- Research article
- Open access
- Published: 04 December 2018
The relationship between learning styles and academic performance in TURKISH physiotherapy students
- Nursen İlçin ORCID: orcid.org/0000-0003-0174-8224 1 ,
- Murat Tomruk 1 ,
- Sevgi Sevi Yeşilyaprak 1 ,
- Didem Karadibak 1 &
- Sema Savcı 1
BMC Medical Education volume 18 , Article number: 291 ( 2018 ) Cite this article
Learning style refers to the unique ways an individual processes and retains new information and skills. In this study, we aimed to identify the learning styles of Turkish physiotherapy students and investigate the relationship between academic performance and learning style subscale scores in order to determine whether the learning styles of physiotherapy students could influence academic performance.
The learning styles of 184 physiotherapy students were determined using the Grasha-Riechmann Student Learning Style Scales. Cumulative grade point average was accepted as a measure of academic performance. The Kruskal-Wallis test was conducted to compare academic performance among the six learning style groups (Independent, Dependent, Competitive, Collaborative, Avoidant, and Participant).
The most common learning style was Collaborative (34.8%). Academic performance was negatively correlated with Avoidant score ( p < 0.001, r = − 0.317) and positively correlated with Participant score ( p < 0.001, r = 0.400). The academic performance of the Participant learning style group was significantly higher than that of all the other groups ( p < 0.003).
Although Turkish physiotherapy students most commonly exhibited a Collaborative learning style, the Participant learning style was associated with significantly higher academic performance. Teaching strategies that encourage more participant-style learning may be effective in increasing academic performance among Turkish physiotherapy students.
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Learning can be defined as permanent changes in behavior induced by life [ 1 ]. According to experiential learning theory, learning is “the process whereby knowledge is created through the transformation of experience” [ 2 , 3 ].
Facilitating the learning process is the primary aim of teaching [ 4 ]. Understanding the learning behavior of students is considered to be a part of this process [ 5 ]. Therefore, the concept of learning styles has become a popular topic in recent literature, with many theories about learning styles put forward to better understand the dynamic process of learning [ 2 , 3 ].
Learning style refers to an individual’s preferred way of processing new information for efficient learning [ 6 ]. Rita Dunn described the concept of learning style as “a unique way developed by students when he/she was learning new and difficult knowledge” [ 7 ]. Learning style is about how students learn rather than what they learn [ 1 ]. The learning process is different for each individual; even in the same educational environment, learning does not occur in all students at the same level and quality [ 8 ]. Research has shown that individuals exhibit different approaches in the learning process and a single strategy or approach was unable to provide optimal learning conditions for all individuals [ 9 ]. This may be related to students’ different backgrounds, strengths, weaknesses, interests, ambitions, levels of motivation, and approaches to studying [ 10 ]. To improve undergraduate education, educators should become more aware of these diverse approaches [ 11 ]. Learning styles may be useful to help students and educators understand how to improve the way they learn and teach, respectively.
Determining students’ learning styles provides information about their specific preferences. Understanding learning styles can make it easier to create, modify, and develop more efficient curriculum and educational programs. It can also encourage students’ participation in these programs and motivate them to gain professional knowledge [ 9 ]. Therefore, determining learning style is quite valuable in order to achieve more effective learning. Researching learning styles provides data on how students learn and find answers to questions [ 5 ].
Considering the potential problems encountered in the undergraduate education of physiotherapists, determining the learning style of physiotherapy students may enable the development of strategies to improve the learning process [ 12 ]. Studies on learning styles in the field of physiotherapy have mostly been conducted in developed countries such as Canada and Australia [ 13 , 14 ]. A study conducted in Australia examined the learning styles of physiotherapy, occupational therapy, and speech pathology students. The results of this study suggest that optimal learning environment should also be taken into consideration while researching how students learn. The authors also stated that future research was needed to investigate correlations between learning styles, instructional methods, and the academic performance of students in the health professions [ 14 ].
To the best of our knowledge, there are no prior publications in the literature that report Turkish physiotherapy students’ learning styles. Furthermore, previous studies mostly used Kolb’s Learning Style Inventory (LSI), Marshall & Merritts’ LSI, or Honey & Mumford’s Learning Style Questionnaire (LSQ) to assess learning styles [ 5 , 13 , 15 , 16 , 17 , 18 ]. Some of these studies also suggested that learning behavior and styles should be investigated using different inventories [ 5 ]. Moreover, a scale that was indicated as valid and reliable for Turkish population was needed to accurately determine the learning styles of Turkish physiotherapy students. Therefore, we opted to use the Grascha-Riechmann Learning Style Scales (GRLSS) to assess the learning styles of physiotherapy students, which will be a first in the literature.
Learning style preferences are influential in learning and academic achievement, and may explain how students learn [ 19 ]. Previous studies have demonstrated a close association between learning style and academic performance [ 20 , 21 ]. Learning styles have been identified as predictors of academic performance and guides for curriculum design. The aim of this study was to determine whether learning style preferences of physiotherapy students could affect academic performance by identifying the learning styles of Turkish physiotherapy students and assessing the relationship between these learning styles and the students’ academic performance. Since physiotherapy education mainly consists of practice lessons and clinical practice and mostly requires active student participation, we hypothesized that physiotherapy students with a Collaborative learning style according to the GRLSS would have higher academic performance.
A cross-sectional survey design using a convenience sample was used. The study population consisted of 488 physiotherapy students who were officially registered for the 2013–2014 academic year in Dokuz Eylul University (DEU) School of Physical Therapy and Rehabilitation. A minimum sample size of 68 participants was calculated with 95% confidence interval and 80% power by using “Epi Info Statcalc Version 6”. Inclusion criteria were (i) age ≥ 17 years, (ii) official registration in DEU School of Physical Therapy and Rehabilitation for the 2013–2014 academic year, (iii) being a first-, second-, third-, or fourth-year undergraduate student of physiotherapy, (iv) ability to read, write, and understand Turkish, and (v) being willing and able to participate in the study. Exclusion criteria were (i) unwilling to participate in the study, (ii) inability to read, write, and understand Turkish. The questionnaire was distributed to the physiotherapy students in a classroom setting during the final exam week of the academic year. Due to the absence of participants who did not attend final exams and were not actively attending classes (non-attendance students), questionnaires were distributed to 217 students in total.
184 physiotherapy students with a mean ± SD age of 21.52 ± 1.75 years participated in the study. Participants were informed verbally and in writing about the purpose of the study and the survey that would be implemented. A research assistant was available in the classroom to provide assistance if required. Demographic characteristics (age, gender, undergraduate year) comprised the first section of the questionnaire, followed by the GRLSS to assess learning style.
Cumulative grade point average (CGPA) shown on the students’ transcripts was used as the measure of academic performance. The students’ CGPAs at the end of the 2013–2014 academic year were obtained from the records held in the student affairs unit of the DEU School of Physical Therapy and Rehabilitation. CGPA was derived by multiplying the grade point (out of 100) with the credit units for each module or course and then dividing the total sum by the total credit units taken in the program.
The local university ethics committee provided ethical approval and informed consent was obtained from the participants before inclusion. Ethical protocol number was 1432-GOA.
Grasha-riechmann student learning style scales.
The GRLSS is a five-point Likert-type scale ( response format: strongly disagree, moderately disagree, undecided, moderately agree, strongly agree ) consisting of 60 items which was designed based on student interviews and survey data [ 22 , 23 ]. In accordance with the response to student attitudes toward learning, classroom activities, teachers and peers, six learning styles were defined [ 24 ]. Learning styles that form subscales are the Independent, Avoidant, Collaborative, Dependent, Competitive, and Participant learning styles [ 24 , 25 ]. The six main styles in the GRLSS are described in Table 1 and the scoring of the GRLSS is shown in Table 2 [ 23 , 24 ]. The GRLSS was adapted to Turkish in 2003 and found to have good reliability [ 25 ] (Table 3 ).
The learning styles of the physiotherapy students in the current study were identified according to GRLSS and the students were grouped based on their predominant (highest scoring) style. The mean and median academic performance values of each group were calculated and the significance of the differences between groups was statistically analyzed.
Statistical analyses were performed to compare academic performances among the learning style groups and test the significance of pairwise differences. All data were analyzed using Statistical Package for Social Science software (IBM Corporation, version 20.0 for Windows). Descriptive statistics were summarized as frequencies and percentages for categorical variables. Continuous variables were presented as mean and standard deviation when normally distributed and as median and interquartile range when not normally distributed. Mann-Whitney U test was used for between-group analyses of abnormally distributed variables. The variables were investigated using visual (histograms, probability plots) and analytical methods (Kolmogorov-Smirnov/Shapiro-Wilk test) to determine whether they showed normal distribution. As parameters were not normally distributed, the correlation coefficients and their significance were calculated using Spearman test. Strength of correlation was defined as very weak for r values between 0.00–0.19, weak for r values between 0.20–0.39, moderate for r values between 0.40–0.69, strong for r values between 0.70–0.89, and very strong for r values over 0.90 [ 26 ]. As the academic performance was not normally distributed, the Kruskal-Wallis test was conducted to compare this parameter among the six learning style groups. The Mann-Whitney U Test was performed to test the significance of pairwise differences using Bonferroni correction to adjust for multiple comparisons. An overall 5% type-I error level was used to infer statistical significance ( p < 0.05).
A total of 217 physiotherapy students were invited to participate in the study. Eighteen students refused to participate. Fifteen surveys were discarded due to missing item responses. As a result, data obtained from 184 students were used for the analyses. Overall response rate was 84.8%.
Demographic characteristics (gender, year) and learning style preferences are presented in Table 4 . The most common learning styles among the physiotherapy students according to the GRLSS were Collaborative (34.8%) and Independent (22.3%). The results of GRLSS subscale scores were given in Table 5 . The highest subscale score was Collaborative (Mean ± SD = 3.57 ± 0.62), while Competitive score was the lowest (Mean ± SD = 2.81 ± 0.69).
A moderate positive correlation between academic performance and Participant score was found (p < 0.001, r = 0.400) . A weak negative correlation was also found between academic performance and Avoidant score (p < 0.001, r = − 0.317) . No other significant correlation between academic performance and subscale scores was found (Table 6 ) .
When students were grouped according to learning styles, between-group (Kruskal-Wallis) analysis showed a significant difference in the academic performance of the groups (p < 0.001). Post-hoc (Mann-Whitney U) analysis revealed significantly higher academic performance in the Participant learning style group compared to all of the other learning style groups (Independent, Avoidant, Collaborative, Dependent, and Competitive) (Table 7 ).
The current study assessed the learning styles of Turkish physiotherapy students, and investigated the relationship between their learning styles and academic performance. The results revealed that the Collaborative learning style was most common among the Turkish physiotherapy students. However, students with Participant learning style had statistically higher academic performance when compared to the others. In addition, we found a positive correlation between Participant score and academic performance of the students, which supports the previous finding, while a negative correlation was found between Avoidant score and academic performance. In the case of physiotherapy students in this study, the emphasis should be on developing Participant and Collaborative learning skills. This might involve providing more class activities, discussions, and group projects.
The physiotherapy program at DEU has a combined case study-based and traditional style curriculum including lectures, tutorials, seminars, case study presentations, and supervised small group clinical practice in the hospital and at other health centers. Learning tasks and assessment methods include individual written examinations, practical examinations, homework and assignments as well as collaborative oral presentation and research projects. In the physiotherapy discipline, clinical practice improves students’ occupational skills and is seen as a crucial part of the teaching process [ 12 , 27 ]. Similarly, the teaching and learning approach at DEU is heavily based on practical training and requires active participation and group work. This could be a reason for the greater preference for Collaborative learning style.
Previous studies have indicated that physiotherapy students prefer abstract learning styles [ 28 ] and have desirable approaches to studying [ 29 ]. Canadian and American physiotherapy students preferred Converger (40 and 37% respectively) or Assimilator (35 and 28% respectively) learning styles [ 13 ]. According to descriptions of the learning style categories in the Kolb LSI, Convergers enjoy learning through activities like homework problems, computer simulations, field trips, and reports and demonstrations presented by others. On the other hand, Assimilators prefer attending lectures, reading textbooks, doing independent research and watching demonstrations by instructors when learning. In our study, Turkish physiotherapy students preferred Collaborative (34.8%) or Independent (22.3%) learning styles. According to GRLSS, Collaboratives prefer lectures with small group discussions and group projects (similar to Assimilators), while Independents prefer self-pace instruction and studying alone (similar to Convergers). Therefore, it can be concluded that learning styles of Canadian, American, and Turkish physiotherapy students are similar to each other.
Katz and Heimann used the Kolb LSI in their study and reported average learning style scores instead of the number of students in each of the four learning styles. They reported Converger as the “average” learning style for physiotherapy students [ 30 ]. In our study, the largest proportion of the physiotherapy students had a Collaborative learning style. Moreover, the average learning style was also Collaborative, with the highest average score.
Competitive learning style was the least frequently preferred (5.4%) by Turkish physiotherapy students in our study. The low preference for Competitive learning style indicates that students were less likely to compete with other students in the class to get a grade. Mountford et al. assessed learning styles of Australian physiotherapy students using Honey & Mumford’s LSQ and found that the Pragmatic learning style was the least preferred. According to LSQ, Pragmatists tend to see problem solving as a chance to rise to a challenge [ 31 ]. Considering that both Competitives and Pragmatists like challenges, the least frequently preferred styles of Australian and Turkish physiotherapy students seem to be similar to each other.
Alsop and Ryan pointed out that “personal awareness of learning styles and confidence in communicating this are first steps to achieve an optimal learning environment” [ 32 ]. According to Kolb’s theory, a preferred learning style affects a person’s problem solving ability [ 13 ]. Wessel et al. also stated that in order to provide students the best learning opportunity, educators must be aware of the learning styles and students’ ability to solve problems [ 13 ]. Indeed, evidence supporting these views can be found in the literature. Previous studies showed that students who were aware of their learning style had improved academic performance [ 33 , 34 ]. Nelson et al. found that college students who were tested on their learning style and were given appropriate education according to their learning style profile achieved higher academic performance than other students [ 33 ]. Linares also investigated learning styles in different health care professions (physiotherapy, occupational therapy, physician assistants, nursing and medical technology) and found a significant relationship between learning style and students’ readiness to undertake self-directed learning [ 15 ]. However, Hess et al. found no association between learning style and problem-solving ability in their study [ 35 ].
While planning this study, we hypothesized that students with a Collaborative learning style would have higher academic performance. Although the Collaborative learning style was the most common, these students did not show significantly higher academic performance. However, students with Participant learning style had statistically higher academic performance when compared to the other learning style groups. Characteristics specific to the Participant learning style are enjoyment from attending and participating in class and interest in class activities and discussions. These students enjoy opportunities to discuss class materials and readings. This may suggest that increasing in-class activities and discussions, which encourage participant-style learning, is needed to increase academic performance. Another approach would be to adapt teaching strategies according to the characteristics of Collaboratives, as they represented the largest body of students. Creating a convenient environment in which students could spend more time sharing and cooperating with their teacher and peers may facilitate collaborative learning, thus enhancing academic performance. Organizing the curriculum to include small group discussions within lectures and incorporate group projects may also be beneficial. As Ford et al. stated, “ Identification teaching profiles could be used to tailor the collaborative structure and content delivery ” [ 36 ].
The most important reason for determining learning style is to create a proper teaching strategy [ 37 , 38 , 39 , 40 ]. However, there seems to be no exact relationship between students’ learning style and the curriculum of a program described in the literature [ 13 ]. Learning style alone is not the only factor that may influence a learning situation. Many factors (educational and cultural context of university, individual awareness, life experience, other learning skills, effect of educator, motivation, etc.) may influence the learning process [ 31 ]. Therefore, expecting a simple relationship between learning style and teaching strategy may not be realistic. Moreover, the review of Pashler et al. showed that there was virtually no evidence that people learn better when teaching style is tailored to match students’ preferred learning style [ 41 ]. Nevertheless, future studies investigating physiotherapy educators’ teaching styles and their association with learning styles and academic performance may elucidate this complex issue.
The major strength of this study is that, to the best of our knowledge, ours is the first study investigating the learning styles of Turkish physiotherapy students with relation to academic performance.
There were some limitations to this study. It should be noted that learning style is a self-reported measure that can change based on experience and the demands of a situation. Therefore, it is subjective and able to provide adaptive behavior [ 42 ]. It should also be kept in mind that the conclusions of this study could be limited due to the cross-sectional design, and respondent bias may be an issue because convenience sampling was used to recruit participants. One possible limitation of the study could be the fact that the three of the scale reliabilities reported for GRLSS was poor.
This study investigated the learning styles of physiotherapy students in only one university (DEU) and this could preclude the generalization of our results. Subsequent studies should include students enrolled in the physiotherapy departments of multiple universities in Turkey to achieve an accurate geographical representation. Moreover, future studies on this topic should be conducted in collaboration with universities in Europe, with which we share a cultural connection.
The results of this study showed that the Collaborative learning style was most common among Turkish physiotherapy students. On the other hand, the physiotherapy students with Participant learning style had significantly higher academic performance than students with other learning styles. Teaching strategies consistent with the unique characteristics of the Participant learning style may be an effective way to increase academic performance of Turkish physiotherapy students. Incorporating more in-class activities and discussions about class material and readings may facilitate Participant learning, thus impacting academic performance positively. Another approach may be to adopt teaching strategies that target the predominant Collaborative learning style. Creating a convenient environment for students to share and cooperate with their teacher and peers and organizing the curriculum to include more small group discussions and group projects may also be supportive. Future studies should investigate physiotherapy educators’ teaching styles and their relations with learning styles and academic performance.
Cumulative Grade Point Average
Dokuz Eylul University
Grascha-Riechmann Learning Style Scales
Learning Style Inventory
Learning Style Questionnaire
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The authors like to thank all physiotherapy students who participated in this study.
No funding was obtained for this study.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Authors and affiliations.
School of Physical Therapy and Rehabilitation, Dokuz Eylul University, 35340, Inciralti, Izmir, Turkey
Nursen İlçin, Murat Tomruk, Sevgi Sevi Yeşilyaprak, Didem Karadibak & Sema Savcı
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Nİ conducted the literature search for the background of the study, analyzed and interpreted statistical data, and wrote the majority of the article. MT conducted the literature search, collected data for the study, analyzed statistical data, and contributed to writing the article. SSY and DK were involved in study planning, data processing, and revising the article. SS contributed to study design and oversaw the study. All authors read and approved the final manuscript.
Correspondence to Nursen İlçin .
Nursen İlçin, PT, PhD.
İlçin graduated from Dokuz Eylul University, School of Physical Therapy and Rehabilitation in 1998. She received her Master’s degree in 2002 and PhD in 2009 from Dokuz Eylül University. She is currently a associate professor in Geriatric Physiotherapy Department.
Murat Tomruk, PT, PhD.
Tomruk graduated from the School of Physical Therapy and Rehabilitation at Dokuz Eylul University in 2009. He received his MSci degree in Musculoskeletal Physiotherapy in 2013 and his PhD degree in 2018. His doctorate thesis was about manual therapy. He works as a research assistant at Dokuz Eylul University since 2011.
Sevgi Sevi Yeşilyaprak, PT, PhD.
Sevgi Sevi Yeşilyaprak’s speciality is shoulder rehabilitation. Her primary research interests are orthopaedic and sports injuries of the shoulder, shoulder biomechanics, proprioception, and exercise. She has one active and two completed grants. Yeşilyaprak teaches courses on musculoskeletal physiotherapy including sports physiotherapy, musculoskeletal disorders, therapeutic exercises, exercise prescription, and manual physiotherapy techniques.
Didem Karadibak, PT, PhD.
Karadibak obtained her BS degree in Physiotherapy from Hacettepe University in 1992 and her MS and PhD degrees from the Physical Therapy Program of the Institute of Health and Sciences, Dokuz Eylul University in 1998 and 2003, respectively. She is currently a professor of Cardiopulmonary Rehabilitation in the Dokuz Eylul University School of Physical Therapy and Rehabilitation.
Sema Savcı, PT, PhD.
Savcı obtained her BS degree in Physiotherapy from Hacettepe University in 1988 and her MS and PhD degrees from the Physical Therapy Program of the Institute of Health and Sciences, Hacettepe University in 1990 and 1995, respectively. She is currently a professor and serving as the Head of Cardiopulmonary Rehabilitation in the Dokuz Eylul University School of Physical Therapy and Rehabilitation.
Ethics approval and consent to participate
Written ethical approval was taken from the Dokuz Eylül University’s local ethics committee (approval number 1432-GOA) and written informed consent obtained from all the participants.
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İlçin, N., Tomruk, M., Yeşilyaprak, S.S. et al. The relationship between learning styles and academic performance in TURKISH physiotherapy students. BMC Med Educ 18 , 291 (2018). https://doi.org/10.1186/s12909-018-1400-2
Received : 19 June 2018
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Published : 04 December 2018
DOI : https://doi.org/10.1186/s12909-018-1400-2
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Evidence-Based Higher Education – Is the Learning Styles ‘Myth’ Important?
The basic idea behind the use of ‘Learning Styles’ is that learners can be categorized into one or more ‘styles’ (e.g., Visual, Auditory, Converger) and that teaching students according to their style will result in improved learning. This idea has been repeatedly tested and there is currently no evidence to support it. Despite this, belief in the use of Learning Styles appears to be widespread amongst schoolteachers and persists in the research literature. This mismatch between evidence and practice has provoked controversy, and some have labeled Learning Styles a ‘myth.’ In this study, we used a survey of academics in UK Higher Education ( n = 114) to try and go beyond the controversy by quantifying belief and, crucially, actual use of Learning Styles. We also attempted to understand how academics view the potential harms associated with the use of Learning Styles. We found that general belief in the use of Learning Styles was high (58%), but lower than in similar previous studies, continuing an overall downward trend in recent years. Critically the percentage of respondents who reported actually using Learning Styles (33%) was much lower than those who reported believing in their use. Far more reported using a number of techniques that are demonstrably evidence-based. Academics agreed with all the posited weaknesses and harms of Learning Styles theory, agreeing most strongly that the basic theory of Learning Styles is conceptually flawed. However, a substantial number of participants (32%) stated that they would continue to use Learning Styles despite being presented with the lack of an evidence base to support them, suggesting that ‘debunking’ Learning Styles may not be effective. We argue that the interests of all may be better served by promoting evidence-based approaches to Higher Education.
The use of so-called ‘Learning Styles’ in education has caused controversy. The basis for the use of Learning Styles is that individual difference between learners can supposedly be captured by diagnostic instruments which classify learners into ‘styles’ such as ‘visual,’ ‘kinaesthetic,’ ‘assimilator,’ etc. According to many, but not all, interpretations of Learning Styles theory, to teach individuals using methods which are matched to their ‘Learning Style’ will result in improved learning ( Pashler et al., 2008 ). This interpretation is fairly straightforward to test, and, although there are over 70 different instruments for classifying Learning Styles ( Coffield et al., 2004 ) the current status of the literature is that there is no evidence to support the use of Learning Styles in this way ( Pashler et al., 2008 ; Rohrer and Pashler, 2012 ). This has lead to Learning Styles being widely classified as a ‘myth’ ( Geake, 2008 ; Riener and Willingham, 2010 ; Lilienfeld et al., 2011 ; Dekker et al., 2012 ; Pasquinelli, 2012 ; Rato et al., 2013 ; Howard-Jones, 2014 ).
Despite this lack of evidence, it appears that belief in the use of Learning Styles is common amongst schoolteachers – A 2012 study demonstrated that 93% of schoolteachers in the UK agree with the statement “Individuals learn better when they receive information in their preferred Learning Style (e.g., auditory, visual, kinaesthetic) ( Dekker et al., 2012 ).” A 2014 survey reported that 76% of UK schoolteachers ‘used Learning Styles’ and most stated that to do so benefited their pupils in some way ( Simmonds, 2014 ). A study of Higher Education faculty in the USA showed that 64% agreed with the statement “Does teaching to a student’s learning style enhance learning?” ( Dandy and Bendersky, 2014 ). A recent study demonstrated that current research papers ‘about’ Learning Styles, in the higher education research literature, overwhelmingly endorsed their use despite the lack of evidence described above ( Newton, 2015 ). Most of this endorsement was implicit and most of the research did not actually test Learning Styles, rather proceeded on the assumption that their use was a ‘good thing.’ For example, researchers would ask a group of students to complete a Learning Styles questionnaire, and then make recommendations for curriculum reform based upon the results.
This mismatch between the empirical evidence and belief in Learning Styles, alongside the persistence of Learning Styles in the wider literature, has lead to tension and controversy. There have been numerous publications in the mainstream media attempting to explain the limitations of Learning Styles (e.g., Singal, 2015 ; Goldhill, 2016 ) and rebuttals from practitioners who believe that the theory of Learning Styles continues to offer something useful and/or that criticism of them is invalid (e.g., Black, 2016 ). Some of the original proponents of the concept have self-published their own defense of Learning Styles, e.g., ( Felder, 2010 ; Fleming, 2012 ).
The continued use of Learning Styles is, in theory, associated with a number of harms ( Pashler et al., 2008 ; Riener and Willingham, 2010 ; Dekker et al., 2012 ; Rohrer and Pashler, 2012 ; Dandy and Bendersky, 2014 ; Willingham et al., 2015 ). These include a ‘pigeonholing’ of learners according to invalid criteria, for example a ‘visual learner’ may be dissuaded from pursuing subjects which do not appear to match their diagnosed Learning Style (e.g., learning music), and/or may become overconfident in their ability to master subjects perceived as matching their Learning Style. Other proposed harms include wasting resources on an ineffective method, undermining the credibility of education research/practice and the creation of unrealistic expectations of teachers by students.
This study aimed at asking first whether academics in UK Higher Education also believe in Learning Styles. We then attempted to go beyond the controversy and ask whether academics actually use Learning Styles, and how seriously they rate the proposed harms associated with the use of Learning Styles, with the aim of understanding how best to address the persistence of Learning Styles in education. In addition, we compared belief in/use of Learning Styles to some educational techniques whose use is supported by good research evidence, to put the use of, and belief in, Learning Styles into context.
We found that belief in the use of Learning Styles was high (58% of participants), but that actual use of Learning Styles was much lower (33%) and lower than other techniques which are demonstrably effective. The most compelling weakness/harm associated with Learning Styles was a simple theoretical weakness; 90% of participants agreed that Learning Styles are conceptually flawed.
Materials and Methods
Data were collected using an online questionnaire distributed to Higher Education institutions in the UK. Ethical approval for the study was given by the local Research Ethics Committee at Swansea University with informed consent from all subjects.
The survey was distributed via email. Distribution was undertaken indirectly; emails were sent to individuals at eight different Higher Education institutions across the UK. Those persons were known to the corresponding author as colleagues in Higher Education but not through work related to Learning Styles. Those individuals were asked to send the survey on to internal email distribution lists of academics involved in Higher Education using the following invitation text (approved by the ethics committee) “You are invited to participate in a short anonymous survey about teaching methods in Higher Education. It will take approximately 10–15 min to complete. It is aimed at academics in Higher Education,” followed by a link to the survey which was entitled “Teaching Methods in Higher Education.” Thus the survey was not directly distributed by the authors and did not contain the phrase ‘Learning Styles’ anywhere in the title or introductory text. These strategies of indirect distribution, voluntary completion and deliberately not using the term ‘Learning Styles’ in the title were based upon similar strategies used in similar studies ( Dekker et al., 2012 ; Dandy and Bendersky, 2014 ) and were aimed at avoiding biasing and/or polarizing the participant pool, given the aforementioned controversy associated with the literature on Learning Styles. Although this inevitably results in a convenience sample (we do not know how many people the survey as sent to or how many responded), this was preferable to distributing a survey that was expressly about Learning Styles (which may have put off those who are already familiar with the concept). The survey remained open for 2 months (which included the end-of-year holiday period) and was closed once we had over 100 participants who had fully completed the survey, to ensure a sample size equivalent to similar studies ( Dekker et al., 2012 ; Dandy and Bendersky, 2014 ).
One hundred sixty-one participants started the survey, with 114 completing the survey up to the final (optional) question about demographics. This meant that 29% of participants did not complete, which is slightly better than the average dropout rate of 30% for online surveys ( Galesic, 2006 ). Question-by-question analysis revealed that the majority of these non-completers (79%) did not progress beyond the very first ranking question (ranking the effectiveness of teaching methods) and thus did not complete the majority of the survey, including answering those questions about Learning Styles. Participants had been teaching in Higher Education for an average of 11 years ( SD = 9.8). Participants were asked to self-report their academic discipline. Simple coding of these revealed that participants came from a wide variety of disciplines, including Life and Physical Sciences (26%), Arts, humanities and languages (24%), Healthcare professions (medicine, nursing, pharmacy, etc.) (16%), Social Sciences (10%), Business and Law (5%).
Materials and Procedure
The lack of an evidence base for Learning Styles has been described numerous times in the literature, and these papers have suggested that there may be harms associated with the use of Learning Styles ( Pashler et al., 2008 ; Riener and Willingham, 2010 ; Dekker et al., 2012 ; Rohrer and Pashler, 2012 ; Dandy and Bendersky, 2014 ; Willingham et al., 2015 ). We reviewed these publications to identify commonly posited harms. We then constructed a questionnaire using LimeSurvey TM . All the survey questions are available via the Supplementary Material. Key aspects of the structure and design are described below. The survey was piloted by five academics from Medical and Life Sciences, all of whom were aware of the lack of evidence regarding Learning Styles. They were asked to comment on general clarity and were specifically asked to comment on the section regarding the evidence for the use of Learning Styles and whether it would disengage participants (see below). Key concepts in the survey were addressed twice, from different approaches, so as to ensure the quality of data obtained.
Participants were first asked to confirm that they were academics in Higher Education. They were then asked about their use of five teaching methods, four of which are supported by research evidence [Worked Examples, Feedback, Microteaching and Peer Teaching ( Hattie, 2009 )] and Learning Styles. They were then asked to rank these methods by efficacy.
We then asked participants about their use of Learning Styles, both generally and the use of specific classifications (VARK, Kolb, Felder, Honey and Mumford). For each of these individual Learning Styles classifications we identified, in our question, the individual styles that result (e.g., active/reflective, etc., from Felder). Thus participants were fully oriented to what was meant by ‘Learning Styles’ before we went on to ask them about the efficacy of Learning Styles. To allow comparisons with existing literature, we used the same question as Dekker et al. (2012) “Rate your agreement with this statement ‘Individuals learn better when they receive information in their preferred Learning Style (e.g., auditory, visual, kinaesthetic).”’
We then explained to participants about the lack of an evidence base for the use of Learning Styles, including the work of Coffield et al. (2004) , Pashler et al. (2008) , Rohrer and Pashler (2012) , Willingham et al. (2015) . We explained the difference between learning preferences and Learning Style, and made it clear that there was specifically no evidence to support the ‘matching’ of teaching methods to individual Learning Styles. We explained that this fact may be surprising, and that participants would be free to enter any comments they had at the end of the survey. Those academics who piloted the initial survey were specifically asked to comment on this aspect of the survey to ensure that it was neutral and objective.
We then asked participants to rate their agreement with some of the proposed harms associated with the use of Learning Styles. Mixed into the questions about harms were some proposed reasons to use Learning Styles, regardless of the evidence. These questions were interspersed so as to avoid ‘acquiescence bias’ ( Sax et al., 2003 ). Agreement was measured on a 5-point Likert scale.
Finally, participants were asked for some basic demographic information and then offered the opportunity to provide free-text comments on the content of the survey.
Quantitative data were analyzed by non-parametric methods; specific tests are described in the results. Percentages of participants agreeing, or disagreeing, with a particular statement were calculated by collapsing the two relevant statements within the Likert scale (e.g., ‘Strongly Agree and Agree’ were collapsed into a single value). Qualitative data (free-text comments) were analyzed using a simple ground-up thematic analysis ( Braun and Clarke, 2006 ) to identify common themes. Both authors independently read and re-read the comments to identify their own common themes. The authors then met and discussed these, arriving at agreed common themes and quantifying the numbers of participants who had raised comments for each theme. Many participant comments were pertinent to more than one theme.
Belief vs. Use; Do Teachers in Higher Education Actually Use Learning Styles?
We addressed this question from two perspectives. Academics were asked to identify which teaching methods, from a list of 5, they had used in the last 12 months. Results are shown in Figure Figure1 1 . Thirty-three percent of participants reported having used Learning Styles in the last 12 months, but this was lower than the evidence-based techniques of formative assessment, worked examples, and peer teaching. Participants were then asked “have you ever administered a Learning Styles questionnaire to your students” and were given four specific examples along with the ‘styles’ identified by those examples. The examples chosen were those most commonly found in a recent study of the literature on Learning Styles ( Newton, 2015 ). Participants were also given the option to check ‘other’ and identify any other types of Learning Styles questionnaire that they might have used. 33.1% of participants had given their students any sort of Learning Styles Questionnaire, with the response for individual classifications being 18.5% (Honey and Mumford), 14.5% (Kolb), 12.9% (VARK), and 1.6% (Felder).
Use of various teaching methods in the last 12 months. Academics were asked which of the methods they had used in the last 12 months. Four of the methods were accompanied by a brief description: Formative Assessment (practice tests), Peer Teaching (students teaching each other), Learning Styles (matching teaching to student Learning Styles). Microteaching (peer review by educators using recorded teaching).
We subsequently asked two, more general, questions about Learning Styles. The first of these was the same as that used by Dekker et al. “Individuals learn better when they receive information in their preferred Learning Style (e.g., auditory, visual, kinaesthetic),” with which 58% agreed. The second was “I try to organize my teaching to accommodate different student Learning Styles (e.g., visual, kinaesthetic, assimilator/converger),” with which 64% of participants agreed. These data show a contrast between a general belief in the use of Learning Styles, which is much higher than actual use ( Figure Figure2 2 ).
Belief in use of Learning Styles. At different points throughout the survey, participants were asked to rate their agreement with the statements regarding their belief in, and their actual use of, Learning Styles. These questions were asked prior to informing participants about the lack of evidence for the use of Learning Styles. When asked if they believed in the use of Learning Styles 1,2 , approximately two thirds of participants agreed, whereas when asked specifically about actual use 3,4 , agreement dropped to one-third.
1 Rate your agreement with this statement: Individuals learn better when they receive information in their preferred Learning Style (Individuals learn better LS) .
2 Rate your agreement with the statement: I try to organize my teaching to accommodate different Learning Styles (Accomodate LS) .
3 Have you ever administered a Learning Styles questionnaire to your students? If so, please state which one (Given students a LSQ) .
4 Which of these teaching methods have you used in the last 12 months? (Used LS in year) .
Possible Harms Associated with the Use of Learning Styles
There was significant agreement with all the proposed difficulties associated with the use of Learning Styles, as shown in Figure Figure3 3 . However, compared to the other proposed harms, participants showed stronger agreement with the statement “The theory of Learning Styles is conceptually flawed” – it does not account for the complexity of ‘understanding.’ It is not possible to teach complex concepts such as mathematics or languages by presenting them in only one style. In addition, some information cannot be presented in a single style (e.g., teaching medical students to recognize heart sounds would be impossible using visual methods, whereas teaching them to recognize different skin rashes would be impossible using sounds). In this section of the survey we also included two questions that were not about proposed harms. Forty-six percent of participants agreed with the statement “Even though there is no ‘evidence base’ to support the use of Learning Styles, it is my experience that their use in my teaching benefits student learning,” while 70% agreed that “In my experience, students believe, rightly or wrongly, that they have a particular Learning Style.”
Participants were asked to rate their agreement with various difficulties that have been proposed to result from the use of Learning Styles. Participants agreed with all the proposed harms but there was a stronger agreement (compared to other options) with the idea that the use of Learning Styles is conceptually flawed. ∗ , significantly different from median of ‘3’ (1-sample Wilcoxon Signed Rank test). #, different from other statements (Kruskal–Wallis test).
Ranking of Proposed Harms
Having asked participants to rate their agreement (or not) with the various harms associated with the use of Learning Styles, we then asked participants to “Rank the aforementioned factors in terms of how compelling they are as reasons not to use Learning Styles” (1, most compelling, 6, least compelling) and to “only rank those factors which you agree with.” There is not universal agreement on the analysis of ranking data and so we analyzed these data in two simple, descriptive ways. The first was to determine how frequently each harm appeared as the top ranked reason. The second was to calculate a ranking score, such that the top ranked harm was scored 6, and the lowest ranked scored 1, and then to sum these across the participants. Both are shown in Table Table1 1 . Results from both methods were similar and agreed with the prior analysis ( Figure Figure3 3 ), with participants most concerned about the basic conceptual flaws associated with the use of Learning Styles, alongside a potential pigeonholing of learners into a particular style.
Ranking of proposed harms as compelling reasons not to use Learning Styles.
Continued Use of Learning Styles?
Toward the end of the questionnaire, we asked participants two question to determine whether the completion of the questionnaire had made any difference to their understanding of the evidence base for the use of Learning Styles. Participants were first asked to rate their agreement with the statement “Completing this questionnaire has helped me understand the lack of any evidence base to support the use of Learning Styles.” The 64% agreed while 9% disagreed and 27% neither agreed or disagreed.
Participants were then asked “In light of the information presented, rate your agreement with the following statement – ‘I plan to try and account for individual student Learning Styles in my teaching.”’ 31.6% agreed, 43.9% disagreed, and 23.6% neither agreed or disagreed. The results from this question were compared to those obtained before the evidence was presented, when participants were asked to rate their level of agreement with this statement “I try to organize my teaching to accommodate different student Learning Styles (e.g., visual, kinaesthetic, assimilator/converger).” The results, shown in Figure Figure4 4 , show a statistically significant difference in the two sets of responses suggesting that completion of the questionnaire improved participants understanding of the lack of an evidence base for the use of Learning Styles and thus they were unlikely to continue using them. However, almost one-third of participants still agreed with the statement; they intended to continue using Learning Styles.
The completion of the survey instrument associated with a change of participants views of Learning Styles. At the beginning of the study, participants were asked to rate their agreement with the statement “I try to organize my teaching to accommodate different student Learning Styles (e.g., visual, kinaesthetic, assimilator/converger),” and 64% agreed. At the end of the study, participants were asked “In light of the information presented, rate your agreement with the following statement – ‘I plan to try and account for individual student Learning Styles in my teaching,”’ and 32% agreed. ∗ , a Wilcoxon signed rank test revealed a statistically significant difference in the pattern of response ( P < 0.0001, W = -1977).
This then raised a series of interesting questions about why participants would persist in using Learning Styles despite having been presented with all the evidence showing that they are not effective (although participants were not specifically asked whether they would persist in the matching of instructional design to student Learning Style). The sample size here, although equivalent to previous studies, is modest and obviously the 32% are only a portion of that. Thus we were reluctant to undertake extensive post hoc analysis to identify relationships within the sample. However, in response to a reviewer’s suggestion we undertook a simple descriptive analysis of the profile of the 31.6% of participants who indicated that they would continue to account for Learning Styles and compare them to the 43.9% who said that they would not. When splitting the data into these two groups, we observed that almost all (94.4%) of those who said they would still use Learning Styles at the end of the survey had originally agreed with the question “I try to organize my teaching to accommodate different student Learning Styles (e.g., visual, kinaesthetic, assimilator/converger),” and no participants from that group had disagreed. In contrast, agreement was only 40% for the group that eventually said they would not use Learning Styles, while disagreement was 46%. A similar split was found for the question “Even though there is no ‘evidence base’ to support the use of Learning Styles, it is my experience that their use in my teaching benefits student learning”; for the group that would go on to say that they will still use Learning Styles, 89% agreed, while agreement was only 18% from the group that would go on to say they will not continue to use Learning Styles.
Educational Research Literature
Finally we asked participants to rate their agreement with the statement “my educational practice is informed by the education research literature.” Forty-eight percent of participants agreed with the statement. A Spearman Rank Correlation test revealed no correlation between responses on that question and on the ‘Dekker’ question “Individuals learn better when they receive information in their preferred Learning Style (e.g., auditory, visual, kinaesthetic)” r = 0.07508, P = 0.4.
Forty-eight participants left free-text comments. The dominant common theme, raised by 23 participants was the need to use a variety of teaching methods in order to (for example) keep students engaged or to promote reflection. This theme was often stated in the context of ‘despite the evidence again showing a lack of effectiveness of Learning Styles.’ A related theme (13 participants) was that participants had a looser interpretation of ‘Learning Styles,’ for example that they referred simply to ‘styles of learning,’ while a second related theme from nine participants was they would still, despite the evidence, use Learning Styles and/or found them useful. Eight participants commented that they were aware of the lack of evidence base for the use of Learning Styles and eight participants also gave their own examples of why Learning Styles were conceptually flawed. Despite the careful piloting described above, a small number of participants (four) commented that the survey was biased against Learning Styles, while eight participants perceived some of the questions to be ‘leading.’ No specific ‘leading’ questions were identified but there was a substantial overlap between these two themes, with three of the comments about the survey being ‘biased against Learning Styles’ coming alongside, or as part of, a comment about questions being ‘leading,’ with an implied relationship between the two. An additional theme, from five participants, was thanks; for raising the issue and/or interesting content.
The first aim of this study was to determine how widespread belief in, and use of, Learning Styles is by academics in UK Higher Education. In a 2012 study, 93% of a sample of 137 UK school teachers agreed with the statement “Individuals learn better when they receive information in their preferred learning style (e.g., auditory, visual, kinesthetic).” In our sample of academics in UK Higher Education, 58% agreed with that same statement while 64% agreed with the similar, subsequent statement “I try to organize my teaching to accommodate different Learning Styles.” Thus a majority of academics in UK HE ‘believe’ in the use of Learning Styles although the figures are lower than in the 2012 study of schoolteachers. However, prior to asking these questions we asked some more direct questions about the actual use of Learning Styles instruments. Here the figures were much lower, with 33% of participants answering ‘yes’ to the statement “Have you ever administered a Learning Styles questionnaire to your students” and the same number stating that they had used ‘Learning Styles’ as a method in the last 12 months, where the method was defined as “matching teaching to individual student Learning Styles.” This value was lower than for a number of teaching methods that are evidence-based. Interestingly the most commonly used Learning Styles instrument was the Kolb Learning Styles Inventory; this is the Learning Styles classification that has been most frequently tested for evidence of such a ‘matching effect’ and where no evidence has been found ( Pashler et al., 2008 ).
The empirical evidence is clear that there is currently no evidence to support the use of Learning Styles instruments in this way ( Coffield et al., 2004 ; Pashler et al., 2008 ) and thus the fact that actual use of Learning Styles is lower than the use of demonstrably evidence-based methods could be considered reassuring, as could our finding that actual use is lower than ‘belief’ in the efficacy of Learning Styles. In addition, although we find that a majority of UK academics in Higher Education believe in the use of Learning Styles, the actual numbers observed are the lowest of any similar study. Studies examining belief in the use of Learning Styles have been carried out over the last few years in a number of different populations, and the overall trend is down, from 93% of UK schoolteachers in 2012 (Dekker), to 76% of UK schoolteachers in 2014 (Simmonds), 64% of HE academics in the US in 2014 (Dandy and Bendersky) to 58% here. There are obviously a number of caveats to consider before concluding that belief in the use of Learning Styles is declining; these studies have been conducted in different countries (US and UK), using teachers in different disciplines (school teachers and higher education). A follow-up, longitudinal study across different populations/contexts would be informative to address whether belief in the use of Learning Styles is truly declining, and to further understand whether actual use of Learning Styles is lower than ‘belief,’ as we have found here.
However, a more pessimistic interpretation of the data would be to focus on our finding that one-third of academics in UK higher education have, in the last year, used a method that was shown to be ineffective more than a decade earlier. The free-text comments give us some insight into the broader issue and perhaps a further hypothesis as to why the ‘myth’ of Learning Styles persists. The dominant theme was a stated need to use a diverse range of teaching methods. This is a separate issue to the use of Learning Styles and there was no suggestion in the survey that to not use Learning Styles was to advocate for all students to be taught the same way, and/or to use only one method of teaching. Neither of these approaches are advocated by the wider literature which seeks to ‘debunk’ Learning Styles, but it is clear from the abundance of comments on this theme that these two issues were related in the view of many of the participants. This is supported by the emergence of the related theme of ‘styles of learning rather than Learning Styles’; many participants had a looser definition of ‘Learning Styles’ than those introduced early in the survey. This finding leads us to urge caution and clarity in the continued ‘debunking’ of the ‘myth’ of Learning Styles. Learners obviously have preferences for how they learn. In addition, there is an obvious appeal to using a variety of teaching methods and in asking students to reflect on the ways in which they learn. However, these three concepts are unrelated to the (unsupported) idea that there is a benefit to learners from diagnosing their ‘Learning Style’ using one of the specific classifications ( Coffield et al., 2004 ) and attempting to match teaching to those styles. However, these concepts were clearly linked in the mind of many of our participants.
Participants agreed with many of the statements describing proposed harms or weaknesses of Learning Styles. Part of our intention here was to understand which are the most compelling of these; all have, at least, a face validity if not empirical evidence to support them. As we attempt to ‘spread the word’ about Learning Styles and promote alternate, evidence-based approaches, it is useful to know where perceived weaknesses are with Learning Styles. Thus our aim was not so much to observe absolute rates of agreement with individual harms/weaknesses (we would expect to see agreement, given that participants had just been told of the lack of evidence for Learning Styles), but to identify any differences in rates of agreement between the individual statements. There was strongest agreement with the conceptual weaknesses associated with Learning Style theory; that it is not possible to teach ‘understanding’ using a particular style, or to capture certain types of learning in all styles. Weakest agreement was with the statement that “The continued promotion of Learning Styles as a product is exploiting students and their teachers, for the financial gain of those companies which sell access to, and training in, the various Learning Style questionnaires.” The difference between the ‘conceptual weakness’ and other weaknesses/harms was statistically significant, suggesting that, where efforts are being made to ‘debunk’ the ‘myth’ of Learning Styles, then an appeal to the simple conceptual problems may be the most compelling approach. This would also seem to fit with the data described above re: ‘belief vs. use’; although it is tempting to believe that individual students have a Learning Style than can be utilized to benefit their education, the conceptual flaws inherent in the theory mean that actually putting them into practice may prove challenging.
Completion of the questionnaire, which highlighted all of the problems associated with the use of Learning Styles, was clearly associated with a group-shift in the stated likelihood that the participant group would use Learning Styles, although we must also consider that, having been presented with all the evidence that Learning Styles are not effective, it seems reasonable to assume that some participants may succumb to some form of social desirability bias, wherein participants respond in the way that they perceive the researchers desire or expect ( Nederhof, 1985 ). However, despite being presented with all the aforementioned evidence, approximately one-third of participants still agreed with the statement “In light of the information presented……‘I plan to try and account for individual student Learning Styles in my teaching.’” As described in the section “Introduction” there is an ongoing controversy, often played out via blogs and social media, about the use of Learning Styles, with some continuing to advocate for their use despite presentation of all the aforementioned evidence. It is even possible that to persist with a ‘myth debunking’ approach to Learning Styles may be counter-productive; the so-called ‘backfire effect’ describes a phenomenon wherein attempts to counter myths and misconceptions can result in a strengthening of belief in those myths. For example, 43% of the US population believe that the flu vaccine causes flu, and amongst that group are some who are very worried about the side effects of vaccines. Correcting the misconception that the vaccine causes flu is effective in reducing belief in the myth, yet reduces the likelihood that those who are concerned about vaccines will get vaccinated ( Nyhan and Reifler, 2015 ). We observed that almost all those who said they would still use Learning Styles after completing the survey had originally said that they try to account for Learning Styles in their teaching. An interesting question for further study may be to ask, of those who are currently using Learning Styles, whether being presented with the (lack of) evidence regarding their use makes it more likely that those academics will continue to use them? In addition, it may be informative to use an in-depth qualitative approach that would allow us to understand, in detail, what it is about Learning Styles that continues to appeal.
Instead of focusing on Learning Styles, it may be more productive for all, most importantly for students, to focus on the use of teaching and development activities which are demonstrably effective. For example, the use of microteaching, a simple, multi-peer review activity, the effectiveness of which has been repeatedly demonstrated in teacher-training settings ( Yeany and Padilla, 1986 ). Only 12% of survey participants here stated that they had used microteaching within the last 12 months, yet to do so would be relatively straightforward; it is little more than the application of a few more peers to an episode of peer-observation; something that is routinely undertaken by academics in UK Higher Education. This finding may be confounded by participants simply not being aware that ‘microteaching’ means, basically, ‘multi-peer observation and feedback,’ although this was explained twice in the survey itself.
Further support for an approach focused on raising awareness comes from our finding ( Figure Figure1 1 ) that, as a group, participants stated use of different teaching methods mapped directly on to their perceived usefulness (e.g., the most commonly used technique was formative assessment which was also perceived as the most effective). It seems reasonable to infer a causative relationship between these two observations, i.e., that participants use techniques which they consider to be effective, and thus if we can raise awareness of techniques which are demonstrably effective, then their use will increase.
There are some limitations to our study. A review of factors associated with dropouts from online surveys ( Galesic, 2006 ) observed that the average dropout rate amongst general-invitation online surveys (such as this one) is ∼30%, and so our dropout rate is entirely within expectations, although upon reflection we could perhaps have designed the instrument in a way that reduced dropout. A number of factors are associated with higher dropout rates, including the participant’s level of interest in the topic and the presence of ‘matrix questions.’ As described in the methods, we deliberately avoid entitling the survey as being about ‘Learning Styles’ to avoid biasing the responses, and a detailed analysis of the participation rate for each question revealed that the majority of dropouts occurred very early in the survey, after being asked to rank the effectiveness of the five teaching methods; a question potentially requiring higher effort than the others. An additional point reviewed by Galesic (2006) is the evidence that the quality of responses tails off for the items preceding the actual dropout point, thus the fact that participation rate remained steady after this early dropout is reassuring. It would also have been helpful to have a larger sample size. Although ours was equivalent to that in similar studies ( Dekker et al., 2012 ; Dandy and Bendersky, 2014 ) we may have been able to tease out more detail from the responses with a larger sample size, for example to determine whether ‘belief’ in Learning Styles was associated with any of the demographics factors (e.g., subject discipline, or age) to get a deeper understanding of why and where Learning Styles persist.
In summary, we found that 58% of academics in UK Higher Education believe that Learning Styles are effective, but only about a third actually use them, a lower percentage than use other, demonstrably evidence-based techniques. Ninety percent of academics agreed that there is a basic conceptual flaw with Learning Styles Theory. These data suggest that, although there is an ongoing controversy about Learning Styles, their actual use may be low, and further attempts to educate colleagues about this limitation might best focus on the fundamental conceptual limitations of Learning Styles theory. However, approximately one-third of academics stated that they would continue to use Learning Styles despite being presented with all the evidence. Thus it may be better still to focus on the promotion of techniques that are demonstrably effective.
PN conceived the study, PN and MM designed the questionnaire, PN piloted and distributed the questionnaire, PN and MM analyzed the data, PN wrote the manuscript.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors would like to thank those colleagues who distributed the survey at their institutions, and Helen Davies from the Swansea Academy of Learning and Teaching for support with Limesurvey TM .
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00444/full#supplementary-material
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“Learning style” was proposed in the study of English as a native language by American scholar Thelen in 1954, and then developed into the study of English as a second language. In China, learning style started to be studied relatively late, which was developed on the basis of referring to the concept of learning style in western educational psychology. Chinese experts in the field of education have devoted themselves to investigating the individual differences of learners. In 1994, studies were carried out on learning style, focusing mainly on its classification, the relationship between learning style and language learning performance, etc. Studies have shown that the learning efficiency of learners can be promoted by providing them with appropriate learning content organization methods according to their different learning styles. These studies are of great significance for future research on the acquisition and teaching of Chinese as a foreign language.
A Review of Research on Learning Style
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The traditional school education cannot really teach every child without class and teach them according to their aptitude. In recent years, online education has broken the limitations of time and space, making the best educational resources at your fingertips. The mainstream trend of education is to give students accurate personalized learning guidance. Learning style focuses on students’ personalized learning.
Learning style refers to the way in which learners absorb, process and store new information and master new skills. Natural and habitual, this way will not change with teaching methods or learning content (Reid, 1987) . Since the 1960s, the focus of language teaching has shifted from teachers to students. Accordingly, the research focus of language (especially foreign language) educators and researchers has shifted from teaching methods and processes to the language learning process and the characteristics of language learners themselves. Therefore, the individual differences of learners have increasingly attracted the attention of researchers studying second language (L2) acquisition (Li, 2021) . The academic community has basically reached a consensus that the learning efficiency of learners can be improved by offering them proper learning content organization methods according to their different learning styles and guiding their personalized learning styles during learning. Research on learning styles in foreign countries began early. Nearly 70 years have passed since American scholar Thelen (1954) put forward the term “learning style”. In this paper, relevant research studies in different countries were taken as main reference materials, including the research results of L2 acquisition, English teaching, psychology and teaching Chinese as a foreign language (TCFL). Besides, studies on the learning style of learners were sorted out. In addition, a summary was made of previous research results and the consensus reached, providing some references for future research on language acquisition and teaching.
2. Definition and Theoretical Basis of Learning Style
2.1. Definition of Learning Style
Opinions on the definition of learning style vary. At present, it has no unified concept. Reid (2002) , an influential foreign scholar, believed that learning style is a natural and habitual method and skill of personal preference for learners to absorb, process and store new information and master new skills. In China, Tan (1995) provided a generally accepted definition of learning style: Learning style is a study method a learner uses consistently with characteristics of personality and the summation of learning strategy and inclination. After research, it is found that learning style has a consistent definition despite lacking a unified concept in the academic circle. First, learning style is the learning way of learners with personal habits and preferences in the learning process. Second, it is formed by individuals in their long-term study life, with strong stability. The learning style of everyone is different and unique due to the influence of the environment, culture and other factors.
In the current research, learning and cognitive styles as well as learning strategy are usually used as synonyms. The study of learning style was later than and drew on that of cognitive style. Also known as the cognitive approach, cognitive style refers to the habitual way that individuals often adopt in their cognitive process. Specifically, it is the attitudes and ways that individuals prefer and get habituated to during the process of perception, memory, thinking and problem-solving (Song, Li, & Wang, 2001) . Cognitive style primarily studies the way of information processing, while learning style focuses on the differences in the intelligence, emotion, motivation and other aspects of learners, and their preferences for learning environments, content, strategies, etc. To some extent, cognitive style is a significant component of learning style.
Learning strategy refers to the actions taken by learners to facilitate the acquisition, storage, extraction and utilization of information. In plain terms, it is the methods or behaviors of learners to promote learning and make learning faster and more effective (Jiang, 2000) . Learning strategy can be developed through practice and generally change with learning objects and subjects as well as changes in environmental conditions, showing greater flexibility. However, learning style originates from the personality of learners which is different from that of others and has a certain degree of heredity. Moreover, it is gradually formed in long-term learning activities and seldom changes with the change of learning environments and content, with stability (Chen, 2016) .
2.2. Related Teaching Theories
Affiliated with educational psychology, learning style has great guiding significance to educational practice. In the 1630s, Czech educator Comenius put forward the class teaching system in his work Magna Didactica , and initiated the teacher-centered teaching theory. As emphasized by the theory, teachers are the center of teaching activities and take charge of organizing and monitoring the whole process of teaching activities, while students are the objects of knowledge infusion. This learning theory occupied an important position in the realm of education for a time. By the 20 th century, the behaviorist learning theory was affected by the cognitive learning theory holding that students are the subjects of information processing. As a major branch of the cognitive learning theory, the constructivism learning theory proposes that knowledge is acquired through learning, others’ help, information query and meaning construction in a specific environment. The construction of meaning is the ultimate goal of this learning process. The constructivism learning theory is conducive to pushing forward the development of student-centered teaching.
Originating in the 1950s, the individualism learning theory underlines that students are the center of teaching. It also maintains that teachers should help students discover their potential in the teaching process and enable them to teach themselves. Additionally, teachers should advocate meaningful and experiential learning, and require students to take responsibility for themselves, set up scenarios, select materials, raise questions, determine progress and pay attention to results by themselves (Yue, 2015) .
In his monograph Personalized Teaching Theory , Professor Deng East China Normal University put forward the characteristics of personalized teaching in seven aspects: media technology, learning pace, methods, content and objectives as well as evaluation methods and criteria. For instance, learners can learn at their own pace and choose different media technologies and diversified learning strategies and contents. The diversity of learning objectives can adapt to the individual differences of students. Learning tasks, evaluation criteria, etc. can be freely selected. Further, the research equated personalized teaching with adaptive teaching and differentiated teaching with inclusive teaching.
2.3. Related Psychological Theories
Sweller, a famous Australian educational psychologist, and other scholars proposed the cognitive load theory in the 1980s. In light of the theory, people have limited cognitive resources in the cognitive process. A high cognitive load will be brought to learners if the resources to be occupied in a link of information processing exceed the total amount of cognitive resources owned by learners per se, thereby influencing the learning outcomes of learners.
“Schema” has already appeared in the philosophical works of Kant. In 1932, psychologist Bartlett formally brought up the concept of “schema” in psychology and formed a quite systematic schema theory referring to the theory of knowledge representation and storage mode organized on a topic. In brief, it is necessary for people to learn and master a lot of knowledge in their life. Such knowledge is not stored randomly in the brain but divided according to different topics. The related contents under the same topic constitute a knowledge unit which is a schema. Knowledge is schematically stored in long-term memory, which thus reduces the cognitive load of learners.
As a cognitive theory, the dual coding theory was formally put forward in the book Imagery and Verbal Processes in 1971, with a central assumption that verbal and imagery information is stored separately in the long-term memory of people. The theory states that people possess separate visual and auditory processing channels where respective cognitive resources are also independent of each other. The separate visual image channel is used to process materials of visual representation such as videos, pictures, animations and texts, while the separate auditory/verbal one is utilized to process materials of auditory representation like voice commentaries and background music. Learning efficiency can be better improved when people process information through two independent channels, which is more in line with the characteristics of human information processing. The limited capacity hypothesis holds that people’s visual and auditory processing channels are limited in information capacity and unable to present too much information simultaneously. Otherwise, it will lead to information overload and hence affect learning outcomes.
3. Related Research on Learning Styles in China and Abroad
3.1. Foreign Research Results
3.1.1. Elements of Learning Style
The elements of learning style are classified into different types, which are directly related to the classification and measurement of learning style. The most representative elements of learning style have the following several explanations.
The elements of learning style were divided by the Dunns into five categories: environmental, emotional, social, physiological and psychological elements, with a total of 27 specific elements. Keefe (1979) claimed that learning style is composed of 32 elements in cognitive, emotional and physiological categories.
By combining the characteristics of the educational system and culture in China, Chinese scholar Tan pointed out the inappropriateness of western research on the elements of learning style in China. Apart from that, he segmented learning style into 23 elements in physiological, psychological and social categories, and conducted detailed research on them in On Learning Style . Furthermore, the scholar categorized the measurement of the elements of learning style into comprehensive and individual measurements. Comprehensive measurement means that a set of test scales measure multiple elements, which is characterized by strong comprehensiveness. Representative scales are the Learning Style Inventory (LSI) of the Dunns and the Learning Style Profile (LSP) of the National Association of Secondary School Principals. Individual measurement carries out analysis on physiological, psychological and social elements, among which psychological elements mainly include cognitive and emotion-conation factors, and social elements mainly contain personality types and gender perspectives.
Tan summarized the elements of learning style in Learning Style as follows: Physiological elements chiefly comprise intuitive response, brain function and learning time, sound, light, temperature as well as mobility and sitting posture preferences. The learning styles corresponding to learning time preference are principally morning, forenoon, afternoon and evening types. The learning styles corresponding to perceptual response are mainly visual, auditory and kinesthetic types. The learning styles corresponding to sound preference primarily include the need for silence, the use of background sound to mask the interference of other sounds during learning and the tolerance of a certain degree of noise. The learning styles corresponding to light preference are mainly stronger and darker light preferences. Psychological elements largely consist of cognitive, emotional and conative elements as well as psychological development. Social elements are made up of personality types, gender perspectives, etc., including 16 personality types.
3.1.2. Classification of Learning Style
From the 1950s to the present, learning style theory models have developed into more than 70 types, whose specific types are listed in Table 1 .
In the 1990s, Felder & Silerman co-created Feler-Silverman Learning Style Model. In 1991, Felder cooperated with Solomon to design the Felder-Soloman Index of Learning Style (ILS) which mainly measures the situation of learners in the four dimensions of information input, perception, processing and understanding.
Compared with other questionnaires of the same type, this questionnaire has a more reasonable classification and measures the learning styles of learners more scientifically. Index of Learning Style (ILS) is listed in Table 2 .
Table 1 . Table of learning style theory models.
Table 2 . Table of Index of Learning Style (ILS).
3.1.3. Relationship between Learning Style and Language Acquisition
Kogan is the first person to apply the cognitive style to language teaching and published the paper Cognitive Style and Reading Performance in 1980. In his view, “compensation strategies can be better sought by studying the cognitive style of individuals in order to overcome the possible obstacles encountered in reading.” Chen & Wu explored the learning effects of learners who had visual and verbal cognitive styles and learned three different types of teaching videos (classroom record, three split screens and picture-in-picture types) in an online teaching environment. The results show that three kinds of teaching videos had no significant effect on the academic performance of verbal and visual learners. However, verbal learners paid more sustained attention to learning teaching videos than visual ones. In addition, the cognitive load generated by visual learners was significantly higher than that generated by verbal learners in the learning of picture-in-picture teaching videos. Chen & Sun confirmed that multimedia materials containing videos and animations are more suitable for visual learners than those containing texts and animations. In contrast to learners with visual preference, those with verbal preference generate lower cognitive load when learning teaching videos continuously presenting teacher images compared with briefly presenting teacher images. Horner et al. found that learners with a low visual preference would produce a higher cognitive load when learning teaching videos with teacher images, whereas those with a higher visual preference would produce a higher cognitive load when learning teaching videos without teacher images.
3.2. Related Research in the Field of Chinese
In China, the early research on learning style mostly discussed its theoretical definition. Learning style has been lacking a unified definition for a long time, whose definition varies by research angle. Most domestic scholars agree with or cite the definition provided by Tan that learning style refers to the preferences of learners with personality characteristics for methods, means, learning content and environments to complete learning tasks. The research on Chinese includes that of Chinese as a first language on the one hand and L2 on the other hand.
3.2.1. Related Research on Chinese as a First Language
The cognitive style was first applied in Chinese teaching in China from the mid and late 1980s. In Influence of Field Dependence on the Effects of Centralized and Decentralized Literacy , Zhang and Feng (1985) conducted experimental research and reached the following conclusion: “field-independent children are suitable for centralized literacy, while field-dependent ones are suitable for decentralized literacy. Children in between show no significant difference between the two teaching methods.” In A Study of the Relationship between Cognitive Style and Language Learning Strategies , Yao (2006) claimed that learners with different cognitive styles should adopt different language learning strategies. Through the study of learning strategies, it was found that the use and frequency of learning strategies by language learners have a great impact on the learning effect. The study of learning strategies is inseparable from that of cognitive styles. This is because learners can adopt different learning strategies according to their advantages or disadvantages and by understanding their cognitive styles with the aim of improving their language learning ability.
Currently, the domestic research on the relationship between cognitive style and foreign language learning mainly focuses on college English teaching. Zhu et al. employed the Perceptual Learning Style Preference Questionnaire (PLSPQ) developed by Reid and classified 133 students from six classes in a senior high school into six groups of subjects with different learning styles through the experimental method. It was confirmed that the kinesthetic learning style of senior high school students at different levels has the strongest correlation with English learning performance. Wang et al. applied Kolb’s Learning Style Model to verify the significant impact of learning style on fluency in L2 tasks. Zhao made use of Reid’s PLSPQ to test the English acquisition level of college students. Experiments show that students of higher vocational colleges under the Sino-foreign cooperative education model have different learning style preferences and students with different English levels are different in learning style.
Little research has been done on Chinese preschool children. Although some studies have investigated the bilingual education and cognitive style of preschool children (Wang, 2001) , few have explored the relationship between the cognitive style and the L2 acquisition process of children. Through observing and studying 23 preschool children, Li and Ju probed into the correlations of cognitive style with their L2 learning and classroom performance. The results show that the cognitive style of preschool children has an impact on their performance in L2 class despite being not directly associated with their L2 test performance. Meanwhile, field-independent children tend to be better than field-dependent ones in L2 test performance.
Shi (2003) of Chung Yuan Christian University took 122 freshmen as research objects to examine the influence of different learning styles and methods on the learning outcomes of these students in an online learning environment and studied the interactive effects of learning methods on learning outcomes. It was discovered that learning styles and methods exert an influence on learning outcomes.
Different types of cognitive styles will have a certain impact on the learning effects of learners, which however should not be related to the intelligence of learners. Witkin’s Field Cognitive Style Scale represents the measurement and classification of intelligence rather than cognitive style to some degree. In addition, it has been shown that field independence increases with age. Li and Che (2006) revised the verbal-imagery sub-scale in the cognitive style analysis (CSA) system, and analyzed and compared the differences in cognitive style between Chinese and British college students. The results show that Chinese college students prefer the verbal side in the verbal-imagery dimension and the analytic side in the wholist-analytic dimension. Bao et al. (2012) questioned the cognitive style of verbal-imagery division and further distinguished the imagery cognitive style model, thus advancing the research and development of the object-spatial imagery and verbal cognitive style model. Wu (2011) adopted the dividing standards of object-spatial imagery and verbal cognitive style to study and discuss the relationship among the spatial ability, cognitive style and mathematics learning effect of senior high school students. It was discovered that spatial imagery learners have strong abilities in spatial orientation, rotation and visualization, and can achieve better results in mathematics tests. Additionally, male students with a strong tendency towards verbal learning are more likely to achieve better results in spatial orientation ability than female ones. The above analysis shows that different conclusions will be reached according to the classification of different cognitive styles. As a result, more scientific experiments are needed to support the influence of the dividing standards of cognitive style on the learning effects of different learners.
3.2.2. Related Research on Chinese as L2
Few domestic studies have focused on the learning style of Chinese as L2, most of which are based on questionnaires by foreign scholars, and modified and investigated according to the actual situation of teaching and students. Since the second half of the 1990s, especially after 2000, a growing number of researchers had begun to attach importance to the individual differences of students in the research of L2 teaching. Cognitive style, an important part of the individual differences of students, is extensively applied in the study of Chinese teaching. During this period, Wang, Xu & Wang et al. were rather influential in the independent research of cognitive style in the domain of Chinese teaching.
Wang (2006) included the paper Research on Learners Learning Chinese as a Second Language and Cognitive Style in the book Research on Learners Learning Chinese as a Second Language and Cognitive Style . The content involved research on the cognition of Chinese phonetics, characters and vocabulary, the individual differences of learners, etc.
Xu (2006) expressed his opinion in the article Research on the Differences in the Learning Strategies of Chinese Learners with Different Cognitive Styles . From his perspective, the significance of studying cognitive style is that “cognitive style varies from individual to individual. If learning about the cognitive characteristics of learning objects, teachers can formulate corresponding teaching plans and try to take teaching approaches matching the personality characteristics of learners. During group learning, teachers should properly take into account the personality of learners and mobilize their respective strengths to realize mutual complementarity and render the style of teaching and learning as harmonious as possible”. Wang (2009) believed that “cognitive style is a critical individual difference variable. Putting forward the strategy of TCFL based on the cognitive style theory through exploring the relationship between cognitive style and L2 acquisition is beneficial to optimizing the process of TCFL and improving the implementation quality of the TCF course.”
The research on the application of cognitive and learning styles in Chinese teaching greatly enriches the theory and practice of Chinese teaching and lays a good foundation for the further discussion of cognitive/learning styles in Chinese teaching in the future. Based on Reid’s PLSPQ, Yi and Yan (2009) carried out a study on the perceptual style of 325 foreign students from Central Asia. It was found that the Chinese learning style of Central Asian students presents the following overall tendency: tactile > visual > group > individual > auditory, and an obvious tendency in tactile, visual and group types. Fang (2013) conducted a survey on 90 Thai learners from Thailand and Shanghai and discovered that auditory > tactile > kinesthetic > visual in terms of sensory learning style and group > individual in terms of social learning style. Zhao (2016) combined this with the actual situation of Chinese teaching in the Philippines and examined the learning style of 440 students in Philippine public secondary schools. The research showed that the learning style of students in Philippine public secondary schools presents the following tendency: group > visual > kinesthetic > auditory > tactile > individual and perceptual learning style shows no significant difference in age and gender. Ye (2017) conducted an investigation on 156 high school students in Italian high schools, and noticed that the learning style of Italian high school students shows the following tendency: kinesthetic > tactile > auditory > visual.
In addition, some scholars used the questionnaire designed by Oxford et al. as a measurement tool for research. Chen (2015) surveyed 85 overseas students from the United States and found that the visual learning style was preferred by the surveyed student most, followed by auditory and kinesthetic learning styles successively. Yang (2016) drew on the design thought of Wang (2014) based on this questionnaire and investigated 74 middle school students from Burma. Statistics show that the Chinese learning style of these middle school students presents the following tendency: visual > auditory > kinesthetic. Wei (2012) took the learning style questionnaire prepared by Xi’an Jiaotong University as the basis, referred to the questionnaires of Reid & Oxford, and investigated the learning style of 62 South Korean students in Shandong Province. The results show that South Korean students exhibit the following learning style tendency on the whole: auditory > kinesthetic > visual, and use auditory learning style as the main learning style. Li (2014) referred to the English learning style questionnaire compiled by Liu & Dai and the learning style questionnaires of Oxford & Reid. Moreover, the specific situation of Chinese teaching for Russian students in Shandong Province was combined to study the Chinese learning style of 150 Russian students. The conclusions drawn are as follows: Russian students show an obvious tendency towards tactile and visual learning styles; Russian students of different genders show significant differences in auditory and visual learning styles, while those of different ages show significant differences in cooperative and individual learning styles.
Studies on the correlation between learning style and academic performance have obtained abundant research results and conclusions. A large number of empirical studies have proved that a certain correlation exists between academic performance and learning style. Many scholars, including Wang and Xu (2005) , Lu (2005) , Yao, Yan and Liu (2011) , Song and Wang (2012) , Lu, Liu and Xia (2016) , and Zhang (2019) , etc., looked into different experimental objects. The results all show that learning style is significantly associated with academic performance.
4. Conclusion and Research Prospect
4.1. Results of Research on Learning Style
From the perspective of theoretical research, the development of learning style research in recent decades has received attention from multiple disciplines. Plenty of achievements have been attained in not only linguistics but also psychology. The vast majority of domestic and foreign studies on first language and L2 suggest that: How learners absorb, process and store new information and grasp new skills is natural and habitual, and will not change because of different teaching methods or learning contents. Concomitant vocabulary acquisition will be affected by learners’ L2 level, vocabulary size and word-guessing ability, the number of occurrences of target words, reading tasks, tools, etc. Theories related to teaching and psychology have facilitated the deepening of learning style theories. Nevertheless, the results of research on learning style have not yet been applied to language teaching and acquisition as well as psychological research.
4.2. Future Research Prospects
The study of “learning style” has been developing for decades. Where will it go from here? Simply repeating the classification of learning style and merely investigating its influence factors have been unable to meet current research development. Increasing researchers feel that the learning style of learners plays an increasingly important role in both classroom language teaching and daily language acquisition. For this reason, future learning style research should focus on how to put learning style research into the context of language teaching and acquisition for examination and make a combination of learning style, deep language learning, language teaching and other aspects for discussion.
On the other hand, after investigation and research, we learned from consulting front-line Chinese teachers that, scores of different popular language learning methods are currently suitable for young L2 learners, like learning Chinese through singing Chinese songs, watching films, television dramas and variety shows, cooking Chinese food… In future practice, the following questions can be addressed: How about L2 acquisition through these listening, speaking and other channels? Are these channels appropriate for all L2 learners with different learning styles? How to match multi-channel L2 acquisition with learning style?
Apart from that, Chinese research still has room for further development despite some consensus reached by previous studies. For instance, overseas Chinese students remain the largest number among people learning Chinese around the world at present (Li, 2018) . Is the learning style of Chinese learners different from that of European, American, Japanese and Korean learners? Prior research rarely touches upon this aspect. Therefore, it is necessary to conduct related research in more detail and more deeply in the future.
The authors would like to thank International College of Education, Inner Mongolia University.
This research was supported by Inner Mongolia Philosophy and Social Science Planning Project (Grant No. 2021NDC158).
Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.
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Brief research report article, the learning styles educational neuromyth: lack of agreement between teachers' judgments, self-assessment, and students' intelligence.
- 1 Department of Primary Education, School of Education, National and Kapodistrian University of Athens, Athens, Greece
- 2 School of Education and Social Work, University of Dundee, Scotland, United Kingdom
Learning styles (LS) have dominated educational practice since their popularization in the 1970s. Studies have shown that they are accepted by more than 90% of teachers worldwide. However, LS have also received extensive criticism from researchers and academics, due to the poor theoretical justification of the theory, their problematic measurement, and the lack of systematic studies supporting them. The present study tested the hypothesis that teachers' and students' assessment of preferred LS should correspond. Moreover, it tested whether teachers' judgment of LS is driven by the students' IQ. Both questions were studied for the first time in a systematic fashion within LS research in primary school pupils. Fifth and sixth grade pupils ( n = 199) were asked to self-assess their preferred LS, while their teachers were asked to provide their own assessment on individual pupils' LS. No relationship was found between pupils' self-assessment and teachers' assessment, suggesting that teachers cannot assess the LS of their students accurately. Moreover, students' intelligence was not found to drive teachers' assessment of their LS. This study adds to the body of evidence that is skeptical of the adoption of LS in mainstream education.
The term Learning Styles (LS) is used to describe the idea that different individuals differ in the modality of instruction that is most effective to them ( Pashler et al., 2008 ). Criticism of the concept of LS has been widespread ( Curry, 1990 ; Coffield et al., 2004 ; Geake, 2008 ) and in 2002 the Organization for Economic Cooperation and Development (OECD), through its Centre for Educational Research and Innovation (CERI), pronounced LS a neuromyth ( OECD, 2002 ). The OECD classification was particularly concerned with the three LS that are often seen in educational practice, namely the visual, auditory, or haptic (kinaesthetic) types ( OECD, 2002 ).
Despite the lack of evidence in support of the concept, LS remain ever popular with a great majority of educators. A study looking at teachers from the UK and the Netherlands showed that more than 90% of teachers believe there is an optimal delivery style for each learner ( Dekker et al., 2012 ). Similar studies have found equally high numbers in Spain ( Ferrero et al., 2016 ) and Portugal ( Rato et al., 2013 ). In Greece, the setting for this current study, 97% of practicing teachers believe that students' performance can be enhanced when material is delivered in an individual's preferred LS ( Deligiannidi and Howard-Jones, 2015 ) and 94% of student teachers agree ( Papadatou-Pastou et al., 2017 ).
Only a few empirical studies have sought to shed light on the rather obscure picture ( Marcus, 1977 ; Rogowsky et al., 2015 ). For example, Rogowsky et al. (2015) investigated the effect of LS preference in text comprehension in an adult sample. According to the findings, no statistical significance was to be found in the relationship between LS preference, mode of delivery, and learning aptitude.
Building upon this evidence, the current study was designed. Its main aim was to assess whether the LS of primary school aged pupils as assessed by the students and their teachers, would agree. These are important questions, as teachers typically adopt LS within a classroom context by relying on their own assessment of students LS ( Cassidy, 2004 ; Graf and Liu, 2009 ). Moreover, there has been limited research done on primary-aged pupils (e.g., Sun et al., 2008 ), with research mainly available on older students or adult samples ( Diaz and Cartnal, 1999 ; Massa and Mayer, 2006 ; Husmann and O'Loughlin, 2018 ). There is also very limited literature relating LS to IQ ( Dunn and Price, 1980 ; Barbe and Milone, 1982 ; Griggs and Dunn, 1984 ; Dunn, 1990 ) and no studies investigating the hypothesis of whether teachers confuse their students' intellectual ability with a specific LS. However, there is previous research to suggest that teachers can erroneously associate IQ with other characteristics, such as being left-or right-handed ( Papadatou-Pastou et al., 2017 ), or socio-economic status and gender ( Auwarter and Aruguete, 2008 ).
In the current study, intellectual ability was measured by means of a fluid intelligence test, Raven's Colored Progressive Matrices (CPM; Raven et al., 2004 ), which is “the paradigm test of non-verbal, abstract reasoning ability” ( Mackintosh, 1996 , p. 564) and is widely regarded as one of the best tests of Spearman's g, the general factor underlying all cognitive abilities ( Spearman, 1946 ). Raven's matrices have been frequently used in educational research ( Brouwers et al., 2009 ), and have been shown to have good construct validity across age, gender, and country ( Rushton et al., 2003 , 2004 ). Across cultures with a tradition of literacy, like the Greek culture, the norms for the RPM have been shown to be unexpectedly similar ( Raven, 2008 ). Moreover, in contrast to full-scale IQ tests, such as the Wechsler Intelligence Scale for Children (WISC; Kaufman et al., 2015 ), it is easy to administer and can be completed through group administration.
To conclude, the main aim of the study was to investigate whether there is an association between primary school pupils' self-report of their preferred LS and teachers' evaluation of each pupil's LS. The second aim of the study was to investigate whether teachers' assessments of LS would be informed by their students' intellectual ability.
One hundred and ninety nine 5th and 6th grade primary school students including 105 girls (mean age = 135.90 months, SD = 7.27, range = 125–149) and 94 boys (mean age = 136.25 months, SD = 7.28, range = 121–148) participated in this study after their parents gave written informed consent. Five state schools in the first district of the municipality of Athens were recruited to take part in the study. Nineteen teachers (15 women; mean age = 50.52 years, SD = 5.24, range = 31–55) also participated after giving written informed consent. Their mean teaching experience was 20.05 years (SD = 4.05, range = 7–25). Ten teachers taught 5th grade and nine 6th grade level. The study was granted ethical approval by the Institute of Educational Policy, supervised by the Greek Ministry of Education, Research, and Religious Affairs (protocol number: Φ15/1181/174596/Δ1). Written informed consent was given in accordance with the Declaration of Helsinki.
Forced-choice LS question . Student participants were asked whether they are auditory, visual, or kinaesthetic learners. Students had to circle among three choices “visual,” “auditory,” “kinaesthetic.”
Raven's Colored Progressive Matrices ( Raven et al., 2004 ) . The CPM is a measure of fluid intelligent and is considered to be a culture-fair intelligence test ( Van de Vijver and Hambleton, 1996 ). It comprises three sets of 12 items. For each item students have to identify a missing piece in a pattern, choosing among six possible options. Each set of items gets progressively harder. Raw scores were matched with each participant's chronological age in order to calculate IQ scores.
Teachers were asked to respond to two items, namely “Does teaching that is tailor made to the students' LS reinforce the students' performance?,” which was an open-ended question, and “Which is the learning style of each of your students?” with possible responses to the latter question being auditory, visual, or kinaesthetic. Each teacher provided only one LS for each student, after being prompted to recall specific incidents from the classroom. Each student's LS was judged by one teacher.
All analyses were performed using the Statistical Package for the Social Sciences (SPSS) v.25. In order to analyze the qualitative data collected from the open-ended question, word cloud analysis was used, which graphically represents word frequency, giving greater prominence to words that appear more frequently in the participants' responses. In addition, the most characteristic segments of text were presented. The analysis was performed using Iramuteq ( Ratinaud and Dejean, 2009 ), an R interface for multidimensional analysis of texts and questionnaires. Word clouds are increasingly being employed in exploratory qualitative analysis in order to identify the focus of written material ( Atenstaedt, 2012 ). In order to investigate a possible association between the two types of LS assessment (student's self-assessment and teachers' assessment), χ 2 analysis was performed. In order to test whether the three LS, as assessed by the teachers, differed in terms of IQ, analysis of variance (ANOVA) was performed, with the biological sex of the students and the LS of the students according to the teachers as the grouping factors. Whether teachers adopted the LS styles myth could not be used, as all teachers reported that they believe in LS. The partial eta squared (η 2 ) statistic was used as the effect size measure. All p -values were two-tailed and the α-level was set at 0.05. All data and quantitative analysis code are available in the Open Science Framework repository ( https://osf.io/a9g7s/ ).
All teachers reported that they believed that teaching tailored to the students' LS enhances the intake of new information. However, only four teachers referred to the VAK explicitly, that is by using the words visual, auditory, and/or kinaesthetic. For example, one female teacher reported, “ Yes, of course I try to support the students whom I have found out to be visual, auditory, or kinaesthetic types with material that I design myself or that I find online.” Most teachers, however, referred to “learning styles” in a more general fashion or did not make it clear in their responses they referred to the VAK model. For example a male teacher reported “ Students' performance is enhanced when using material that I create personally through handicrafts or through a computer.” and a female teacher reported “ Yes, teaching is tailored to the learning styles of the students sometimes and there is a great enhancement in their performance.”
Figure 1 presents the word cloud stemming from the text of the teachers' responses to the open-ended question, after removing common words, such as “and.” The words that were more prominent, as indicated by the size of the words in the word cloud were “students,” “performance,” “learning,” “ teaching,” and “material.”
Figure 1 . Word cloud representing the most frequent words, giving greater prominence to words that appear more frequently, in the teachers' responses to the open-ended question: “Does teaching that is tailor made to the students' learning styles reinforce the students' performance?”.
Table 1 presents the LS of the students as assessed by self-assessment and by the teachers. A χ 2 analysis was performed to test for their possible association. No statistically significant associations were found to exist; self-assessment—teacher-assessment, χ ( 4 ) 2 = 4.86, p = 0.30.
Table 1 . Crosstabulation of Learning Styles (LS) as measured by self-assessment by the students and by the assessment of teachers.
A 2 × 3 (ANOVA) was performed, with the biological sex of the students (male or female) and the LS of the students according to the teachers (visual, auditory, or kinaesthetic) as the grouping factors and the IQ score as the dependent variable. No main effect of LS was found, F (2,198) = 0.38, p = 0.69, η 2 = 0.004 (mean raw Raven score for visual types = 30.86, SD = 3.60, mean raw Raven score for auditory types = 30.27, SD = 4.45, mean raw Raven score for kinaesthetic types = 30.16, SD = 4.73) or main effect of sex, F (1,198) = 0.21, p = 0.65, η 2 = 0.001 (mean raw Raven score for males = 30.29, SD = 4.51, mean raw Raven score for females = 30.44, SD = 4.31). No interaction was found between LS and sex, F (2,198) = 1.20, p = 0.30, η 2 = 0.012.
The present study looked at whether self-assessment and teacher assessment agreed in the identification of preferred LS in primary school-aged pupils. Results show that there is no correlation between the two. Findings, moreover, suggest that the teachers do not see intellectual ability as a proxy for a particular learning style. This was the first study to investigate these questions and one of the few studies within the LS literature to employ a sample of primary school students. It adds to the growing body of critical literature about the use of LS in educational settings ( Coffield et al., 2004 ; Franklin, 2006 ; Pashler et al., 2008 ).
The present study focused at a certain type of LS, VAK, as it is very commonly used in primary schools, with each student's LS assessed by one teacher ( Sharp et al., 2008 ). Teachers were asked in an open-ended question whether they believed that teaching tailored to the students' LS enhances the intake of new information. The phrasing of this question did not refer specifically to the VAK typology, and this was also reflected in the teachers' responses, as only four teachers referred to the VAK model per se . The rest of the teachers referred to “learning types” in a more general manner, possibly reflecting the vague nature of the concept. This was a limitation of the present study, as we could not ascertain if the teachers adopted the VAK model specifically and could further not test for possible differences between those teachers who adopt the model and those who do not. Future studies should add a question on the VAK typology, as it could be the case that teachers believe in LS, but not specifically to the VAK model. Moreover, judgments made by different teachers for the same students could be compared.
Overall, we posit that identifying preferred LS can be a hit-and-miss process, with no agreement between the assessment made by teachers and students. We suggest that if the identification of LS, as they are currently understood and used within primary education, is unreliable, as evident by the findings of the present study, this should constitute an additional reason why teachers should abandon the use of LS in instruction. Our study thus adds to the growing body of literature against the use of LS in education. Moreover, debunking the myth of LS as well as educating teachers in the use of evidence-based practices is recommended.
All subjects gave written informed consent in accordance with the Declaration of Helsinki. The study was granted ethical approval by the Institute of Educational Policy, supervised by the Greek Ministry of Education, Research and Religious Affairs.
MP-P conceived and designed the study. AB was consulted at the initial stages. MG collected the data. MP-P analyzed the data. AB and MP-P did the drafting, and revising of the work, and wrote the final manuscript. MP-P supervised the project from conception to submission.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Keywords: learning styles, auditory, visual, kinaesthetic, neuromyths, VAK, intelligence
Citation: Papadatou-Pastou M, Gritzali M and Barrable A (2018) The Learning Styles Educational Neuromyth: Lack of Agreement Between Teachers' Judgments, Self-Assessment, and Students' Intelligence. Front. Educ . 3:105. doi: 10.3389/feduc.2018.00105
Received: 26 September 2018; Accepted: 14 November 2018; Published: 29 November 2018.
Copyright © 2018 Papadatou-Pastou, Gritzali and Barrable. 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: Marietta Papadatou-Pastou, [email protected]
- Research article
- Open access
- Published: 01 October 2021
Adaptive e-learning environment based on learning styles and its impact on development students' engagement
- Hassan A. El-Sabagh ORCID: orcid.org/0000-0001-5463-5982 1 , 2
International Journal of Educational Technology in Higher Education volume 18 , Article number: 53 ( 2021 ) Cite this article
Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose of this paper is to design an adaptive e-learning environment based on students' learning styles and study the impact of the adaptive e-learning environment on students’ engagement. This research attempts as well to outline and compare the proposed adaptive e-learning environment with a conventional e-learning approach. The paper is based on mixed research methods that were used to study the impact as follows: Development method is used in designing the adaptive e-learning environment, a quasi-experimental research design for conducting the research experiment. The student engagement scale is used to measure the following affective and behavioral factors of engagement (skills, participation/interaction, performance, emotional). The results revealed that the experimental group is statistically significantly higher than those in the control group. These experimental results imply the potential of an adaptive e-learning environment to engage students towards learning. Several practical recommendations forward from this paper: how to design a base for adaptive e-learning based on the learning styles and their implementation; how to increase the impact of adaptive e-learning in education; how to raise cost efficiency of education. The proposed adaptive e-learning approach and the results can help e-learning institutes in designing and developing more customized and adaptive e-learning environments to reinforce student engagement.
In recent years, educational technology has advanced at a rapid rate. Once learning experiences are customized, e-learning content becomes richer and more diverse (El-Sabagh & Hamed, 2020 ; Yang et al., 2013 ). E-learning produces constructive learning outcomes, as it allows students to actively participate in learning at anytime and anyplace (Chen et al., 2010 ; Lee et al., 2019 ). Recently, adaptive e-learning has become an approach that is widely implemented by higher education institutions. The adaptive e-learning environment (ALE) is an emerging research field that deals with the development approach to fulfill students' learning styles by adapting the learning environment within the learning management system "LMS" to change the concept of delivering e-content. Adaptive e-learning is a learning process in which the content is taught or adapted based on the responses of the students' learning styles or preferences. (Normadhi et al., 2019 ; Oxman & Wong, 2014 ). By offering customized content, adaptive e-learning environments improve the quality of online learning. The customized environment should be adaptable based on the needs and learning styles of each student in the same course. (Franzoni & Assar, 2009 ; Kolekar et al., 2017 ). Adaptive e-learning changes the level of instruction dynamically based on student learning styles and personalizes instruction to enhance or accelerate a student's success. Directing instruction to each student's strengths and content needs can minimize course dropout rates, increase student outcomes and the speed at which they are accomplished. The personalized learning approach focuses on providing an effective, customized, and efficient path of learning so that every student can participate in the learning process (Hussein & Al-Chalabi, 2020 ). Learning styles, on the other hand, represent an important issue in learning in the twenty-first century, with students expected to participate actively in developing self-understanding as well as their environment engagement. (Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Truong, 2016 ).
In current conventional e-learning environments, instruction has traditionally followed a “one style fits all” approach, which means that all students are exposed to the same learning procedures. This type of learning does not take into account the different learning styles and preferences of students. Currently, the development of e-learning systems has accommodated and supported personalized learning, in which instruction is fitted to a students’ individual needs and learning styles (Beldagli & Adiguzel, 2010 ; Benhamdi et al., 2017 ; Pashler et al., 2008 ). Some personalized approaches let students choose content that matches their personality (Hussein & Al-Chalabi, 2020 ). The delivery of course materials is an important issue of personalized learning. Moreover, designing a well-designed, effective, adaptive e-learning system represents a challenge due to complication of adapting to the different needs of learners (Alshammari, 2016 ). Regardless of using e-learning claims that shifting to adaptive e-learning environments to be able to reinforce students' engagement. However, a learning environment cannot be considered adaptive if it is not flexible enough to accommodate students' learning styles. (Ennouamani & Mahani, 2017 ).
On the other hand, while student engagement has become a central issue in learning, it is also an indicator of educational quality and whether active learning occurs in classes. (Lee et al., 2019 ; Nkomo et al., 2021 ; Robinson & Hullinger, 2008 ). Veiga et al. ( 2014 ) suggest that there is a need for further research in engagement because assessing students’ engagement is a predictor of learning and academic progress. It is important to clarify the distinction between causal factors such as learning environment and outcome factors such as achievement. Accordingly, student engagement is an important research topic because it affects a student's final grade, and course dropout rate (Staikopoulos et al., 2015 ).
The Umm Al-Qura University strategic plan through common first-year deanship has focused on best practices that increase students' higher-order skills. These skills include communication skills, problem-solving skills, research skills, and creative thinking skills. Although the UQU action plan involves improving these skills through common first-year academic programs, the student's learning skills need to be encouraged and engaged more (Umm Al-Qura University Agency, 2020 ). As a result of the author's experience, The conventional methods of instruction in the "learning skills" course were observed, in which the content is presented to all students in one style that is dependent on understanding the content regardless of the diversity of their learning styles.
According to some studies (Alshammari & Qtaish, 2019 ; Lee & Kim, 2012 ; Shih et al., 2008 ; Verdú, et al., 2008 ; Yalcinalp & Avc, 2019 ), there is little attention paid to the needs and preferences of individual learners, and as a result, all learners are treated in the same way. More research into the impact of educational technologies on developing skills and performance among different learners is recommended. This “one-style-fits-all” approach implies that all learners are expected to use the same learning style as prescribed by the e-learning environment. Subsequently, a review of the literature revealed that an adaptive e-learning environment can affect learning outcomes to fill the identified gap. In conclusion: Adaptive e-learning environments rely on the learner's preferences and learning style as a reference that supports to create adaptation.
To confirm the above: the author conducted an exploratory study via an open interview that included some questions with a sample of 50 students in the learning skills department of common first-year. Questions asked about the difficulties they face when learning a "learning skills" course, what is the preferred way of course content. Students (88%) agreed that the way students are presented does not differ according to their differences and that they suffer from a lack of personal learning that is compatible with their style of work. Students (82%) agreed that they lack adaptive educational content that helps them to be engaged in the learning process. Accordingly, the author handled the research problem.
This research supplements to the existing body of knowledge on the subject. It is considered significant because it improves understanding challenges involved in designing the adaptive environments based on learning styles parameter. Subsequently, this paper is structured as follows: The next section presents the related work cited in the literature, followed by research methodology, then data collection, results, discussion, and finally, some conclusions and future trends are discussed.
This section briefly provides a thorough review of the literature about the adaptive E-learning environments based on learning styles.
Adaptive e-learning environments based on learning styles
The adaptive e-learning employment in higher education has been slower to evolve, and challenges that led to the slow implementation still exist. The learning management system offers the same tools to all learners, although individual learners need different details based on learning style and preferences. (Beldagli & Adiguzel, 2010 ; Kolekar et al., 2017 ). The interactive e-learning environment requisite evaluating the learner's desired learning style, before the course delivery, such as an online quiz or during the course delivery, such as tracking student reactions (DeCapua & Marshall, 2015 ).
In e-learning environments, adaptation is constructed on a series of well-designed processes to fit the instructional materials. The adaptive e-learning framework attempt to match instructional content to the learners' needs and styles. According to Qazdar et al. ( 2015 ), adaptive e-learning (AEL) environments rely on constructing a model of each learner's needs, preferences, and styles. It is well recognized that such adaptive behavior can increase learners' development and performance, thus enriching learning experience quality. (Shi et al., 2013 ). The following features of adaptive e-learning environments can be identified through diversity, interactivity, adaptability, feedback, performance, and predictability. Although adaptive framework taxonomy and characteristics related to various elements, adaptive learning includes at least three elements: a model of the structure of the content to be learned with detailed learning outcomes (a content model). The student's expertise based on success, as well as a method of interpreting student strengths (a learner model), and a method of matching the instructional materials and how it is delivered in a customized way (an instructional model) (Ali et al., 2019 ). The number of adaptive e-learning studies has increased over the last few years. Adaptive e-learning is likely to increase at an accelerating pace at all levels of instruction (Hussein & Al-Chalabi, 2020 ; Oxman & Wong, 2014 ).
Many studies assured the power of adaptive e-learning in delivering e-content for learners in a way that fitting their needs, and learning styles, which helps improve the process of students' acquisition of knowledge, experiences and develop their higher thinking skills (Ali et al., 2019 ; Behaz & Djoudi, 2012 ; Chun-Hui et al., 2017 ; Daines et al., 2016 ; Dominic et al., 2015 ; Mahnane et al., 2013 ; Vassileva, 2012 ). Student characteristics of learning style are recognized as an important issue and a vital influence in learning and are frequently used as a foundation to generate personalized learning experiences (Alshammari & Qtaish, 2019 ; El-Sabagh & Hamed, 2020 ; Hussein & Al-Chalabi, 2020 ; Klasnja-Milicevic et al., 2011 ; Normadhi et al., 2019 ; Ozyurt & Ozyurt, 2015 ).
The learning style is a parameter of designing adaptive e-learning environments. Individuals differ in their learning styles when interacting with the content presented to them, as many studies emphasized the relationship between e-learning and learning styles to be motivated in learning situations, consequently improving the learning outcomes (Ali et al., 2019 ; Alshammari, 2016 ; Alzain et al., 2018a , b ; Liang, 2012 ; Mahnane et al., 2013 ; Nainie et al., 2010 ; Velázquez & Assar, 2009 ). The word "learning style" refers to the process by which the learner organizes, processes, represents, and combines this information and stores it in his cognitive source, then retrieves the information and experiences in the style that reflects his technique of communicating them. (Fleming & Baume, 2006 ; Jaleel & Thomas, 2019 ; Jonassen & Grabowski, 2012 ; Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Pashler et al., 2008 ; Willingham et al., 2105 ; Zhang, 2017 ). The concept of learning style is founded based on the fact that students vary in their styles of receiving knowledge and thought, to help them recognizing and combining information in their mind, as well as acquire experiences and skills. (Naqeeb, 2011 ). The extensive scholarly literature on learning styles is distributed with few strong experimental findings (Truong, 2016 ), and a few findings on the effect of adapting instruction to learning style. There are many models of learning styles (Aldosarim et al., 2018 ; Alzain et al., 2018a , 2018b ; Cletus & Eneluwe, 2020 ; Franzoni & Assar, 2009 ; Willingham et al., 2015 ), including the VARK model, which is one of the most well-known models used to classify learning styles. The VARK questionnaire offers better thought about information processing preferences (Johnson, 2009 ). Fleming and Baume ( 2006 ) developed the VARK model, which consists of four students' preferred learning types. The letter "V" represents for visual and means the visual style, while the letter "A" represents for auditory and means the auditory style, and the letter "R/W" represents "write/read", means the reading/writing style, and the letter "K" represents the word "Kinesthetic" and means the practical style. Moreover, VARK distinguishes the visual category further into graphical and textual or visual and read/write learners (Murphy et al., 2004 ; Leung, et al., 2014 ; Willingham et al., 2015 ). The four categories of The VARK Learning Style Inventory are shown in the Fig. 1 below.
VARK learning styles
According to the VARK model, learners are classified into four groups representing basic learning styles based on their responses which have 16 questions, there are four potential responses to each question, where each answer agrees to one of the extremes of the dimension (Hussain, 2017 ; Silva, 2020 ; Zhang, 2017 ) to support instructors who use it to create effective courses for students. Visual learners prefer to take instructional materials and send assignments using tools such as maps, graphs, images, and other symbols, according to Fleming and Baume ( 2006 ). Learners who can read–write prefer to use written textual learning materials, they use glossaries, handouts, textbooks, and lecture notes. Aural learners, on the other hand, prefer to learn through spoken materials, dialogue, lectures, and discussions. Direct practice and learning by doing are preferred by kinesthetic learners (Becker et al., 2007 ; Fleming & Baume, 2006 ; Willingham et al., 2015 ). As a result, this research work aims to provide a comprehensive discussion about how these individual parameters can be applied in adaptive e-learning environment practices. Dominic et al., ( 2015 ) presented a framework for an adaptive educational system that personalized learning content based on student learning styles (Felder-Silverman learning model) and other factors such as learners' learning subject competency level. This framework allowed students to follow their adaptive learning content paths based on filling in "ils" questionnaire. Additionally, providing a customized framework that can automatically respond to students' learning styles and suggest online activities with complete personalization. Similarly, El Bachari et al. ( 2011 ) attempted to determine a student's unique learning style and then adapt instruction to that individual interests. Adaptive e-learning focused on learner experience and learning style has a higher degree of perceived usability than a non-adaptive e-learning system, according to Alshammari et al. ( 2015 ). This can also improve learners' satisfaction, engagement, and motivation, thus improving their learning.
According to the findings of (Akbulut & Cardak, 2012 ; Alshammari & Qtaish, 2019 ; Alzain et al., 2018a , b ; Shi et al., 2013 ; Truong, 2016 ), adaptation based on a combination of learning style, and information level yields significantly better learning gains. Researchers have recently initiated to focus on how to personalize e-learning experiences using personal characteristics such as the student's preferred learning style. Personal learning challenges are addressed by adaptive learning programs, which provide learners with courses that are fit to their specific needs, such as their learning styles.
- Student engagement
Previous research has emphasized that student participation is a key factor in overcoming academic problems such as poor academic performance, isolation, and high dropout rates (Fredricks et al., 2004 ). Student participation is vital to student learning, especially in an online environment where students may feel isolated and disconnected (Dixson, 2015 ). Student engagement is the degree to which students consciously engage with a course's materials, other students, and the instructor. Student engagement is significant for keeping students engaged in the course and, as a result, in their learning (Barkley & Major, 2020 ; Lee et al., 2019 ; Rogers-Stacy, et al, 2017 ). Extensive research was conducted to investigate the degree of student engagement in web-based learning systems and traditional education systems. For instance, using a variety of methods and input features to test the relationship between student data and student participation (Hussain et al., 2018 ). Guo et al. ( 2014 ) checked the participation of students when they watched videos. The input characteristics of the study were based on the time they watched it and how often students respond to the assessment.
Atherton et al. ( 2017 ) found a correlation between the use of course materials and student performance; course content is more expected to lead to better grades. Pardo et al., ( 2016 ) found that interactive students with interactive learning activities have a significant impact on student test scores. The course results are positively correlated with student participation according to previous research. For example, Atherton et al. ( 2017 ) explained that students accessed learning materials online and passed exams regularly to obtain higher test scores. Other studies have shown that students with higher levels of participation in questionnaires and course performance tend to perform well (Mutahi et al., 2017 ).
Skills, emotion, participation, and performance, according to Dixson ( 2015 ), were factors in online learning engagement. Skills are a type of learning that includes things like practicing on a daily foundation, paying attention while listening and reading, and taking notes. Emotion refers to how the learner feels about learning, such as how much you want to learn. Participation refers to how the learner act in a class, such as chat, discussion, or conversation. Performance is a result, such as a good grade or a good test score. In general, engagement indicated that students spend time, energy learning materials, and skills to interact constructively with others in the classroom, and at least participate in emotional learning in one way or another (that is, be motivated by an idea, willing to learn and interact). Student engagement is produced through personal attitudes, thoughts, behaviors, and communication with others. Thoughts, effort, and feelings to a certain level when studying. Therefore, the student engagement scale attempts to measure what students are doing (thinking actively), how they relate to their learning, and how they relate to content, faculty members, and other learners including the following factors as shown in Fig. 2 . (skills, participation/interaction, performance, and emotions). Hence, previous research has moved beyond comparing online and face-to-face classes to investigating ways to improve online learning (Dixson, 2015 ; Gaytan & McEwen, 2007 ; Lévy & Wakabayashi, 2008 ; Mutahi et al., 2017 ). Learning effort, involvement in activities, interaction, and learning satisfaction, according to reviews of previous research on student engagement, are significant measures of student engagement in learning environments (Dixson, 2015 ; Evans et al., 2017 ; Lee et al., 2019 ; Mutahi et al., 2017 ; Rogers-Stacy et al., 2017 ). These results point to several features of e-learning environments that can be used as measures of student participation. Successful and engaged online learners learn actively, have the psychological inspiration to learn, make good use of prior experience, and make successful use of online technology. Furthermore, they have excellent communication abilities and are adept at both cooperative and self-directed learning (Dixson, 2015 ; Hong, 2009 ; Nkomo et al., 2021 ).
Overview of designing the adaptive e-learning environment
The paper follows the (ADDIE) Instructional Design Model: analysis, design, develop, implement, and evaluate to answer the first research question. The adaptive learning environment offers an interactive decentralized media environment that takes into account individual differences among students. Moreover, the environment can spread the culture of self-learning, attract students, and increase their engagement in learning.
Any learning environment that is intended to accomplish a specific goal should be consistent to increase students' motivation to learn. so that they have content that is personalized to their specific requirements, rather than one-size-fits-all content. As a result, a set of instructional design standards for designing an adaptive e-learning framework based on learning styles was developed according to the following diagram (Fig. 3 ).
The ID (model) of the adaptive e-learning environment
According to the previous figure, The analysis phase included identifying the course materials and learning tools (syllabus and course plan modules) used for the study. The learning objectives were included in the high-level learning objectives (C4-C6: analysis, synthesis, evaluation).
The design phase included writing SMART objectives, the learning materials were written within the modules plan. To support adaptive learning, four content paths were identified, choosing learning models, processes, and evaluation. Course structure and navigation were planned. The adaptive structural design identified the relationships between the different components, such as introduction units, learning materials, quizzes. Determining the four path materials. The course instructional materials were identified according to the following Figure 4 .
Adaptive e-course design
The development phase included: preparing and selecting the media for the e-course according to each content path in an adaptive e-learning environment. During this process, the author accomplished the storyboard and the media to be included on each page of the storyboard. A category was developed for the instructional media for each path (Fig. 5 )
Roles and deployment diagram of the adaptive e-learning environment
The author developed a learning styles questionnaire via a mobile App. as follows: https://play.google.com/store/apps/details?id=com.pointability.vark . Then, the students accessed the adaptive e-course modules based on their learning styles.
The Implementation phase involved the following: The professional validation of the course instructional materials. Expert validation is used to evaluate the consistency of course materials (syllabi and modules). The validation was performed including the following: student learning activities, learning implementation capability, and student reactions to modules. The learner's behaviors, errors, navigation, and learning process are continuously geared toward improving the learner's modules based on the data the learner gathered about him.
The Evaluation phase included five e-learning specialists who reviewed the adaptive e-learning. After that, the framework was revised based on expert recommendations and feedback. Content assessment, media evaluation in three forms, instructional design, interface design, and usage design included in the evaluation. Adaptive learners checked the proposed framework. It was divided into two sections. Pilot testing where the proposed environment was tested by ten learners who represented the sample in the first phase. Each learner's behavior was observed, questions were answered, and learning control, media access, and time spent learning were all verified.
Research purpose and questions.
This research aims to investigate the impact of designing an adaptive e-learning environment on the development of students' engagement. The research conceptual framework is illustrated in Fig. 6 . Therefore, the articulated research questions are as follows: the main research question is "What is the impact of an adaptive e-learning environment based on (VARK) learning styles on developing students' engagement? Accordingly, there are two sub research questions a) "What is the instructional design of the adaptive e-learning environment?" b) "What is the impact of an adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation, performance, emotional) in comparison with conventional e-learning?".
The conceptual framework (model) of the research questions
The research aims to verify the validity of the following hypothesis:
There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale.
There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group.
This research was a quasi-experimental research with the pretest-posttest. Research variables were independent and dependent as shown in the following Fig. 7 .
Research "Experimental" design
Both groups were informed with the learning activities tracks, the experimental group was instructed to use the adaptive learning environment to accomplish the learning goals; on the other hand, the control group was exposed to the conventional e-learning environment without the adaptive e-learning parameters.
The sample consisted of students studying the "learning skills" course in the common first-year deanship aged between (17–18) years represented the population of the study. All participants were chosen in the academic year 2109–2020 at the first term which was taught by the same instructors. The research sample included two classes (118 students), selected randomly from the learning skills department. First-group was randomly assigned as the control group (N = 58, 31 males and 27 females), the other was assigned as experimental group (N = 60, 36 males and 24 females) was assigned to the other class. The following Table 1 shows the distribution of students' sample "Demographics data".
The instructional materials were not presented to the students before. The control group was expected to attend the conventional e-learning class, where they were provided with the learning environment without adaptive e-learning parameter based on the learning styles that introduced the "learning skills" course. The experimental group was exposed to the use of adaptive e-learning based on learning styles to learn the same course instructional materials within e-course. Moreover, all the student participants were required to read the guidelines to indicate their readiness to participate in the research experiment with permission.
In this research, the measuring tools included the VARK questionnaire and the students' engagement scale including the following factors (skills, participation/interaction, performance, emotional). To begin, the pre-post scale was designed to assess the level of student engagement related to the "learning skills" course before and after participating in the experiment.
Questionnaires are a common method for collecting data in education research (McMillan & Schumacher, 2006 ). The VARK questionnaire had been organized electronically and distributed to the student through the developed mobile app and registered on the UQU system. The questionnaire consisted of 16 items within the scale as MCQ classified into four main factors (kinesthetic, auditory, visual, and R/W).
Reliability and Validity of The VARK questionnaire
For reliability analysis, Cronbach’s alpha is used for evaluating research internal consistency. Internal consistency was calculated through the calculation of correlation of each item with the factor to which it fits and correlation among other factors. The value of 0.70 and above are normally recognized as high-reliability values (Hinton et al., 2014 ). The Cronbach's Alpha correlation coefficient for the VARK questionnaire was 0.83, indicating that the questionnaire was accurate and suitable for further research.
Students' engagement scale
The engagement scale was developed after a review of the literature on the topic of student engagement. The Dixson scale was used to measure student engagement. The scale consisted of 4 major factors as follows (skills, participation/interaction, performance, emotional). The author adapted the original "Dixson scale" according to the following steps. The Dixson scale consisted of 48 statements was translated and accommodated into Arabic by the author. After consulting with experts, the instrument items were reduced to 27 items after adaptation according to the university learning environment. The scale is rated on a 5-point scale.
The final version of the engagement scale comprised 4 factors as follows: The skills engagement included (ten items) to determine keeping up with, reading instructional materials, and exerting effort. Participation/interaction engagement involved (five items) to measure having fun, as well as regularly engaging in group discussion. The performance engagement included (five items) to measure test performance and receiving a successful score. The emotional engagement involved (seven items) to decide whether or not the course was interesting. Students can access to respond engagement scale from the following link: http://bit.ly/2PXGvvD . Consequently, the objective of the scale is to measure the possession of common first-year students of the basic engagement factors before and after instruction with adaptive e-learning compared to conventional e-learning.
Reliability and validity of the engagement scale
The alpha coefficient of the scale factors scores was presented. All four subscales have a strong degree of internal accuracy (0.80–0.87), indicating strong reliability. The overall reliability of the instruments used in this study was calculated using Alfa-alpha, Cronbach's with an alpha value of 0.81 meaning that the instruments were accurate. The instruments used in this research demonstrated strong validity and reliability, allowing for an accurate assessment of students' engagement in learning. The scale was applied to a pilot sample of 20 students, not including the experimental sample. The instrument, on the other hand, had a correlation coefficient of (0.74–0.82), indicating a degree of validity that enables the instrument's use. Table 2 shows the correlation coefficient and Cronbach's alpha based on the interaction scale.
On the other hand, to verify the content validity; the scale was to specialists to take their views on the clarity of the linguistic formulation and its suitability to measure students' engagement, and to suggest what they deem appropriate in terms of modifications.
To calculate the homogeneity and group equivalence between both groups, the validity of the first hypothesis was examined which stated "There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale", the author applied the engagement scale to both groups beforehand, and the scores of the pre-application were examined to verify the equivalence of the two groups (experimental and control) in terms of students' engagement.
The t-test of independent samples was calculated for the engagement scale to confirm the homogeneity of the two classes before the experiment. The t-values were not significant at the level of significance = 0.05, meaning that the two groups were homogeneous in terms of students' engagement scale before the experiment.
Since there was no significant difference in the mean scores of both groups ( p > 0.05), the findings presented in Table 3 showed that there was no significant difference between both experimental and control groups in engagement as a whole, and each student engagement factor separately. The findings showed that the two classes were similar before start of research experiment.
Learner content path in adaptive e-learning environment
The previous well-designed processes are the foundation for adaptation in e-learning environments. There are identified entries for accommodating materials, including classification depending on learning style.: kinesthetic, auditory, visual, and R/W. The present study covered the 1st semester during the 2019/2020 academic year. The course was divided into modules that concentrated on various topics; eleven of the modules included the adaptive learning exercise. The exercises and quizzes were assigned to specific textbook modules. To reduce irrelevant variation, all objects of the course covered the same content, had equal learning results, and were taught by the same instructor.
The experimental group—in which students were asked to bring smartphones—was taught, where the how-to adaptive learning application for adaptive learning was downloaded, and a special account was created for each student, followed by access to the channel designed by the through the application, and the students were provided with instructions and training on how entering application with the appropriate default element of the developed learning objects, while the control group used the variety of instructional materials in the same course for the students.
In this adaptive e-course, students in the experimental group are presented with a questionnaire asked to answer that questions via a developed mobile App. They are provided with four choices. Students are allowed to answer the questions. The correct answer is shown in the students' responses to the results, but the learning module is marked as incomplete. If a student chooses to respond to a question, the correct answer is found immediately, regardless of the student's reaction.
Figure 8 illustrates a visual example from learning styles identification through responding VARK Questionnaire. The learning process experienced by the students in this adaptive Learning environment is as shown in Fig. 4 . Students opened the adaptive course link by tapping the following app " https://play.google.com/store/apps/details?id=com.pointability.vark ," which displayed the appropriate positioning of both the learning skills course and the current status of students. It directed students to the learning skills that they are interested in learning more. Once students reached a specific situation in the e-learning environment, they could access relevant digital instructional materials. Students were then able to progress through the various styles offered by the proposed method, giving them greater flexibility in their learning pace.
Visual example from "learning of the learning styles" identification and adaptive e-learning course process
The "flowchart" diagram below illustrates the learner's path in an adaptive e-learning environment, depending on the (VARK) learning styles (visual, auditory, kinesthetic, reading/writing) (Fig. 9 ).
Student learning path
According to the previous design model of the adaptive framework, the students responded "Learning Styles" questionnaire. Based on each student's results, the orientation of students will direct to each of "Visual", "Aural", "Read-Write", and "Kinesthetic". The student took at the beginning the engagement scale online according to their own pace. When ready, they responded "engagement scale".
Based on the results, the system produced an individualized learning plan to fill in the gap based on the VARK questionnaire's first results. The learner model represents important learner characteristics such as personal information, knowledge level, and learning preferences. Pre and post measurements were performed for both experimental and control groups. The experimental group was exposed only to treatment (using the adaptive learning environment).
To address the second question, which states: “What is the impact "effect" of adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation/interaction, performance, emotional) in comparison with conventional e-learning?
The validity of the second hypothesis of the research hypothesis was tested, which states " There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group". To test the hypothesis, the arithmetic means, standard deviations, and "T"-test values were calculated for the results of the two research groups in the application of engagement scale factors".
Table 4 . indicates that students in the experimental group had significantly higher mean of engagement post-test (engagement factors items) scores than students in the control group ( p < 0.05).
The experimental research was performed to evaluate the impact of the proposed adaptive e-learning. Independent sample t-tests were used to measure the previous behavioral engagement of the two groups related to topic of this research. Subsequently, the findings stated that the experimental group students had higher learning achievement than those who were taught using the conventional e-learning approach.
To verify the effect size of the independent variable in terms of the dependent variable, Cohen (d) was used to investigate that adaptive learning can significantly students' engagement. According to Cohen ( 1992 ), ES of 0.20 is small, 0.50 is medium, and 0.80 is high. In the post-test of the student engagement scale, however, the effect size between students' scores in the experimental and control groups was calculated using (d and r) using means and standard deviations. Cohen's d = 0.826, and Effect-size r = 0.401, according to the findings. The ES of 0.824 means that the treated group's mean is in the 79th percentile of the control group (Large effect). Effect sizes can also be described as the average percentile rank of the average treated learner compared to the average untreated learner in general. The mean of the treated group is at the 50th percentile of the untreated group, indicating an ES of 0.0. The mean of the treated group is at the 79th percentile of the untreated group, with an ES of 0.8. The results showed that the dependent variable was strongly influenced in the four behavioral engagement factors: skills: performance, participation/interaction, and emotional, based on the fact that effect size is a significant factor in determining the research's strength.
Discussions and limitations
This section discusses the impact of an adaptive e-learning environment on student engagement development. This paper aimed to design an adaptive e-learning environment based on learning style parameters. The findings revealed that factors correlated to student engagement in e-learning: skills, participation/interaction, performance, and emotional. The engagement factors are significant because they affect learning outcomes (Nkomo et al., 2021 ). Every factor's items correlate to cognitive process-related activities. The participation/interaction factor, for example, referred to, interactions with the content, peers, and instructors. As a result, student engagement in e-learning can be predicted by interactions with content, peers, and instructors. The results are in line with previous research, which found that customized learning materials are important for increasing students' engagement. Adaptive e-learning based on learning styles sets a strong emphasis on behavioral engagement, in which students manage their learning while actively participating in online classes to adapt instruction according to each learning style. This leads to improved learning outcomes (Al-Chalabi & Hussein, 2020 ; Chun-Hui et al., 2017 ; Hussein & Al-Chalabi, 2020 ; Pashler et al., 2008 ). The experimental findings of this research showed that students who learned through adaptive eLearning based on learning styles learned more; as learning styles are reflected in this research as one of the generally assumed concerns as a reference for adapting e-content path. Students in the experimental group reported that the adaptive eLearning environment was very interesting and able to attract their attention. Those students also indicated that the adaptive eLearning environment was particularly useful because it provided opportunities for them to recall the learning content, thus enhancing their overall learning impression. This may explain why students in the experimental group performed well in class and showed more enthusiasm than students in the control group. This research compared an adaptive e-learning environment to a conventional e-learning approach toward engagement in a learning skills course through instructional content delivery and assessment. It can also be noticed that the experimental group had higher participation than the control group, indicating that BB activities were better adapted to the students' learning styles. Previous studies have agreed on the effectiveness of adaptive learning; it provides students with quality opportunity that is adapted to their learning styles, and preferences (Alshammari, 2016 ; Hussein & Al-Chalabi, 2020 ; Roy & Roy, 2011 ; Surjono, 2014 ). However, it should be noted that this study is restricted to one aspect of content adaptation and its factors, which is learning materials adapting based on learning styles. Other considerations include content-dependent adaptation. These findings are consistent with other studies, such as (Alshammari & Qtaish, 2019 ; Chun-Hui et al., 2017 ), which have revealed the effectiveness of the adaptive e-learning environment. This research differs from others in that it reflects on the Umm Al-Qura University as a case study, VARK Learning styles selection, engagement factors, and the closed learning management framework (BB).
The findings of the study revealed that adaptive content has a positive impact on adaptive individuals' achievement and student engagement, based on their learning styles (kinesthetic; auditory; visual; read/write). Several factors have contributed to this: The design of adaptive e-content for learning skills depended on introducing an ideal learning environment for learners, and providing support for learning adaptation according to the learning style, encouraging them to learn directly, achieving knowledge building, and be enjoyable in the learning process. Ali et al. ( 2019 ) confirmed that, indicating that education is adapted according to each individual's learning style, needs, and characteristics. Adaptive e-content design that allows different learners to think about knowledge by presenting information and skills in a logical sequence based on the adaptive e-learning framework, taking into account its capabilities as well as the diversity of its sources across the web, and these are consistent with the findings of (Alshammari & Qtaish, 2019 ).
Accordingly, the previous results are due to the following: good design of the adaptive e-learning environment in light of the learning style and educational preferences according to its instructional design (ID) standards, and the provision of adaptive content that suits the learners' needs, characteristics, and learning style, in addition to the diversity of course content elements (texts, static images, animations, and video), variety of tests and activities, diversity of methods of reinforcement, return and support from the instructor and peers according to the learning style, as well as it allows ease of use, contains multiple and varied learning sources, and allows referring to the same point when leaving the environment.
Several studies have shown that using adaptive eLearning technologies allows students to improve their learning knowledge and further enhance their engagement in issues such as "skills, performance, interaction, and emotional" (Ali et al., 2019 ; Graf & Kinshuk, 2007 ; Murray & Pérez, 2015 ); nevertheless, Murray and Pérez ( 2015 ) revealed that adaptive learning environments have a limited impact on learning outcome.
The restricted empirical findings on the efficacy of adapting teaching to learning style are mixed. (Chun-Hui et al., 2017 ) demonstrated that adaptive eLearning technologies can be beneficial to students' learning and development. According to these findings, adaptive eLearning can be considered a valuable method for learning because it can attract students' attention and promote their participation in educational activities. (Ali et al., 2019 ); however, only a few recent studies have focused on how adaptive eLearning based on learning styles fits in diverse cultural programs. (Benhamdi et al., 2017 ; Pashler et al., 2008 ).
The experimental results revealed that the proposed environment significantly increased students' learning achievements as compared to the conventional e-learning classroom (without adaptive technology). This means that the proposed environment's adaptation could increase students' engagement in the learning process. There is also evidence that an adaptive environment positively impacts other aspects of quality such as student engagement (Murray & Pérez, 2015 ).
Conclusions and implications
Although this field of research has stimulated many interests in recent years, there are still some unanswered questions. Some research gaps are established and filled in this study by developing an active adaptive e-learning environment that has been shown to increase student engagement. This study aimed to design an adaptive e-learning environment for performing interactive learning activities in a learning skills course. The main findings of this study revealed a significant difference in learning outcomes as well as positive results for adaptive e-learning students, indicating that it may be a helpful learning method for higher education. It also contributed to the current adaptive e-learning literature. The findings revealed that adaptive e-learning based on learning styles could help students stay engaged. Consequently, adaptive e-learning based on learning styles increased student engagement significantly. According to research, each student's learning style is unique, and they prefer to use different types of instructional materials and activities. Furthermore, students' preferences have an impact on the effectiveness of learning. As a result, the most effective learning environment should adjust its output to the needs of the students. The development of high-quality instructional materials and activities that are adapted to students' learning styles will help them participate and be more motivated. In conclusion, learning styles are a good starting point for creating instructional materials based on learning theories.
This study's results have important educational implications for future studies on the effect of adaptive e-learning on student interaction. First, the findings may provide data to support the development and improvement of adaptive environments used in blended learning. Second, the results emphasize the need for more quasi-experimental and descriptive research to better understand the benefits and challenges of incorporating adaptive e-learning in higher education institutions. Third, the results of this study indicate that using an adaptive model in an adaptive e-learning environment will encourage, motivate, engage, and activate students' active learning, as well as facilitate their knowledge construction, rather than simply taking in information passively. Fourth, new research is needed to design effective environments in which adaptive learning can be used in higher education institutions to increase academic performance and motivation in the learning process. Finally, the study shows that adaptive e-learning allows students to learn individually, which improves their learning and knowledge of course content, such as increasing their knowledge of learning skills course topics beyond what they can learn in a conventional e-learning classroom.
Contribution to research
The study is intended to provide empirical evidence of adaptive e-learning on student engagement factors. This research, on the other hand, has practical implications for higher education stakeholders, as it is intended to provide university faculty members with learning approaches that will improve student engagement. It is also expected to offer faculty a framework for designing personalized learning environments based on learning styles in various learning situations and designing more adaptive e-learning environments.
Students with their preferred learning styles are more likely to enjoy learning if they are provided with a variety of instructional materials such as references, interactive media, videos, podcasts, storytelling, simulation, animation, problem-solving, games, and accessible educational tools in an e-learning environment. Also, different learning strategies can be accommodated. Other researchers would be able to conduct future studies on the use of the "adaptive e-learning" approach throughout the instructional process, at different phases of learning, and in various e-courses as a result of the current study. Meanwhile, the proposed environment's positive impact on student engagement gained considerable interest for future educational applications. Further research on learning styles in different university colleges could contribute to a foundation for designing adaptive e-courses based on students' learning styles and directing more future research on learning styles.
Implications for practice or policy:
Adaptive e-learning focused on learning styles would help students become more engaged.
Proving the efficacy of an adaptive e-learning environment via comparison with conventional e-learning .
Availability of data and materials
The author confirms that the data supporting the findings of this study are based on the research tools which were prepared and explained by the author and available on the links stated in the research instruments sub-section. The data analysis that supports the findings of this study is available on request from the corresponding author.
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The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for the continuous support. This work was supported financially by the Deanship of Scientific Research at Umm Al-Qura University to Dr.: Hassan Abd El-Aziz El-Sabagh. (Grant Code: 18-EDU-1-01-0001).
Hassan A. El-Sabagh is an assistant professor in the E-Learning Deanship and head of the Instructional Programs Department, Umm Al-Qura University, Saudi Arabia, where he has worked since 2012. He has extensive experience in the field of e-learning and educational technologies, having served primarily at the Educational Technology Department of the Faculty of Specific Education, Mansoura University, Egypt since 1997. In 2011, he earned a Ph.D. in Educational Technology from Dresden University of Technology, Germany. He has over 14 papers published in international journals/conference proceedings, as well as serving as a peer reviewer in several international journals. His current research interests include eLearning Environments Design, Online Learning; LMS-based Interactive Tools, Augmented Reality, Design Personalized & Adaptive Learning Environments, and Digital Education, Quality & Online Courses Design, and Security issues of eLearning Environments. (E-mail: [email protected]; [email protected]).
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E-Learning Deanship, Umm Al-Qura University, Mecca, Saudi Arabia
Hassan A. El-Sabagh
Faculty of Specific Education, Mansoura University, Mansoura, Egypt
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El-Sabagh, H.A. Adaptive e-learning environment based on learning styles and its impact on development students' engagement. Int J Educ Technol High Educ 18 , 53 (2021). https://doi.org/10.1186/s41239-021-00289-4
Received : 24 May 2021
Accepted : 19 July 2021
Published : 01 October 2021
DOI : https://doi.org/10.1186/s41239-021-00289-4
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