent a significant contribution to the theoretical discussion about perceived feedback. Even if a student perceived that his/her teachers used little effective feedback, if s/he was in a classroom with a highly supportive environment, the student would have higher levels of school identification and behavioral engagement than if s/he was in a classroom with a less supportive environment. These results suggest that feedback interactions may affect the learning and engagement of other students in the classroom because they are exposed to both their peers’ behavior and performance and teachers’ feedback to their peers, as observed by Conroy et al. (2009) . By contrast, if teachers display differential feedback for some students based on their individual characteristics (gender, nationality or achievement levels), creating an unsupportive classroom environment, students’ trust for or receptivity to the teacher as a source of support and feeling of belonging will be reduced ( Voelkl, 2012 ). Therefore, similarly to what Hattie and Timperley (2007) and Wisniewski et al. (2020) have said, the feedback has huge power. The use (or not) of effective feedback can have an overall impact on the classroom environment and climate, increasing (or decreasing) students’ engagement and school identification. This conclusion deserves to be studied more deeply in future research because students were asked to think about a discipline they liked. In a discipline where students have negative experiences, this impact can have other effects on their behavioral engagement and identification.

More importantly, the quality of the supportive classroom environment was not related to the number of students in the school, suggesting that teachers in schools with both large and small numbers of students were able to create a supportive environment using effective feedback. Nevertheless, our results indicated that students in classrooms from schools with fewer students had higher levels of school identification and behavioral engagement, which is coherent with previous studies ( Finn and Voelkl, 1993 ; Willms, 2003 ; Weiss et al., 2010 ). However, the size effect of the number of students in the school is smaller than the effect of the supportive classroom environment. As a result, our findings indicated that the classroom environment is more critical than the number of students in the school to predict students’ engagement. As mentioned by Weiss et al. (2010) , the reduction of the number of students in a class does not guarantee that students will experience the same benefits as students in smaller schools, especially if their feedback environment is not adequate to prompt students’ engagement.

At the classroom level, the results indicated that students in classes from lower grade levels presented higher school identification levels. This was expected since the literature indicates that affective engagement tends to decrease upon the transition to adolescence ( Eccles et al., 1993 ; Mahatmya et al., 2012 ). Eccles et al. (1993) suggested that the mismatch between the needs of developing adolescences and the opportunities afforded by their social environments could decrease their school identification. Our results are consistent with this theory since we observed a less supportive classroom environment in classrooms from higher grade levels. Therefore, students in higher grades perceive that their teachers offer less feedback than what they feel they need. It could be that students in lower grade levels perceive a more supportive environment because in 6th and 7th grades teachers focused more on mastery than on performance ( Guo, 2020 ). Teachers’ primary goal in the middle school years is to help students master certain knowledge and skills to prepare for secondary school. Consequently, they may provide more feedback at the process and self-regulation levels to enhance learning habits and abilities ( Guo, 2020 ). On the contrary, in the higher grade levels, teachers may focus most on providing correct answers or solutions to students because teachers are more focused on performance to help students prepare for their upcoming examinations ( Guo, 2020 ). Future studies could test this hypothesis by assessing both the supportive classroom environment and teachers’ goals and beliefs about feedback in several grade levels.

Although the present investigation contributes to the theory about feedback and students’ engagement by accounting for both individual and classroom factors that may impact students’ schooling experience, it has some important limitations to consider for future research. For the assessment of the supportive classroom environment, we relied on the perception that students have of their teachers’ feedback practices. Although classroom-level aggregated measures of students’ perceptions are reliable indicators of a learning environment ( Marsh et al., 2012 ), using classroom observations, interviews, and teacher reports would have provided a complementary evaluation of teachers’ feedback practices. Additionally, we used a manifest-latent approach to aggregate the classroom level variable. Although it controlled sampling errors at the classroom level, we did not control measurement errors at the individual level. A doubly latent approach was necessary to control both types of errors ( Marsh et al., 2009 ). Unfortunately, double latent models required a larger sample than used in the present study ( Marsh et al., 2009 ). Future studies should replicate this research using a larger size sample, with a higher number of classrooms. Another limitation of this research was the use of a single research method. To produce in-depth and richer information in order to better understand the relations between the variables of the problem being studied, it would be better to have leaned toward a mixed methods approach to get potentialities that together give a more precise view of the role of feedback on school identification and student engagement ( Hughes, 2016 ).

Despite these limitations, in the present study we found evidence that indicated that students’ perceptions of teachers’ feedback and the classroom environment created by the effective feedback were more critical for explaining students’ school identification and behavioral engagement than their individual characteristics (like mother’s level of education, gender and grade retention) or the size of the school. Therefore, improving teachers’ use of effective feedback, especially in the upper grade levels, could impact students’ engagement levels. It is essential to provide teachers with training that focuses on giving effective and high-quality feedback. This training should enable teachers to provide a supportive environment to all their students, independent of their gender or what achievements they expect from their students, based on their school trajectory.

Being competent as a teacher to develop a supportive environment in a classroom also means being alert to the expectations constructed around the students. If teachers hold different expectations based on achievement levels or gender, this can influence students’ trust to see teachers as a source of support or as a person that allows her or him to develop feelings of school belonging ( Voelkl, 2012 ).

The evidence suggests that teachers can improve school identification and behavioral engagement by using effective feedback. If there is a supportive environment developed by teachers in the classroom through the use of effective feedback, students will learn better and achieve their psychological and social goals. A supportive environment will motivate the students to communicate with their teachers and peers, to engage in different activities and forms of learning environments and to increase their sense of school identification.

Data Availability Statement

The data analyzed in this study is subject to the following licenses/restrictions: The datasets generated for this study are available on request. Requests to access these datasets should be directed to Carolina Carvalho, [email protected].

Ethics Statement

The studies involving human participants were reviewed and approved by the Comissão de Ética (CdE) of the Instituto de Educação (IE) da Universidade de Lisboa. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

This study was supported by the FCT–Science and Technology Foundation–UIDP/04853/2020.

Conflict of Interest

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.661736/full#supplementary-material

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Keywords: teachers’ feedback practices, school identification, behavioral engagement, supportive classrooms, multilevel analisys, middle school, secondary school

Citation: Monteiro V, Carvalho C and Santos NN (2021) Creating a Supportive Classroom Environment Through Effective Feedback: Effects on Students’ School Identification and Behavioral Engagement. Front. Educ. 6:661736. doi: 10.3389/feduc.2021.661736

Received: 31 January 2021; Accepted: 11 June 2021; Published: 25 June 2021.

Reviewed by:

Copyright © 2021 Monteiro, Carvalho and Santos. 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: Natalie Nóbrega Santos, [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.

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Learning environments’ influence on students’ learning experience in an Australian Faculty of Business and Economics

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• Published: 29 March 2021
• Volume 25 , pages 271–285, ( 2022 )

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• Lisiane Closs   ORCID: orcid.org/0000-0003-1971-9341 1 ,
• Marian Mahat 2 &
• Wesley Imms 2  

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We investigated how learning environments–involving their physical, pedagogical, and psychosocial dimensions–influence students learning experiences in an Australian Faculty of Business and Economics. Qualitative data collection involved observations of eight classrooms over a semester, four focus groups with 21 students and interviews with six educators. The study provided deeper understanding of the dynamic and complex intrinsic interrelations of learning environment dimensions over time, addressing previous gaps in research. It identified and analysed spaces and practices, educational activities, and students’ subjective experiences in different learning environments to illustrate how these multiple elements intersect and influence on the students’ experience. The mixed methods used in the research helped to uncover a broader view of the learning environment and its interdependent influences over time on students’ learning experiences. One practical implication is that any strategies to support a more holistic student learning experience through more effective use of learning environments should be developed at an institutional level.

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Introduction

Higher education has been receiving growing attention worldwide in the literature resonating with its responsibility for preparing skilled people in the complex modern knowledge society (OECD 2019 ). Amongst learning needs reported in previous studies are nonroutine analytical and interpersonal skills, complex ways of thinking and doing (OECD 2019 ), flexibility, independence, responsibility, creativity, cooperation (Illeris 2009 ), self-directed learning and entrepreneurship (Fisher 2019 ). In particular, the recent financial, socio-environmental and health global crisis have fuelled the debate over the relevance of business schools and the importance of corporate social responsibility, ethics and leadership considering the positive and negative influences of organizations in society (Thomas and Cornuel 2011 ).

Although research should inform and help to improve educational practices, it does not seem to be supporting changes in higher-education teaching and learning (Acton 2018 ; OECD 2019 ). In relation to learning environments, most existing research focuses predominantly on their physical characteristics rather than on the alignment of spaces and practices, desired educational activities, behaviours, and student opinions (Acton 2018 ; Cleveland and Fisher 2014 ). There is a lack of holistic studies involving dynamic interactions and processes over time (Haggis 2009 ), particularly within classroom settings in higher education (Skordi and Fraser 2019 ), and their influence on students’ learning experience (Chambliss and Taracs 2014 ; Tan et al. 2016 ). There is also a call for research into the interrelationship between the different dimensions of learning environments such as spaces, pedagogy, and learning (Acton 2018 ; McNeil and Borg 2018 ).

Based on the previous literature gaps identified, the research question that we addressed was: how do learning environments influence students learning experiences in higher education? Because learning is contextualised (Lave and Wenger 1991 ) in particular situations and places, this qualitative study was conducted in a single, large, renowned Australian university where international students represented 42% of enrolments in 2018 according to the university website. The research involved the Faculty of Business and Economics which is located in a modern purpose-built building. This site was selected because of its potential to provide students with a favourable learning environment.

As the quality of university students’ learning environments has been positively associated with student learning and experience at universities (Dorman 2014 ), our findings can support academics and educational managers to foster the development and improvement of higher-education learning environments.

The dimensions of a learning environment

Literature on learning environment research presents different concepts, understandings, and dimensions based on diverse epistemological and ontological perspectives. In this study, learning environment was conceived as the “social, physical, psychological, and pedagogical contexts in which learning occurs and which affect student achievement and attitudes” (Learning Environments Research 2019) and which allow an organic understanding of the students’ learning experience in higher education. While elements such as financial resources, structure, people, and time are associated with organizational and government rules, processes or priorities might affect educators and students in the learning environments (Day 2009 ), their physical, pedagogical, and psychosocial dimensions play a central role in the learning process (Merriam and Brockett 2007 ).

Physical dimension

The physical dimension of a learning environment encompasses the physical structure, including technologies, tools, and furniture (Hannafin and Land 1997 ). The classroom physical space and its affordances–the learning activities allowed by furniture, technology, arrangement of rooms and so on–can stimulate or inhibit different teaching strategies (Beckers 2019 ; Marmot 2014 ). Research has also shown that colour, texture, views, light, acoustics, temperature and air quality are important elements of the physical learning environment (Marmot 2014 ), while aesthetic aspects are perceived as less relevant (Beckers 2019 ).

Millennial students need experiential and active learning spaces (Fisher 2019 ) which involve more participation and collaboration from students and require furniture that enables flexible classroom settings (Asino and Pulay 2019 ) where students can see and hear each other and their teacher, see all screens, and use suitable tables and chairs (Marmot 2014 ). In such spaces, teachers and students assume more agentive and active behaviour, relations of power are more balanced and fluid, and the teacher functions as a nucleus and students act as satellites in a dynamic way as the teacher moves around the room (Ravelli 2018 ).

The learning space signals to teachers and students to adopt a particular mode of teaching and learning and they tend to respond to the space consciously and subconsciously (Ramsay et al. 2017 ; Ravelli 2018 ). However, teachers can use new spaces in traditional lecture forms, on one hand, and lecture theatres in new innovative ways, on the other (Ravelli 2018 ). But, when the use of a familiar space deviates from previous experience, it often seems strange to students (Graetz 2006 ).

Pedagogical dimension

The pedagogical dimension of the learning environment (Skordi and Fraser 2019 ) relates to the activities, tools, resources, methods, strategies, and structures involved in facilitating student learning (Hannafin and Land 1997 ). Among the components that stand out in literature reviews on adult learning are: the voluntary nature of learning; self-directedness; the practical or experiential nature of learning; the collaborative and participatory nature of education; and the influence of self-concept on learning (Cranton 2006 ).

Contemporary learning environments are usually based on constructivist learning approaches and are student-centred. They encourage knowledge creation, consider the educator as a facilitator and coach, use cooperative work, adopt authentic assignments, and provide opportunities for self-regulated learning (Baeten et al. 2016 ; Stefanou et al. 2013 ). Although online quizzes and polling allow instant and faster feedback than was otherwise possible (Henderson et al. 2017 ), the massive use of digital technologies by students is mainly outcome-focussed, instead of having a more active, participatory or creative purpose (Henderson et al. 2017 ). Furthermore, tradition, national requirements, accreditation, teacher evaluation, and high-stakes testing restrict learning opportunities, and also can negatively influence students’ learning experiences (Mishra et al. 2013 ).

Psychosocial dimension

Because the psychological and social dimensions are closely connected in a learning environment, these dimensions–aptly known as psychosocial–refer to the origins or outcomes of human behaviour. This dimension involves the ambiance or climate of a particular setting (Dorman 2014 ) and is a predictor of student affective and cognitive outcomes (Fraser 2012 ). Factors that characterise psychosocial environments include: personalization; involvement; student cohesiveness; satisfaction; task orientation; innovation; individualization; investigation; cooperation; equity; and teacher support (Dorman 2014 ; Skordi and Fraser 2019 ), and can also be categorised into the three general dimensions of relationship, personal development, and system maintenance and change (Moss 1974 ).

The social aspects of the learning environment are increasingly acknowledged as central in the university student experience (Childers et al. 2014 ). Relationships with a friend, tutor or lecturer who cares can hinder or foster the motivation to learn and have a deep impact on student outcomes (Chamliss and Taracs 2014 ). Sharing of emotions between students and teachers (Merriam and Brockett 2007 ) also have been highlighted as positive aspects of a learning environment. Feelings of isolation, prejudice, and challenges in establishing relationships with domestic students, on the other hand, have been detrimental to students’ learning experience, particularly for international students (Arkoudis et al. 2019 ).

Our study addressed all the previous learning environments dimensions simultaneously in order to deepen understanding of their influence on students learning experiences.

This qualitative study used mixed methods–observations, focus groups and semi-structured interviews–to provide triangulation of data collected. The participants were students, lecturers and tutors involved in two undergraduate and one Master subjects.

Observations

Observations were conducted in eight different learning spaces: two large lecture theatres, one theatrette and five tutorial classrooms. Observations focused on the dynamic interactions and processes occurring in the learning environments over time (Haggis 2009 ; Skordi and Fraser 2019 ). One of the researchers observed the classes during a 12-week semester from August to October 2019. The physical, pedagogical and psychosocial aspects of the embodied learning environments observed—including verbal and non-verbal communication—were described in a field notebook (Thanen and Knights 2019 ).

Focus groups

Four focus groups involving 21 students were conducted by a single researcher following Liamputtong’s ( 2011 ) procedures. Students self-selected voluntarily to participate in the study. Although student participation (see Table 1 ) was quite dispersed between the three subjects, there were almost equal numbers of undergraduate and graduate students as well as males and females, which provides a balanced perspective. Focus groups were conducted in private comfortable rooms within the university campus right after the last day of the classes. Students were offered lunch or afternoon tea, depending on the time scheduled. The focus groups were audio-recorded and transcribed. Thematic analysis was performed involving initial and axial coding of data (Liamputtong 2011 ).

Semi-structured interviews

A single interviewer conducted semi-structured interviews with two tutors (T1 and T2), one tutor coordinator (TC), two undergraduate subject coordinators (SC1 and SC2), and a graduate subject coordinator (SC3). The face-to-face interviews integrated open questions in a pre-defined script based on the objective of the study and previous data. The audio-recorded interviews were transcribed and data were analysed using content analysis through an interpretive approach: printing, sorting, and then organising the data (Bardin 2011 ). The analysis was guided by the theoretical framework. Each author identified and grouped the findings into a priori established categories (learning environment dimensions) and into micro-categories that emerged a posteriori. The interpretations were compared, discussed, and then categorized. A semantic criterion was the basis of category constructions. Prior theory was used as a criterion in analysing and selecting the final categories presented in this article.

Results: students’ experience of learning environment

We investigated various aspects of the students embodied experience in the learning environments. We have included the responses from focus groups and interviews that most accurately illustrate our findings, as well as aspects observed by the researchers. A synthesis of the overall research results is presented and discussed.

Physical environment

The undergraduate students in the study had classes in two lecture theatres and in five different tutorial rooms. The graduate students attended classes in a theatrette. The themes related to the learning environment dimension involved these specific spaces.

The lecture theatres had similar sizes (capacity for 502–506 students) and infrastructure: data projector, hearing aid loop, document camera, and lapel and lectern microphone for the lecturer (see Figs.  1 and 2 ). They were both generally perceived as modern and comfortable by undergraduate students participating in focus groups. The theatres were poorly illuminated to enhance the visibility of the screen(s). The comfort of the chairs and the temperature of the room had a sleepy effect on some students as observed by the researcher and illustrated in the following comment:

They turned the heaters on, and it’s just more comfortable than with my bed at home... you can’t help but fall asleep. (FG1)

Lecture theatre for 506 students

Lecture theatre for 502 students

These elements, reported as important in student learning (Marmot 2014 ), in addition to a non-engaging lecture, influenced students’ embodied learning experience. By the middle of the lecture (around 20–25 min from its start), students would increasingly lean their heads on the back of the chair, yawn, or rub their eyes, among other physical signs of tiredness and disengagement in the class. Also, students would get distracted on their mobile devices when bored, as discussed in various focus groups and exemplified by a student quote:

I pick up my phone when I am really bored in a lecture or something. (FG2)

The size of the theatres, considered too big by students and lecturers, generated in students a sense of invisibility, also observed by Chambliss and Taracs ( 2014 ), that ‘allowed’ student behaviours illustrated by this comment:

We are spread out in this huge lecture theatre and everybody’s just sitting in their own island and the lecturer is also kind of just wandering around not interacting so much… Everybody’s looking at their computer [and], in my laptop, you can access so many things and people can’t see what you’re doing. So it’s so easy to get distracted. (FG3)

One lecturer (SC2) mentioned the difficulty in physically connecting with students apart from those in the first three or four first rows. Possibly associated with the lack of interaction and engagement, some students, especially domestic ones who lived far away from campus, claimed that they would not miss anything when not physically attending a lecture and therefore they watched its recording online at home (FG3).

Students were generally satisfied with their tutorial rooms according to the undergraduate focus-group discussions (FG1, FG2, FG3). Despite a few comments such as “being clinically like” or “not having an incredible look or colours” (FG3), the most important aspect for students was the functionality of the rooms (FG1; FG2; FG3) which corroborates Beckers’ ( 2019 ) findings. White boards, large projector screens, chairs (and tables in one tutorial room) that can move, TV screens (in one tutorial) and good wi-fi connectivity were important affordances mentioned by students. These rooms provided a collaborative, interactive, and safe space which students enjoyed, therefore reinforcing millennials’ preference for experiential and active learning spaces (Fisher 2019 ), as exemplified:

I enjoyed the tutorials a lot more than the lectures because it’s a collaborative space where I feel a bit safer, I guess, to ask questions and get that immediate feedback. When the questions were asked in the group, people would answer. (FG3)

Despite being considered a bit too small by one student (FG1), the best tutorial room for students according to T2 was tutorial room (A), shown in different angles and arrangements in Fig.  3 . It had wide windows providing natural light, white boards on two walls, a computer and a projector (although students and the tutor presenting slides had to face the wall at the back of the room). It was the only tutorial room that had moveable tables and chairs. In this room, students organised the setting by joining tables before the tutorials started when they were not already in a group-work format, generating a sense of agency and active behaviour (Ravelli 2018 ). The room offered enough space for the tutor to move around and support students in class. This moving away from a focal point is characteristic in active learning spaces (Leonard et al. 2017 ; Ramsay et al. 2017 ). Students were close enough to work as a “whole class” (T2) which created a safe environment for students for participating, sharing knowledge, and hearing each other in class. The space also enabled students to write on boards and move around during exercises. This tutorial room provided sufficient flexible settings for experiential and active learning, and conditions for students to see and hear each other adequately (Asino and Pulay 2019 ; Marmot 2014 ).

Tutorial classroom (A) with different furniture arrangements

The theatrette (Fig.  4 ), a smaller version of a lecture theatre, had a traditional configuration with rows of students facing the front (Marmot 2014 ; Thomas 2010 ). This space was “more about the teacher talking and the students at the receiving end” (SC3). The lecturer would like to have round tables so that students could look at each other and have a conversation instead of being in individual seats which don’t allow it to happen.

In the focus groups, students shared feelings that their seats in the theatrette were “too tight, too narrow” and “hard to move” (FG4). On the other hand, compared with other bigger lecture theatres, students thought that the seating provided more intimacy and that everyone could hear the lecturer and other classmates adequately. Participants reinforced the millennial learners’ preference for spaces that allow them to interact and collaborate (Asino and Pulay 2019 ; Fisher 2019 ). They also appreciated the comfortable chairs, the visibility of screens and the lecture recordings. The combination of colours, air quality and natural light had a positive influence on students’ well-being, which is corroborated by Marmot’s ( 2014 ) findings and illustrated by the following comment:

I like the combination of colours in the room… not very popped up, not very dull. So it is a quite good balance that keeps you calm […] plus every lecture has windows. So you know, it’s not intoxicating. You feel the kind of air around always. (FG4)

Our findings show that not only the type of space (e.g. more or less student-centred) but also the combination of factors such as room sizes, furniture, and technology (un)reliability, influence students’ learning experience.

Pedagogical environment

The most appreciated learning strategies observed by the researcher and expressed by students in the focus groups in general where the more hands-on, interactive, and collaborative ones, as identified in adult learning literature (Cranton 2006 ). Students valued being actively engaged in answering questions and “sharing experiences and knowledge” (FG4) with other students, which allowed them to learn from each other, especially in the graduate subject. In the Master subject, the role of facilitator adopted by the teacher (SC3) encouraged knowledge creation and cooperative work–key aspects in a student-centred learning environment (Baeten et al. 2016 ; Stefanou et al. 2013 ). Students also pointed out how discussions in class motivated them to be physically present instead of watching the lecture capture from home (FG4).

Undergraduate students preferred learning experiences that encompassed whole-body activities, as discussed in focus groups (FG1, FG2, FG3), such as working on case studies, writing an analysis on white boards, and rotating to read or add on classmates’ work. The researchers also observed students’ embodied joy in their gestures and movements, pointing proudly at their ‘results’, dancing, clapping and cheering each other’s work. According to one student:

It’s quite a physical activity; it’s not just you on a laptop doing that. You go in and you write on boards, you know, you speak to people… I enjoy. It’s different, you know, and I think it is better. (FG1)

Simulating a real-life production and supplying experience with Lego was another learning task that was appreciated by students and that “allowed things to come together” from theory to practice (FG2). Even though graduate students did not have this kind of experience in their subject, they also mentioned how meaningful it was for them to participate in experiential learning activities that they had in another subject (FG4), which corroborates previous higher-education literature (Baten et al. 2016 ; Stefanou et al. 2013 ).

Despite the criticism of the traditional lecture theatre delivery mode (Marmot 2014 ; Thomas 2010 ), a few strategies for breaking away from that mode were observed in the study. Asking students to discuss a question in pairs seemed to work well when students were already seated together as observed by the researcher. But when there were only around 80 students spread out in a 506-seat lecture theatre—which often happened – students who were isolated would not move their seats to share thoughts with a classmate. Trying to engage students leaving the ‘stage’ and walking around the theatre asking questions was not successful either because students felt embarrassed and threatened by answering in front of a huge audience (FG1, FG2) as exemplified in the following comment:

In lectures, it’s very daunting. You don’t want to make a fool of yourself if you get the answer wrong. So that’s probably why everyone stays quiet, especially because it’s recorded. (FG1)

Also, a non-traditional experiential learning activity involving a game of cards to connect theory and practice was perceived as one of the “awkward moments in the lectures” (FG1). This is consistent with the feeling of strangeness that students feel when the use of a space differs from their familiar experiences (Graetz 2006 ).

One engaging strategy for lectures mentioned by students in all focus groups was the use of online quizzes and polls. But students also pointed out some of its limitations, such as not having enough time to analyse questions and its excessive use (FG1, FG2, FG3). Additionally, technical issues were a consistent problem. Frequent failures experienced during the semester generated time pressure for lecturer SC2, as also found by Marmot ( 2014 ), and signs of impatience in students.

Another aspect mentioned by students was class time management (FG1, FG3, FG4). Students would lose interest when academics spent too much time introducing a class, explaining a concept already known, or allowing long discussions, and then had to rush with the theory at the end of the class. A student quote illustrates this:

When that’s happening, I just sort of lose interest […] I feel like he could have already delivered [the content] in a couple of minutes and very clearly, but it’s just stretched out. I’m just like thinking what am I doing here exactly? (FG3)

Students would maintain their attention longer when academics, among other dynamic activities, walked around the room, asked questions, and presented attractive slides with limited content, videos, current cases, and examples relevant to the young and multicultural audience. This reinforces the importance of student-centred approaches (Baeten et al. 2016 ) and varied activities for students’ learning experience, but it also underlines the short attention span that students verbally and physically demonstrated in the study.

Group assignment was a controversial topic that generated different learning experiences and feelings. Students discussed some tutors’ unnecessary negative expectations set for group work as exemplified by a student quote:

[The tutor] kept saying that it was going to be so difficult to work in a group. There was so much emphasis on the fact that we would have conflict and like it will be so difficult. Like genuinely my group had no conflicts. (FG3)

Peer assessment as a way of punishing “free riders” in groups was also discussed. A typical question was “why should the person who hasn’t worked get the marks for something that I have done for them?” (FG4). When planned as a video assignment with tasks that required students to work as a team, though, it contributed to team building and communication skills, providing an appreciated learning experience as exemplified by the following comment:

I like the communication aspect because you get to hear from other people […] You get to understand how they think, and you learn from other people. (FG1)

Despite the benefits of the cooperative work (Baeten et al. 2016 ; Stefanou et al. 2013 ) offered by group assignments, managing them was one of the most-difficult challenges mentioned by teachers (T1, T2, TC, SC3).

Another salient aspect observed in the study was concern about examinations . Academics mentioned that subjects were often more geared to examinations than to learning outcomes, even though it was not considered the best way to assess students learning (SC2, SC3, T1). While examinations are required as part of course accreditation and/or universities policies (Mishra et al. 2013 ), too much emphasis seems to have been placed on them. Particularly for international students—for whom English is not their first language—a time limit to answer questions is a hurdle and ‘closed book’ examinations were difficult and caused pressure and fear, which might decrease students’ cognitive capabilities (FG4). Examinations also stimulated rote learning and the pursuit of “right answers” (FG1, FG2, FG3, FG4, SC2, T2), which does not foster the critical and creative thinking (Marmot 2014 ) demanded by modern society (OECD 2019 ).

Students also valued well-structured subjects with clear plans, assignments, rubrics, assessments, applicable knowledge, and organized online learning management system (FG1, FG2, FG3)—aspects involved in effective higher education teaching (Ramsden 2003 ). Receiving assignments in advance allowed some students to work autonomously (FG1). Students also appreciated different kinds of assessments (individual and in groups) (FG1, FG3, FG4) because they enable testing of different skills and support different learning styles (Cranton 2006 ). Graduate students shared a negative perception about the overload of concepts and contents, which might limit their curiosity and pursuit of their own interests (Marmot 2014 ). But starting every class by recapitulating the last one and providing images synthesising ideas into a single slide were beneficial for their learning (FG4).

In all focus groups, students emphasised how important the support and solutions provided by academics were when they had difficulties with learning the subject matter, with classmates in group assignments, or specific personal and professional issues. The following quote exemplifies how academics handled such unique situations:

The group issue just came in week 10. And everyone was crying, they were not talking to each other, and it sort of was just a different moment for me. And then I had to look at their assignments individually and write detailed feedback. (SC3)

To express this distinguishing aspect in their learning experience, we borrow from Van Manen ( 1991 ) the concept of pedagogical tact which requires an academic “to see a situation calling for sensitivity, to understand the meaning of what is seen, to sense the significance of the situation, to know how and what to do, and to actually do something right” (p. 146). It requires empathy and sensibility to support real-time understanding of students and take pedagogically-tactful action accordingly (Van Manen 2015 ).

Psychosocial environment

Closely related to pedagogical tact is the perception that academics care about students and want them to succeed in the subject that emerged; this was perceived as a very significant element for students’ learning experience (FG1, FG2, FG3, FG4) which involves the relationship dimension of the learning environment (Moos 1974). Most students were concerned about giving the ‘wrong answers’, with the ability of educators to deal with that being crucial for maintaining students’ participation in class. Thus, teacher support (Fraser et al. 1996 ), especially from tutors who teach in smaller groups and can get closer to students, was key in establishing a safe learning environment. This result corroborates with Chambliss and Taracs’ ( 2014 ) findings regarding the influence of caring relationships on students’ motivation to learn:

You’re so much more willing to participate if you can tell they [teachers] care about your learning and want you to succeed […] they’re really encouraging, they don’t say no, that’s wrong. They offer an alternative, an example. […] It’s a nurturing environment. It depends a lot on the tutors. (FG1)

Students in all focus groups expressed their satisfaction when teachers called them by their names. Such findings corroborate elements involved in the personalisation scale, which is related to concern about students’ personal welfare (Fraser et al. 1996 ). Respecting the identity of students with non-English names was another relevant aspect, particularly in the multicultural context of the study. After realising that students could give her a name that she could pronounce, Tutor 2, for example, reported that she would tell her students:

You don’t have to give me an English name that you don’t really identify with to make it easier for me… If your original name is what you’re comfortable with, stick with that. (T2)

Some language and communication barriers presented interaction challenges for students from different nationalities . International students from the same country tended to sit together in class and speak in their own language when it was over. These behaviours might result in difficulties for international students in improving their English fluency and in establishing relationships with domestic students. Common quotes from international students were: “It is not that easy [to make local friends] (FG3) and “Most of my friends are international students” (FG2). Domestic students also faced problems related to group work with international students as exemplified by the following comment:

My group has had a little bit of difficulty and some people don’t really understand. […] I just feel like we’re not on the same level and I’m not sure if it’s a communication thing because of language or if it’s communication thing because they are just personally not good at communicating. (FG4)

In order to promote student cohesiveness (Skordi and Fraser 2019 ), one subject coordinator used a template in the graduate subject to organize students in cultural, gender, work experience, and other diversity criteria. This enabled students to get to know each other and was appreciated by students. According to the lecturer:

That kind of package has worked for us where we've asked for diversity. […] I think that template is a star. It's one of the best things ever in this class. And I think that also lets students share with each other. That came out in the reports as well. (SC3)

Another subtle aspect influencing the psychosocial learning environment was associated with student gender and ethnicity . A group of domestic white male students, for example, had a negative influence on other students’ participation in one of the tutorials. Whispers, gazes, and laughs from this group generated a tense learning environment and mitigated the involvement (Fraser et al. 1996 ) of other students in class. Similar situations, detrimental to the classroom climate (Dorman 2014 ), arose in other classes according to tutors to present difficult challenges to overcome (when they could) in order to build trust and students cohesiveness in class (Dorman 2014 ; Skordi and Fraser 2019 ). Another situation that illustrated these aspects occurred in a tutorial during a hands-on activity when the researcher observed a female Asian student trying to participate in an exercise with a group of domestic male Caucasian students, but was ‘invisible’ to them. Such invisibility was equally noticed by an academic:

Some Asian girls were probably sitting right in front of me and I completely ignored them in the class, not intentionally, but it just apparently happened to be that they weren't vocal. So, in a class that is so noisy and talkative, sometimes a lot of people get missed out. (SC3)

Although perceived by the researcher during observations, such situations were silenced by students in the focus groups. Previous research, though, has reported how Australian higher education permits men to dominate discussion, as well as physical and discursive spaces (Gray and Nicholas 2019 ). Such aspects of the psychosocial learning environment, involving teacher support and student interaction and learning from each other, were among the most salient in the students’ learning experience in this study, corroborating previous research (Chamliss and Tarac 2014 ; Childers et al. 2014 ).

Our results shed light on how physical, pedagogical, and psychosocial dimensions of the learning environment are closely interconnected and have an impact on the students’ learning experiences. Specifically, physical spaces facilitated or hindered different pedagogies and influenced the psychosocial learning environment. Flexible spaces, such as tutorials classrooms, for example, supported students and teachers in agentive and active behaviours (Ravelli 2018 ), cooperative work, and knowledge creation. Such a student-centred physical and pedagogical learning environment dimensions (Baeten et al. 2016 ; Stefanou et al. 2013 ) stimulated student cohesiveness and satisfaction-elements from the psychosocial dimension (Dorman 2014 ; Skordi and Fraser 2019 ) which all influenced the students’ learning experience.

On the other hand, teachers have shown that more-interactive and collaborative pedagogies (Ravelli 2018 ) could engage students in higher-order learning (French et al. 2019 ) even in more traditional teacher-centred classrooms such as the theatrette classroom. The pedagogy adopted motivated students to be physically present in class, providing more personalization, involvement, cooperation, equity, and satisfaction, influencing the psychosocial learning environment (Dorman 2014 ; Skordi and Fraser 2019 ) and the overall student learning experience. Furthermore, pedagogical tact, subject organisation, amount of content, time management, assignment planning, and an excessive focus on assessments by the university all influenced the psychosocial dimension of the students learning environment.

Results for the psychosocial dimension also call attention to the interrelated influence of learning environment dimensions on one another. Teacher support, for example, would stimulate willingness to participate in class and interfere with the pedagogical dimension. Additionally, the mix of national and international students in a class would interfere with different uses of the classroom spaces.

The importance of the psychosocial learning environment dimension elements such as sharing emotions between students and teachers (Merriam and Brockett 2007 ), and supportive relationships (Chambliss and Taracs 2014 ; Childers et al. 2014 ), especially teachers’ influence on this, have been previously discussed in the literature. The role of students, however, has gained less attention. This study illuminates how students’ nationalities, genders, and ethnicities influenced different uses of spaces in the classroom physical environment, as well as the effectiveness (or not) of the learning activities proposed by tutors and lecturers. Being aware of cultural differences and learning how to treat students equally (Skordi and Fraser 2019 ), helping to avoid isolation and prejudice, and supporting diverse relationships are relevant elements in improving the quality of students’ learning experience (Arkoudis et al. 2019 ), but they still represent a challenge for educators and universities.

The massive use of technology by students and teachers has been mainly outcomes-focused and does not seem to support more participatory or creative activities, as observed by Henderson et al. ( 2017 ). Technology should be able to recreate learning “spaces” that allow the interaction and collaboration required by students (Asino and Pulay 2019 ; Fisher 2019 ). Furthermore, the short attention span of students, incentivized by the immediatism that information and communication technologies generate, might highlight a need to emphasize in education the importance of stopping, analysing, and reflecting before giving immediate responses to the ever more complex solutions to the problems that the world is facing (Coll and Monereo 2010 ).

In this study, we sought understanding of how learning environments–involving physical, pedagogical, and psychosocial dimensions–influence students’ learning experiences in an Australian Faculty of Business and Economics. The study has deepened understanding of the dynamic and complex intrinsic interrelations of learning environment dimensions over time, addressing previous gaps in research (Acton 2018 ; Chambliss and Taracs 2014 ; Cleveland and Fisher 2014 ; Haggis 2009 ; Skordi and Fraser 2019 ). We also identified and analysed spaces and practices, educational activities, and students’ subjective experiences in different learning environments to illustrate how these multiple elements intersect and influence students’ experience. Also, the protocol of mixed methods used in the research contributed to uncovering a broader view of the learning environment and its interdependent influences over time on the students’ learning experiences.

The importance of learning environments in higher education continues to gain momentum. One implication that is clear is that any strategies to support a more holistic student learning experience through more effective use of learning environments should be developed at an institutional level (Day 2009 ). Constraints, such as tight subject organisation and high-stakes examinations stimulate rote learning and anxiety, which are detrimental to the student learning experience (Mishra et al. 2013 ). Flexibility, independence, responsibility, creativity (Illeris 2009 ), and self-directed learning (Fisher 2019 ), among other skills demanded by modern society (OECD 2019 ), are equally hindered by those institutional powers. This discussion goes beyond learning environments, but considering the relevance of business schools for preparing socially-responsible and ethical organisational leaders for society (Thomas and Cornuel 2011 ), especially in face of the COVID-19 crisis, we highlight the relevance of this debate.

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Closs, L., Mahat, M. & Imms, W. Learning environments’ influence on students’ learning experience in an Australian Faculty of Business and Economics. Learning Environ Res 25 , 271–285 (2022). https://doi.org/10.1007/s10984-021-09361-2

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Effects of Classroom Environment for Improving Students’ Learning at Secondary Level in Punjab Province, Pakistan Gulzar Ahmed 1* , Muhammad Tayyub 2 , Rubina Ismail 3

1 Department of Teacher Education, Sheikh Ayaz University, Shikarpur, Sindh, Pakistan

2 EST, Punjab School Education Department, District Bahawalnagar, Pakistan

3 M. Phil Scholar (Education), University of Lahore(Pakpattan Campus), Punjab, Pakistan

* Corresponding author: Dr.Gulzar Ahmed

Citation: Ahmed G, Tayyub M, Ismail R (2020) Effects of Classroom Environment for Improving Students’ Learning at Secondary Level in Punjab Province, Pakistan. Sci Academique 1(1): 2-15.

Received date: 27 September, 2020; Accepted date: 21 October, 2020; Publication date : 27 October, 2020

Class environment plays leading role in uplifting students’ learning and achieving national educational goals in due course of time. The aim of this present study was to explore effects of classroom environment for improving students’ learning at secondary level and recommend measures for its further improvement. The population of study comprised on all teachers teaching at secondary level and students studying in secondary classes in total 194 (105M+89F) public sector secondary schools of district Bahawalnagar, Punjab province, Pakistan. 120 (60M+60F) teachers and 120 (60 boys +60 girls) students were selected from population through simple random technique. As the study was descriptive type in nature, so a self-developed questionnaire comprising on Five – point Likert scale was applied to collect requisite information from respondents. The collected data was later on organized, tabulated, analyzed and interpreted by using descriptive statistics comprising on frequency score, percentages, mean score, standard deviation and t-value. It was found from the results of the study that availability and use of physical and instructional classroom environmental facilities including ICT innovative instructional material in classrooms prove helpful indicators for improving students’ learning outcomes at secondary level.

Keywords : Classroom learning environment; Teaching Methods; ICT Instructional Material; Teacher Training; Students’ learning

Introduction

Class environment refers to utilization of available physical, instructional facilities and maintenance of discipline in classroom for effective teaching and better students’ learning (Williams, 2016). It is an amalgamation of internal and external factors like curriculum, methods of teaching, teachers’ behavior and interaction with students, learning atmosphere, academic and social environment and support services used in classroom for teaching and learning process (Jawaid&Aly 2014). It is a wide variety of techniques and skills used in classrooms enables teachers to keep students attentive, organized and actively participating in classroom activities to produce productive results (Arshadet al. 2018). It includes planning, organizing, communicating and mentoring. It also demands teachers’ professionalism, taken of initiatives, dedication, devotion, job commitment, and willingness to adjust themselves at students’ socio-cultural and intellectual caliber (Abel, 2011).

Students’ learning is a systematic process of obtaining knowledge or skills carried out in or out of educational institutions through formal or non-formal systems of education (Lawal, 2014). Use of various pedagogical skills in classrooms produce fruitful results for improving quality of teaching and enhancing students’ learning which is a major factor in uplifting students’ academic performance (Ramli, et al. 2014). It comprised on social and physical environments of educational institutions. Social environment refers to interaction between teachers and students and their active participation in classroom teaching and learning activities, whereas physical environment consists on physical facilities includes classroom design, classroom decoration, lighting, color, ventilation, seating arrangements and ICT related instructional equipment (Earthman,2002, Tanner &Lackney, 2006).

Teachers use various teaching methods like lectures, discussion in classrooms for making teaching and learning process effective and more productive. Discussion method is process of students talking about a specific topic, helped them to share their ideas openly which develops them confidence building habit and improves their learning (Ten Have, 2017). It helps in provision of better learning environment which improves students’ learning (Malik & Rizvi, 2018). Activity-based method is another teaching method helped teachers to engage students by their active and physique participation in learning activities. In this method students’ learning ratio improved by their doing practically (Mishra & Koehler, 2016). Students’ actively take part in activity-based learning which develop their interest in studies and they learn willingly (Finn, 2010; Noreen, 2018). It was also found that deductive method of teaching was more effective for overcrowded class in public schools (Shahzadi, 2019). Students taught through accelerated learning method produced good results due to taking keen interest in studies as they have high level of knowledge to solve problems related to cognitive learning domain from knowledge level to evaluation level (Shams, Arshad & Ahmed, 2019).

Classroom environment has a positive impact on students’ academic achievement, as by provision of physical facilities like furniture, electric supply, painted walls, drinking water, models, charts, overhead projector and other ICT related instructional material, students take much interest in classroom activities which help them to get high marks in examinations (Kausar,Kiyani& Suleman.2017). Provision of physical facilities to schools like well-equipped library, clean drinking water, well-furnished classroom, laboratory with related appliances are the main factors play vital role for better teaching and uplifting students’ learning (Omae, et al. 2017). School support facilities like I.T Lab, tablet, first aid box, classrooms having ventilation, store room, cooling and heating systems, staff room, well equipped library with adequate books plays vital role for provision of quality teaching and learning (Arshad, Ahmed &Tayyab, 2019). It was found that lack of conducive classroom environment; non-supportive teachers’ attitude, lack of pedagogical skills and students’ disruptive behavior create hindrances for effective teaching and better students’ learning (Ahmed, Faizi& Akbar, 2020).

Provision of conducive classroom environment to both teachers for effective teaching and for students to have qualitative and productive learning is the primarily responsibility of the state government. Provincial governments in Pakistan were made responsible through constitutional powers to provide maximum physical and instructional facilities to the educational institutions at all levels to achieve educational goals in due course of time. But it is a fact that majority of secondary schools in Pakiatan with reference to district Bahawalnagar, Punjab province were still lacking educational facilities, which badly affecting the whole process of education including students” learning outcomes. So the required objectives for improving institutional classroom environment and improving students’ learning have not been achieved so for. There are many factors accountable for down gradation of students’ learning outcomes in the region, so it was decided to conduct a research study on “Effects of classroom environment for improving students’ learning at secondary level in Punjab Province, Pakistan.

Study Objectives

The present study was designed to achieve following objectives;

• To explore classroom environment.
• To identify factors of students’ learning.
• To find out effects of classroom environment on improving students’ learning at secondary level.

Research Questions

For this purpose, answers were sought the following research questions;

• What is classroom environment?
• What are factors of students’ learning?
• How classroom environment effect students’ learning at secondary level?

Review of Related Literature

Classroom environment comprised on social, physical and emotional factors help in facilitating teaching and learning process for achieving educational objectives. It is the responsibility of teachers to develop positive classroom environment for enriching students’ learning, as it promotes teaching and learning processe (Bierman, 2011). It refers to educational concepts, social and physical contexts, cognitive domains, instructional tools, teaching methods, teachers’ students interaction etc. (Patrick, Ryan & Kaplan, 2007). It helps teachers to draw students’ attention to take interest in studies by their actively participation in classroom activities, hopeful in developing teachers-pupils’ interrelationship and solves students’ academic problems (Bandello, 2015). Socio economic condition of parents, infrastructure of school, education of parents, classroom setting, positive attitude of teachers, supporting attitude of management attitude, students’ motivation, students’ willingness to learn are the major factors affect students’ learning (OECD, 2012).

Classroom environment contained on proper lighting system, bright atmosphere, use of ICT instructional technologies, cupboards and shelves, electric power supply, air coolers or ceiling fans, audio-visual aids in classrooms play active role in improving students’ achievement. Whereas, un-conducive classroom environment consists on small classroom size, in-appropriate ventilations, high classroom temperature, lack of ICT instructional teaching aids, in-appropriate desks, improper seating arrangements, lack of fresh air and overcrowded classrooms prove negative impact on students’ performance (Umar, 2017). Provision of sufficient physical and instructional facilities in classrooms are the major factors found to have positive impact on improving students’ learning (Kilel, 2012), as it is necessary for uplifting students’ learning outcomes. Use of teachers’ better communication skills and a variety of teaching methods in classrooms are major factors to improve students’ learning. Relaxed and friendly learning environment is important for enhancing students’ learning (Sulaiman, Mahbob, &Azlan, 2011).

Conducive learning environment is that environment, allow students to learn more easily (Encyclopedia Britanica, 2010). It is an environment provides conditions make it easy for the students to work (Longman English Dictionary, 2010). It also further has been defined that conducive learning environment is; “The environment that satisfies the needs of its participants, not only in the acquisition of numeracy and literacy skills, but is also able to link the economic and occupational needs of the group to literacy with their learning activities” (Khalid, 2008).

Conducive classroom environment helps both teachers to teach effectively and students to learn with ease and perform better academically. Use of proper available teaching and learning resources in classrooms enhances learning outcomes of students. It has positive impacts on improving students’ learning (Qamar et al. 2018). It is comprised on various components like room size, lighting, temperature, walls, ventilation, whiteboards, mats, seats, floor, PCs and other material prove fruitful effects on students’ learning (Suleman & Hussain, 2014). School facilities like school buildings, electricity, natural/artificial lighting and ventilation in classrooms, drinking water, wash rooms and playground were the main attributes to improve students’ learning (Awan, 2018). Students’ academic achievement in well-furnished and small class size room with better facilities was found better than students having large class size classes (Olufemii& Olayinka, 2017).

Teaching methods indicate strategies teachers use in classrooms for delivering his/her lessons to students based on curriculum instructional objectives to be achieved for promoting students’ learning outcomes (Buseri&Dorgu, 2011).Use of various teaching methods, skills, techniques, pedagogical approaches and instructional strategies in classrooms help teachers for effective teaching and facilitate students in clear understanding of lesson which further prove major ingredients for improving their learning (Chen, Zeng & Yang, 2010). These methods include lecture, discussion, questioning, team work, talk chalk, field trip, modeling, simulation, dramatic, role-playing, inquiry, discovery, demonstration, Dalton plan, programmed learning, experimentation, project, microteaching and use of various learning methods in classrooms. They not only help teachers to teach effectively but help students for enhancing their learning which at last improves their academic performance (dorgu, 2015).

Use of instructional and information technology during classroom instruction plays an important role for achieving students’ successful and fruitful high academic achievement (Iqbal, 2005). Quality of students’ learning be promoted by using multimedia, computer, charts, projectors, graphs, internet, maps, mock ups and other related ICT materials in classes. But advanced innovative ICT pedagogical soft wares and devices are not properly used in classes due to lack of teachers’ professional trainings, badly effects students’ learning (Weiss, 2007; Oliver &Limpman, 2007; Suleman et al. 2011). It was revealed that use of information and communication technological (ICT) related devices like laptop, computers, multimedia, smartphones, projectors, tablets and LCD in classrooms, offices and home by teachers and students brought revolutionized changes in teaching, evaluation, assessment and learning process. In this regards presentations shown to students in classrooms through LCD projector made classroom teaching and learning activities more attractive, productive and produce fruitful results in uplifting students’ learning (Ahmed, Arshad & Tayyab, 2019). It is concluded that teachers’ effective use of ICT tools can prove helpful for improving students’ learning (Imran, Mahmood &Ahmed. 2020).

Conducive classroom environment has positive effects on teachers’ effective teaching and better students’ learning outcomes. It includes floor, walls decorated with charts, maps, windows, chairs or desks, white boards, LCD or computers, dices and cupboards. If students are satisfied and feeling comfortable in classrooms, then they produce excellent academic performance as compared to uncomfortable classrooms, which can demoralize students and they show poor learning outcomes (Fisher, 2008). Well-organized, equipped and facilitated classroom environment has a positive effect on academic achievement of students (Suleman, Aslam & Hussain, 2014). It brings changes in students’ behaviors which indirectly improves students’ results (Isaiah, 2013). It was further found from research that classroom environment has a significant impact on students’ academic performance (Akomolafe&Adesua, 2015). It was also revealed from another research findings of the study that conducive classroom promotes students’ learning (Mancaet al. 2020).

Material and Methods

Research Design

The design of this current research study was descriptive type in nature, so survey method was applied to answer research questions for achieving study objectives. It is the most suitable method used in social sciences research studies for properly elaboration of characteristics and variation of population, which helps to describe samples as per demands of the study for further description of educational phenomena (Gay, Mills &Airasian, 2009; Leob et al., 2017).

Population in social sciences research studies is comprised on the largest targeted group of people have requisite qualities meeting criteria for collection of relevant information (Asiamah, Mensah &Oteng, 2017. It is also consisted on entire set of cases from which required sample to be drawn for research investigational purposes (Alvi, 2016; Mills & Gay, 2018). The Bahawalpur Division of Punjab province (Pakistan), comprised on three districts namely District Bahawalpur, Bahawalnagar and Rahim Yar Khan. District Bahawalnagar is randomly selected as population of the study, as it was easy approach for the researchers to collect required information from respective respondents.

All teachers 3739 (2064M+1675F) serving in 194 (105M+89F) public sector secondary schools (urban & rural) and students 110662 (66088M+47574) studying at secondary level in these schools of Bahawalnagar district were taken as population of the study. Further detail of the distribution of the population was tabulated below;

Table 1: Distribution of Population.

Sampling is a process of selection of respondents from group of people following criteria that persons representing larger group from which they were taken (Best, 2016). It is a subset of population showing complete group, used to make inferences regarding population characteristics or making generalization with reference to population. It is comprised on a group of small number of respondents selected from a population for research purposes. In this this study, simple random sampling technique was followed for selection of sample from population, as each and every member of the population in this type of sampling technique has equal chances to be selected for sample of the study (Alvi, 2016; Mills & Gay, 2018).

Out of total population, 120 (60M+60F) teachers teaching secondary classes and 120 (60M+60F) students studying in the above classes at 30 (15M+15FM) schools (urban &rural) areas of Bahawalnagar district were selected as sample of study. Thus,total sample of this research study was comprised on 240 (120M+120F) respondents. The distribution of the sample was further tabulated below;

Table 2: Sample Distribution.

Tool for Collection of Data

To answer research questions and achieving research study objectives, a self- developed questionnaire, validated through pilot testing and administered to collect requisite information from respondents. In social sciences survey research studies, it is considered most suitable tool, helps researchers for assessing large population perceptions to be assessed with relative ease on individuals’ perspectives. Furthermore, it is a systematic compilation of questions subject for sampling of population used for receiving requisite information from respondents (Mills & Gay, 2018).

In present research study, a questionnaire comprised on five points Likert scale, having on three parts consisted on 31 items related to research study was applied to collect requisite data from respondents. Questionnaire’s part-1 used for collection of demographic information of respondents, part-11 was used for exploring respondents’ perceptions regarding classroom environment and students’ learning, whereas part-111 of the questionnaire was used for finding out effects of classroom environment on improving students’ learning at secondary level. To collect data, researcher personally visited each sampled educational institutions and after obtaining permission from respective school heads, questionnaire was administered to sampled respondents by requesting to fill them as per prescribed given guidelines and researcher also helped them during this process. So in this regards, positive responses were collected from respondents as per demands of the research study.

The information received from respective respondents through questionnaire was tabulated by application of required statistical tools for achieving objectives of the research study like frequency score, percentage, mean score, standard deviation and t, test for data analysis. Results detailed descriptions were tabulated in the following tables;

Table 3 : Teachers use activity-based method in classrooms for improving students’ learning.

This table indicates that 67.5% (41.7%+25.8%) teachers & 58.4% (36.7%+21.7%) students were agreed, whereas 26.7% (11.7%+15.0%) teachers & 36.7% (15.0%+21.7%) students were disagreed with the statement that teachers use activity based method in classrooms for improving students’ learning. Teachers’ mean score (3.67) is greater than the mean score (3.37) of students. The standard deviation increases from (1.485) to (1.608) and t-value (3.262) is significant at (0.05) for teachers and students shows the agreement of the statement that majority (67.5%) of teachers use activity based method in classrooms for improving students’ learning.

Table 4: Teachers use discussion method in classrooms to involve students for better learning.

The above table describes that 73.3% (35.0%+38.3%) teachers & 63.3% (25.8%+37.5%)students were agreed, whereas 16.7 % (5.0%+11.7%) teachers &26.7% (12.5%+14.2%) students were disagreed with the statement that teachers use discussion method to involve students for better learning. Teachers’ mean score (3.80) is greater than the mean score (3.48) of students. The standard deviation increases from (1.294) to (1.372) and t-value (4.130) is significant at 0.00 for teachers and students. So, it shows that there is a significant difference between teachers and students’ responses. It was also found that majority (73.5%) of respondents show agreement that teachers use discussion method to involve students for better learning.

Table 5: Teachers use lecture method in classrooms for teaching students effectively.

Table 5 shows that 65.8% (40.8%+25.0%) teachers & 71.6% (45.8%+25.8students were agreed, whereas 27.5% (12.5%+15.0%) teachers & 19.2% (10.0%+09.2%) students were disagreed with the statement that teachers use lecture method in classrooms for teaching students effectively. Teachers’ mean score (3.64) is greater than the mean score (3.89) of students. The standard deviation increases from (1.488) to (1.333) and t-value1.926) is significant at (.056) for teachers and students. So, it shows that there is a significant difference between teachers and students’ responses. It was also found that majority 65.8% of respondents show agreement that teachers use lecture method in classrooms for teaching students effectively.

Table 6: Teachers motivate students to work hard for obtaining good marks.

This table illustrates that 69.2% (49.2%+20.0%) teachers & 66.7% (40.0+26.7%) students were agreed, whereas, 24.2% (14.2%+10.0%) teachers & 26.7% (17.5+9.2) students were disagreed with the statement that teachers motivate students to work hard for obtaining good marks. Teachers’ mean score (3.84) is greater than the mean score (3.71) of students. The standard deviation decrease from (1.420) to (1.387) and t-value(1.150) is significant at (.252) for teachers and students. So, it shows that there is a significant difference between teachers and students’ responses. Furthermore, majority (69.2%) of the teachers motivate students to work hard for obtaining good marks.

Table 7: Teachers motivate students to maintain discipline in classroom for better students’ learning.

Table 7 shows that 70.9% (39.2% + 31.7%) teachers & 64.2% (39.2%+25.0%) students were agreed, whereas, 24.1% (8.3%+15.8%) teachers & 32.5% (16.7%+15.8%) students were disagreed with the statement that teachers motivate students to maintain classroom discipline for effective teaching and better students’ learning. Teachers’ mean score (3.70) of teachers is less than the mean score (3.55) of students. The standard deviation increases from (1.459) to (1.528) and t-value(.925) is significant at (.357) for teachers and students. So, it shows that there is a significant difference between teachers and students responses. Furthermore, majority (70.9%) of the teachers motivate students to maintain classroom discipline.

Table 8: Sufficient Furniture is available in classrooms.

The above table indicates that 32.5% (18.3%+14.2%) teachers & 43.3% (25.8%+17.5%) students were agreed, whereas 61.7% (30.0%+31.7%) teachers & 50.8% (25.8%+25.0%) students were disagreed with the statement that furniture is available in classrooms. Teachers’ mean score (2.57) is less than the mean score (2.93) of students. The standard deviation increase from (1.510) to (1.576) and t-value(-3.705) is significant at (.000) for teachers and students. So, it shows that there is a significant difference between teachers and students’ responses, as majority 61.7% of respondents were of the opinion that classrooms were lacking sufficient furniture.

Table 9: Proper arrangements of ventilations system are available in classrooms.

This table illustrates that 75.8% (40.8%+35.0%) teachers & 72.5% (39.2%+33.3%) students were agreed and 19.2% (6.7%+12.5%) teachers & 22.5% (7.5%+15.0%) students were disagreed with the statement that proper arrangements of ventilations system are available in classrooms. Teachers’ mean score (3.85) is less than the mean score (3.74) of students. The standard deviation decrease from (1.358) to (1.429) and t-value (1.961) is significant at (.052) for teachers and students. So, it shows that there is a significant difference between teachers and students responses. Furthermore, majority (75.8%) of the respondents were of opinion that proper arrangements of ventilations system are available in classrooms.

Table 10: Facility of AV aids is available in the classrooms.

The above table shows that 68.3% (40.8% + 27.5 %) teacher& 63.3% (37.5%+25.8%) students were agreed,whereas, 25% (10.0%+15.0%) teachers & 27.5%(15.0%+12.5%) students were disagreed with the statement that facility of A.V. aids is available in the classrooms. Teachers’ mean score (3.69) is greater than the mean score (3.61) of students. The standard deviation decrease from (1.466) to (1.434) and t-value (.592) is significant at (.538) for teachers and students. So, it shows that there is a significant difference between teachers and students’ responses, as majority (68.3%) of the respondents were of the opinion that facility of AV aids is available in the classroom.

Provision of conducive classroom environmental facilities to educational institutions at secondary level help teachers to teach well in classrooms which ultimately prove to be a major ingredient for improving students’ learning (Williams, 2016). Results of many research studies previously conducted indicated that teachers’ use of various teaching methods like lectures, discussion method, activity-based method, deductive method help teachers to engage students for actively participating in classroom activities which improve their learning outcomes(Ten Have, 2017; Mishra& Koehler, 2016; Finn, 2010; Noreen, 2018). It was also found from results of this study that majority (67.5%) of respondents were of the opinion that teachers’ use of various teaching methods in classrooms like activity-based method, lectures method and discussion help teachers to teach well which ultimately improves students’ learning. It was further found from findings of various studies conducted in past that provision of physical facilities like furniture, electric supply, painted walls, drinking water, models, charts, overhead projector and other ICT related instructional material, students take much interest in classroom activities which improves their academic achievements (Kausar,Kiyani& Suleman.2017). School support facilities like I.T Lab, tablet, first aid box, classrooms having ventilation, store room, cooling and heating systems, staff room, well equipped library with adequate books plays vital role for provision of quality teaching and learning (Arshad; Ahmed. &Tayyab,2019). It further found that lack of conducive classroom environment; non-supportive teachers’ attitude, lack of pedagogical skills and students’ disruptive behavior create hindrances for effective teaching and better students’ learning (Ahmed, Faizi& Akbar.2020). It was also found from results of this study that majority (68.3%) respondents were of the opinion that teachers’ use ofA.V. aids is available in the classrooms draw students’ attention to take keen interest in classroom activities which enriches their leaning skills.

It was concluded that conducive classroom environment has positive effects on students’ learning outcomes. Availability and teachers’ use of physical and advanced innovative ICT related instructional facilities, teaching methods in classrooms draw students’ attention to actively participate in classroom activities which ultimately improves their learning. It includes furniture, electric supply, drinking water, models, charts, well-furnished classrooms, laboratory, I.T Lab, tablet, first aid box, ventilation, store room, cooling and heating systems, staff room, and well equipped library, room size, temperature, ventilation, whiteboard, mats, seats, floor, natural/artificial lighting, wash rooms facilities and playground, education of parents, parents’ socio-economic conduction, classroom setting, positive attitude of teachers, supporting attitude of management, students’ motivation, students’ willingness to learn, variety of teaching styles were the main attributes to improve students’ learning. It was further concluded that due to lack of conducive classroom environment; non-supportive teachers’ attitude, lack of pedagogical skills and students’ disruptive behavior create hindrances for effective teaching and better students’ learning.

Recommendations

To uplift students’ learning at secondary level, it is recommended that;

• Physical classroom environmental and advance innovative ICT related instructional facilitates may be provided to schools on priority basis.
• Short term ICT refresher training courses facilities may be provided to teachers for properly using advance instructional technology in classrooms.
• Students may be motivated to actively participate in classroom activities.
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