different learning styles of students research paper

• 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

371k Accesses

45 Citations

4 Altmetric

Metrics details

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).

Conclusions

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.

Peer Review reports

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.

Data collection

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 analysis

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.

Abbreviations

Cumulative Grade Point Average

Dokuz Eylul University

Grascha-Riechmann Learning Style Scales

Learning Style Inventory

Learning Style Questionnaire

Hunt DE. Learning style and student needs: An introduction to conceptual level. In: NASSP, editor. Student learning styles: diagnosing and prescribing programs. Reston: National Association of Secondary School Principals; 1979. p. 27–38.

Google Scholar  

Arthurs JB. A juggling act in the classroom: managing different learning styles. Teach Learn Nurs. 2007;2(1):2–7.

Article   Google Scholar  

Coffield F, Moseley D, Hall E, Ecclestone K. Learning styles and pedagogy in post 16 learning: a systematic and critical review. London: The Learning and Skills Research Centre; 2004.

Ramsden P. Learning to teach in higher education. Oxon: Routledge; 2003.

Book   Google Scholar  

Mountford H, Jones S, Tucker B. Learning styles of entry-level physiotherapy students. Adv Physiother. 2006;8(Suppl 3):128–36.

Huston JL, Huston TL. How learning style and personality type can affect performance. The Health Care Supervisor. 1995;13(Suppl 4):38–45.

Dunn RS, Dunn KJ. Teaching secondary students through their individual learning styles: practical approaches for grades 7–12. New Jersey: Prentice Hall; 1993.

Saban A. Ögrenme-Ögretme Süreci Yeni Teori ve Yaklasimlar. Ankara: Nobel; 2005.

Brown T, Zoghi M, Williams B, Jaberzadeh S, Roller L, Palermo C, McKenna L, Wright C, Baird M, Schneider-Kolsky M. Are learning style preferences of health science students predictive of their attitudes towards e-learning? AJET. 2009;25:4.

Felder RM, Brent R. Understanding student differences. J Eng Educ. 2005;94(Suppl 1):57–72.

Carmo L, Gomes A, Pereira F, Mendes A. Learning styles and problem solving strategies. Proceedings of 3rd E-Learning Conference. 2006:7–8.

Hobbs C, Henley E, Higgs J, Williams V. Clinical education program strategies for challenging times. Focus on Health Professional Education: A Multidisciplinary Journal. 2000;2:7.

Wessel J, Loomis J, Rennie S, Brook P, Hoddinott J, Aherne M. Learning styles and perceived problem-solving ability of students in a baccalaureate physiotherapy programme. Physiother Theor Pr. 1999;15(Suppl 1):17–24.

Brown T, Cosgriff T, French G. Learning style preferences of occupational therapy, physiotherapy and speech pathology students: a comparative study. Int J Allied Health Sci Prac. 2008;6:1–12.

Linares AZ. Learning styles of students and faculty in selected health care professions. J Nurs Educ. 1999;38(Suppl 9):407–14.

Zoghi M, Brown T, Williams B, Roller L, Jaberzadeh S, Palermo C, McKenna L, Wright C, Baird M, Schneider-Kolsky M. Learning style preferences of Australian health science students. J Allied Health. 2010;39(Suppl 2):95–103.

Hauer P, Straub C, Wolf S. Learning styles of allied health students using Kolb's LSI-IIa. J Allied Health. 2005;34(Suppl 3):177–82.

Severiens S, Dam GT. Gender and gender identity differences in learning styles. Educ Psychol-Uk. 1997;17(Suppl 1):79–93.

Yazici HJ. Role of learning style preferences and interactive response systems on student learning outcomes. Int J Inf Oper Manag Educ. 2016;6(Suppl 2):109–34.

Ghazivakili Z, Nia RN, Panahi F, Karimi M, Gholsorkhi H, Ahmadi Z. The role of critical thinking skills and learning styles of university students in their academic performance. J Adv Med Educ Prof. 2014;2(Suppl 3):95–102.

Good JP, Ramos D, D'Amore DC. Learning style preferences and academic success of preclinical allied health students. J Allied Health. 2013;42(Suppl 4):81–90.

Riechmann SW, Grasha AF. A rational approach to developing and assessing the construct validity of a student learning style scales instrument. J Psychol. 1974;87(Suppl 2):213–23.

Grasha AF. Teaching with style: a practical guide to enhancing learning by understanding teaching and learning style. Pittsburgh: Alliance publishers; 1996.

Kumar P, Kumar A, Smart K. Assessing the impact of instructional methods and information technology on student learning styles. Issues in Informing Science and Information Technology. 2004;1:533–44.

Uzuntiryaki E, Bilgin I, Geban O. The effect of learning styles on high school students’ achievement and attitudes in Chemistry 2003. https://eric.ed.gov/?id=ED475483 . Accessed 15 May 2016.

Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. Oxford: Oxford University Press; 2014.

Forbes R, Nolan D. Factors associated with patient satisfaction in student-led physiotherapy clinics: a qualitative study. Physiotherapy Theory and Practice. 2018;34(9):705–13.

Barris R, Kielhofner G, Bauer D. Learning preferences, values. and student satisfaction J Allied Health. 1985;14(Suppl 1):13–23.

Van Langenberghe HV. Evaluation of students’ approaches to studying in a problem-based physical therapy curriculum. Phys Ther. 1988;68(Suppl 4):522–7.

Katz N, Heimann N. Learning style of students and practitioners in five health professions. Occup Ther J Res. 1991;11(Suppl 4):238–44.

Honey P, Mumford A. The manual of learning styles. 3rd ed. Maidenhead: Peter Honey; 1992.

Alsop A, Ryan S. Making the most of fieldwork education: a practical approach. California: Springer; 2013.

Nelson B, Dunn R, Griggs SA, Primavera L. Effects of learning style intervention on college students retention and achievement. J Coll Stud Dev. 1993;34:364–9.

Sandmire DA, Boyce PF. Pairing of opposite learning styles among allied health students: effects on collaborative performance. J Allied Health. 2004;33(Suppl 2):156–63.

Hess D, Frantz JM. Understanding the learning styles of undergraduate physiotherapy students. Afr J Health Prof Educ. 2014;6(Suppl 1):45–7.

Ford JH, Robinson JM, Wise ME. Adaptation of the Grasha Riechman student learning style survey and teaching style inventory to assess individual teaching and learning styles in a quality improvement collaborative. BMC Medical Education. 2016;16:252.

Kell C, Rv D. Student learning preferences reflect curricular change. Med Teach. 2002;24(Suppl 1):32–40.

Rappolt S, Tassone M. How rehabilitation therapists gather, evaluate, and implement new knowledge. J Contin. Educ Health. 2002;22(Suppl 3):170–80.

Al Maghraby MA, Alshami AM. Learning style and teaching method preferences of Saudi students of physical therapy. Journal of family & community medicine. 2013;20(Suppl 3):192.

Celik Y, Ceylantekin Y, Kilic I. The evaluation of simulation maket in nursing education and the determination of learning style of students. Int J Health Sci. 2017;11(3):74–9.

Pashler H, McDaniel M, Rohrer D, Bjork R. Learning styles: concepts and evidence. Psychol Sci Public Interest. 2008;9(3):105–1.

Cassidy S. Learning styles: an overview of theories, models, and measures. Educ Psychol-Uk. 2004;24(Suppl 4):419–44.

Download references

Acknowledgements

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.

Author information

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

You can also search for this author in PubMed   Google Scholar

Contributions

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.

Corresponding author

Correspondence to Nursen İlçin .

Ethics declarations

Author’s information.

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.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 19 June 2018

Accepted : 22 November 2018

Published : 04 December 2018

DOI : https://doi.org/10.1186/s12909-018-1400-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Learning style
• Academic performance
• Physiotherapy

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Research Article
• Open access
• Published: 09 February 2016

Learning strategies and styles as a basis for building personal learning environments

• Blanca J. Parra 1  

International Journal of Educational Technology in Higher Education volume  13 , Article number:  4 ( 2016 ) Cite this article

30k Accesses

10 Citations

10 Altmetric

Metrics details

This paper presents the results and reflections from a study conducted on students using the e-learning mode from the Panamerican University Foundation. The aim of the study was to identify learning strategies and styles as a basis for building personal learning environments (PLEs). This study was conducted under the parameters of a mixed research approach, which uses quantitative and qualitative techniques, as well as an interpretative approach. The main learning styles found were active, visual and global. In relation to learning strategies, a tendency towards web searching as well as schemes and summary preparation was found. Although these are the prevailing trends, the study allowed us to recognize that each person learns differently; their style and learning strategies are influenced by the environment and the resources at their disposal. This enables educational institutions to identify and make a available the techno-pedagogical tools and strategies required to strengthen and build PLEs that are more assertive and better adapted to the needs and interests of students.

Introduction

Education, over time and through the challenges of society, has undergone several transformations in educational communities, giving rise to the need to propose new strategies and resources that promote, encourage and strengthen learning, making it a meaningful and enriching experience for the various stakeholders in the process (Carrillo et al. 2012 ). When asked about new educational proposals, we are faced with a number of possibilities, ranging from traditional teacher-centered approaches to student-centered approaches; in the case of the latter, the student plays a fully participatory role and control stance, not only in the process but also in the selection of content and activities, as suggested by Coomey and Stephenson (cited by Casamayor et al., 2008 ) on the grid of e-learning educational models.

Segmentation and classification of data from the interview

Among the different concepts that have gained strength in recent years is the personal learning environment (PLE), which refers to innovative spaces that encourage the generation of knowledge through the integration of different elements, both pedagogical and technological, and allow students to take control of their learning process so that they can set their own goals, manage their work and communicate with others.

Cabero et al. ( 2011 ) suggest, with regard to the origins of PLE, that there are two approaches, a pedagogical one and a technological one. The pedagogical one is understood as a change in educational methodology that promotes self-learning through the use of resources available on the network. It is a system that focuses on the student and allows him/her to take control of his/her learning process to set his/her own goals, manage his/her activity process and communicate with others.

The technological approach refers to the PLE as a software application comprising a repository of content and different management and communication tools.

Meanwhile, Adell and Castañeda ( 2010 ) do not directly relate PLEs to technological resources, and indicate that all types of variables are involved in them (home, school and friends, among others), because the processes of learning achieved in groups and in interaction with others. Ron Lubenskyv says that PLEs have a facility for individuals to access, add, configure and manipulate digital artifacts or tools for continuous learning experiences (cited by Santamaría 2010 ).

However, PLE implementation is not always achieved successfully by culture and traditional methods of teaching and learning in the formative stage: institutions provide resources and implement, in some cases, innovative teaching strategies for learning, but to what extent do they respond to the needs and learning styles of the students?

This study has been designed to answer this question; it takes into account the different learning styles and strategies for effective PLE construction.

Problem statement

A constant concern of tutors, consultants and, in general, educational institutions offering education in e-learning mode is related to how students acquire, analyze and share knowledge. Many of the resources provided in virtual learning platforms are not adjusted to the needs and learning styles of each person, and the potential of resources is not taken advantage of to build PLEs.

On the other hand, students are unaware of their learning styles and the effectiveness of the strategies used in the education process.

Hence the need arises to implement strategies and resources aimed at strengthening the teaching-learning process, enabling students to enhance their skills and abilities by identifying their learning strategies and styles. This is the key focus of this research, based on the following question:

How can students’ learning strategies and styles, in e-learning mode, be identified so that they can contribute to the assertive construction of their PLEs?

Justification

One of the challenges of education in Colombia, listed in the Ten-Year Education Plan 2006–2016, is to ensure access to, and use and critical appropriation of information and communication technologies (ICTs) as tools for learning, creativity, and scientific, technological and cultural progress, such that it allows for human development and active participation in the knowledge society. There is also a need to promote curricular renovation of school levels and the basic functions of education and research and innovation, and to establish content and assessment practices that encourage learning and the social construction of knowledge according to the stages of development, expectations and individual and collective needs of students within their context and the world today (Ministerio de Educación Nacional de Colombia 2006 ).

At this stage and in order to contribute to the education plan, it is important for educational institutions to have the resources and didactic tools required to enable students to identify and enhance their learning styles and strategies, providing educational materials that respond to their needs and encourage their active participation. In this way, we can contribute to a genuine process of personalized and student-centered learning, under the premise that every individual learns and constructs knowledge differently based on their cognitive abilities, interests and preconceptions, hence the importance of promoting active, dynamic and collaborative learning, sharing experiences and generating new knowledge, supported by the use of ICTs. This will be possible if educational institutions have techno-pedagogical resources and strategies adapted to the learning styles of each student, and through the identification of these, more assertive PLEs can be built that are tailored to their needs and interests.

The outcomes of this research are instruments that can be applied to students using the e-learning mode in order to perform continuous monitoring to identify students’ learning styles and develop strategies ranging from the very process of mentoring to the development and availability of resources on the platform.

The aim of this study is to identify the learning strategies and styles of students using the e-learning mode as a basis for building PLEs. To that end, the following objectives have been set:

To recognize those elements that affects the construction of a PLE.

To conduct an analysis of the studies and techniques in order to identify the learning strategies and styles of students in different learning contexts.

To determine techniques and adapt instruments in order to identify the learning strategies and styles of students using the e-learning mode.

This study was conducted under the parameters of a mixed research approach, which uses quantitative and qualitative techniques, as well as an interpretive approach. Hernández et al. ( 2010 ) states that joint investigations refer to a process of collecting, analyzing and linking quantitative and qualitative data in a single study or a series of investigations to answer a research question. This type of research allows the object of study to be analyzed in its natural context, from the point of view of the participants as they perceive it. On the other hand, the use of the interpretive approach involves the description and analysis of learning styles and strategies based on the attitudes, behavior, cognitive features, and the emotional, physiological and procedural characteristics of students using the e-learning mode (Rodríguez & Valldeoriola 2009 ).

The study was conducted with undergraduate students from the Panamerican University Foundation (Unipanamericana), who were taking their course of studies in e-learning mode. In the second half of 2013, during which the research instruments were applied, there were 285 students enrolled on the various programs offered by Unipanamericana in e-learning mode.

A non-probabilistic sample was used for this study. Questionnaires were sent to all students using the e-learning mode and 54 participated in the study, corresponding to 19 % of total students enrolled. Tables  1 and 2 below detail the universe and the corresponding sample:

Research techniques

A combination of techniques allowed us to collect the necessary data to answer the research question, such as the survey, in-depth interviews and a literature review.

The survey technique was conducted via online resources, where an electronic questionnaire was used to collect structured data through closed dichotomous questions, multiple choice questions and others with alternative ordinate answers of the Likert type. The latter were used to identify the students’ learning strategies.

The survey aimed to identify the learning strategies and styles of students using the e-learning mode at Unipanamericana.

The interviews allowed the respondents’ ideas, beliefs and assumptions (Meneses & Rodríguez 2011 ) of learning strategies used and their impact on the learning process to be approached and understood.

The purpose of the interview was to understand the students’ conceptions of learning strategies and to identify the strategies they use and their impact on the learning process.

The interview was semi-structured since it was conducted from a script that allowed the interviewer to prepare information and familiarize him/herself with the topic being investigated. The central questions were open, which encouraged the interviewee to express flexible and comprehensive answers.

The literature review involved finding research and articles related to the learning strategies and styles of students in different learning contexts. The search was performed in specialized databases using search criteria to filter the most relevant and recent research publications.

Types of research tool

As an instrument of the survey technique, a questionnaire was used. This was constructed on the basis of a literature review of studies and techniques that helped to identify the learning strategies and styles of students in different learning contexts. The CHAEA tests were analyzed; CHAEA tests are the Spanish version of the LSQ tool proposed by Honey and Mumford (1988, cited by Alonso, & Gallego, 2006 )) and the Index of Learning Styles Questionnaire by Felder and Soloman ( 2008 ). Likewise, the CEVEAPEU questionnaire was analyzed, which is used to assess the learning strategies of university students (Gargallo et al. 2009 ). A process of selection, classification and adaptation of the questions was conducted and new questions were asked; all of these focused on the aim of this study.

For the interview technique, a script was made to allow the researchers to direct their questions according to the study aim and variables. In addition to the script there was a protocol giving the interviewer general guidelines to consider before, during and after the interview.

The script consisted of 18 questions, of which 6 were closed and corresponded to the respondents’ general information, and a section intended for respondents to give their consent to participating in the process of data gathering and dissemination within the framework of this study. There were also 12 open questions focusing on learning strategies. Thus the relationship of the study objectives to the research question was evident. Finally, a section was assigned to the interviewer to assess the interview. Some of the issues presented in the script are based on scripts validated and implemented in other research: the development of an oral source (Pantaleón & Rey 2006 ) and the design of a system for the management and control of the production of content and learning objects, for e-learning at Unipanamericana (Parra, 2010 ).

On the other hand, the data obtained from the literature review were listed in a matrix outlining the most important aspects of selected publications (general topic or title of the project consulted, authors, year, country, educational institution and funding body, specialist database, URL data, document type, search criteria used, keywords, synthesis and contribution), thus allowing their objects of study to be contextualized, their status to be identified, their results compared and the respective document analysis to be performed.

Data gathering was conducted electronically as follows:

The questionnaire was made available on Google Drive and sent to students via the institution’s e-mail system. It was addressed to all students and was notified through different electronic media.

For the interviews, three students were selected from the active programs in e-learning mode at Unipanamericana, who voluntarily chose to participate in it. The interviews were conducted individually via Skype, following protocol and script, designed for the application of the instrument, where the interviewer created a bond of trust with the interviewees and thus achieved an in-depth interview.

Learning styles of students using the e-learning mode at Unipanamericana

Table  3 shows the predominant learning styles of the 54 students surveyed, defined from the model of learning styles by Felder and Silverman:

Following Felder and Silverman’s bipolar category, in Category 1, the active learning style predominated (89 % of the students surveyed), according to the Index of Learning Styles Questionnaire (Felder & Soloman, 2008 .); the students in whom this style predominates tend to retain and understand information dynamically through dialogues or by explaining to others, and they are generally more likely to work in groups. For their part, reflective students tend to think about and process information in silence before giving their point of view and generally prefer individual work. Of the respondents, 9 % can be found in this category, and 2 % in both learning styles.

In category 2, the intuitive style predominated (44 % of the students surveyed), while only 15 % of the respondents can be found in the sensitive style. However, the prevalence of both learning styles is evident (41 % of the students surveyed). Sensitive students tend towards fact-based learning through problem solving and memorization of situations via laboratories and workshops, whereas intuitive students are often interested in discovery, exploration and connections, are innovative and often have a flair for abstraction and mathematical operations.

In category 3, the visual learning style predominated (83 % of the students surveyed), that is, they are students who better remember what they see (pictures, graphs, charts, timelines, videos and flow charts, among others), while only 7 % of the respondents can be found in the verbal style. Verbal students generally tend to learn best through lectures, readings, discussions and other spoken or written expressions. However, the prevalence of both learning styles is 9 %. This implies that, in 92 % of the sample, the visual style predominates, something that is favored in e-learning since it has several educational resources and graphic materials.

Finally, in category 4, the predominance of the global style is evident (87 % of the students surveyed); they are students who tend to learn in blocks without connections, are often able to solve complex problems quickly, but may struggle to explain how they do so. Of the respondents, 13 % can be found in the sequential style. The latter style is characterized by linear learning, following logical steps in search of solutions to problems, and through connections. Among the students surveyed, the global and sequential styles were not found to exist equitably.

In relation to the gender variable, and according to the results presented in Table  4 , the predominant learning style among women is global with 86 %, followed by the visual and active with 79 % each. Meanwhile, among men, the predominant learning style is active with 100 %, followed by visual and global with 88 % each. This indicates that there is a higher prevalence for the same learning styles in both genders.

Learning strategies of students using the e-learning mode

The interview data were categorized retaining the classification of learning strategies (Gargallo et al. 2009 ) and, from these, the following scheme was generated: (Fig. 1 ).

Respondents expressed their interest in individual work; it allows them to optimize time because of the technical and timing difficulties involved in synchronous meetings. However, in group work they are characterized as leaders and active. Among the strategies for organizing information, the most commonly used are the development of conceptual maps, summaries, keywords and data banks. A technique used to optimize learning is the association of terms. In addition, those interviewed agreed that they are methodical and linearly follow instructions when undertaking an activity.

When they have doubts about a topic, the respondents stated that they initially turn to Internet sources and only turn to teachers when they require clarification of the instructions to undertake academic activities. As for the work environment, there is a preference for spaces of silence and tranquility at night-time, something that coincides with the data collected in the survey.

Undertaking activities is planned according to deadlines for delivery and the level of difficulty of the issues, giving priority to subjects of greater complexity. After receiving feedback from the teacher, the respondents said that they did not always explore the topic further unless, that is, the feedback was not clear or sufficiently detailed.

Finally, the respondents said that the role of the teacher was very important in terms of facilitating their education process.

This study identified the learning strategies and styles of students using the e-learning mode at Unipanamericana, which showed that the active, visual and global styles predominant.

One thing to consider when learning environments and educational resources involved in PLEs are provided – which is significant in the sample – is that students prefer an environment isolated from noise and distraction factors, so as to enable better concentration and enhanced learning. For this reason, it is important to consider these conditions at the time of designing and publishing educational resources on virtual platforms, seeking balance between the different resources and ensuring that they are not distracting from the true purpose of learning that the students hope to achieve.

As for learning strategies, the trend among students is to make enquiries and address their concerns through various online resources. Faced with this situation, they have various options: to improve the quality and ultimately the accuracy of the information published on the network, though this does not depend solely on the teachers and students at Unipanamericana: while it is a viable alternative, it is insufficient. In short, we still need to generate the culture for students to seek out and identify reliable sources like books, specialist databases, scientific articles published in indexed journals, both nationally and internationally, and, finally to promote respect for copyright and the use of standards such as those of the American Psychological Association (APA).

Among the learning strategies, worthy of note is the fact that the students have a structure and are organized to carry out their learning process, plan their activities and to devote extra time to study. As for self-regulation, when undertaking their activities, the students realize whether or not they have been done properly, that is, they independently reflect on their own learning. However, it is necessary to continue strengthening the self-assessment strategies currently implemented at the university.

This indicates that is necessary to recognize that every individual learns and constructs knowledge differently based on their cognitive abilities, interests and preconceptions. This implies that knowledge is unique to the individual and depends on the pace of learning and the meaning given to it. Hence the importance of shifting towards active, dynamic and collaborative learning, sharing experiences and generating new knowledge, supported by the use of ICTs. This is possible if educational institutions have techno-pedagogical resources and strategies that are tailored to the learning styles of each student, and identifying them will allow them to build more assertive learning environments that are better tailored to their needs and interests. It is also important to create action plans that allow educational resources, platforms and mentoring processes to be adapted to the learning strategies and styles of students using the e-learning mode. Of particular importance within these plans is the implementation of instruments at different stages of the students’ formative processes, report results available online to students and tutors, specialized study skills clubs from the predominant learning styles, time management resources and strategies, and the adaptation of resources to different formats for different devices, among others.

Finally, this study allowed a literature review to be conducted, which helped to determine the current status of the issue within the national and international context, and instruments to be made available to students using the e-learning mode at Unipanamericana to identify their learning styles and strategies, which are the cornerstones for building their PLEs.

Study limitations and prospects

During the course of the study, the greatest difficulty was encountered in the questionnaire implementation stage due to the timing of the academic recess for students in Colombia, which coincided with the data collection date. In the questionnaire implementation stage of future projects, it will be important to contact participants via email, social networks and institutional platforms to provide them with a preliminary summary of the study in order to encourage them to become part of the sample.

Based on the results of this study, a new project has been started. The new project seeks to design software to allow students to undertake activities, each designed according to the questions of the instruments used during the study. By using the software, the students will be able to undertake these activities and, on completion, the software will tell each student what his/her dominant learning style is and suggest a series of learning strategies.

Likewise, the need arises to perform new studies to delve into the role of the tutor in the students’ PLEs and learning styles, and into how the tutor will be able to guide the students to ensure that they are better used.

Adell J, Castañeda L (2010). Los Entornos Personales de Aprendizaje (PLEs): una nueva manera de entender el aprendizaje [Personal Enviroments of Learning, a new way to understand the learning process]. Spain. 7. Retrieved from http://digitum.um.es/xmlui/bitstream/10201/17247/1/Adell&Casta%C3%B1eda_2010.pdf

Alonso C, Gallego D (2006). Estilos de aprendizaje. Cuestionario Honey-Alonso de Estilos de Aprendizaje. [Learning styles. Honey-Alonso Questionnaire Learning Styles]. Retrieved from http://www.estilosdeaprendizaje.es/

Cabero J, Marín V, Infante A (2011). Creación de un entorno personal para el aprendizaje: Desarrollo de una experiencia [Creating a personal environment for learning: Development of an experience.] Electronic journal of Educational Technology. Number 38. 4. Retrieved from http://edutec.rediris.es/Revelec2/Revelec38/pdf/Edutec-e_38_Cabero_Marin_Infante.pdf

Carrillo V, Hernández A, Parra B, Pérez Y, Portilla D (2012). Elementos tecno-pedagógicos para la construcción efectiva de entornos personalizados de aprendizaje. Fase I. Grupo de Investigación en Educación Digital y Virtual. [Techno-pedagogical construction for effective personalized learning environments. Elements Phase I. Research Group Digital and Virtual Education]. (Undergraduate thesis). Panamericana University Foundation.

Casamayor G, Alós M, Chiné M, Dalmau O, Herrero O, Mas G, Pérez F, Riera C, Rubio A (2008). La formación On-line. Una mirada integral sobre el e-learning, b-learnin… [The On-line training. A comprehensive look at the e-learning, b –learning…]. 82. Retrieved from http://books.google.com.co/books?ei=3Fh0T4CEEIHe0QGtysj_Ag&hl=es&id=v1uOpG0a6T4C&dq=Coomey+y+Stephenson+cuadricula+modelos&ots=Gshldelvn0&q=cuadrante#v=snippet&q=cuadrante&f=false .

Felder R, Soloman B (2008). Learning styles and strategies. North Carolina State University. Retrieved from http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSdir/styles.htm

Gargallo B, Suárez J, Pérez C (2009). El cuestionario CEVEAPEU. Un instrumento para la evaluación de las estrategias de aprendizaje de los estudiantes universitarios [An instrument for the assessment of the learning strategies of university students]. Retrieved from http://www.uv.es/RELIEVE/v15n2/RELIEVEv15n2_5.htm

Hernández S, Fernández C, Baptista M (2010) Metodología de la investigación [Research methodology]. Editorial McGraw Hill, Mexico, p 370

Google Scholar  

Meneses J, Rodríguez D (2011). El cuestionario y la entrevista [The questionnaire and interview]. Universitat Oberta de Catalunya. 36, 38

Ministerio de Educación Nacional de Colombia. MEN. Plan Decenal de Educación 2006–2016 [Ten- Year Education Plan 2006–2016]. 17, 21. Retrieved from http://www.plandecenal.edu.co/html/1726/articles-166057_cartilla.pdf .

Pantaleón C, Rey T (2006). Elaboración de una fuente oral. USC [Development of an oral source] Retrieved from http://webspersoais.usc.es/export/sites/default/persoais/eduardo.rey/descargas/Guion_para_la_preparacion_de_la_entrevista.pdf

Parra V (2010). Diseño del sistema para la gestión y control de la producción de contenidos y objetos de aprendizaje, para la formación virtual en la Fundación Universitaria Panamericana [Design of the system for management and control of the production of content and learning objects, for virtual training]. (Undergraduate thesis). Panamericana University Foundation, Colombia

Rodríguez G, Valldeoriola R (2009). Metodología de la investigación. (Reseach Methodology) Universitat Oberta de Catalunya. 53. Retrieved from http://zanadoria.com/syllabi/m1019/mat_cast-nodef/PID_00148556-1.pdf

Santamaría F (2010). Entornos personales de aprendizaje: una propuesta tecnológico-educativa para la integración en el aula. Retos Educativos en la Sociedad del Conocimiento. [Personal learning environments: a technological - educational proposal for integration in the classroom. Educational Challenges in the Knowledge Society]. Barcelona. 2. Retrieved from http://fernandosantamaria.com/blog/docs/retos_educativos_sociedad_conocimiento_santamaria.pdf

Download references

Author information

Authors and affiliations.

Panamerican University Foundation, Fundación Universitaria Panamericana, 132 50 143 F Suba, 111311, Bogotá, Colombia

Blanca J. Parra

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Blanca J. Parra .

Additional information

About the author.

Director, Research Group in Engineering GIIS, Panamerican University Foundation

[email protected]

ORCID ID: 0000-0001-8481-0382

Master’s degree in Education and ICT (e-learning). Communication systems specialist and systems engineer. Teaching researcher with experience in e-learning project management, software design and development, web applications, virtual platforms, virtual learning objects, content design, e-learning design, database administration and migration processes, and Blackboard and Moodle LMS platform management. Experience in university teaching, virtual mentoring and curriculum design.

Fundación Universitaria Panamericana

132 50 143 F Suba

111311 BOGOTÁ

Competing interests

The author declares that he has no competing interests.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Parra, B.J. Learning strategies and styles as a basis for building personal learning environments. Int J Educ Technol High Educ 13 , 4 (2016). https://doi.org/10.1186/s41239-016-0008-z

Download citation

Received : 06 May 2015

Accepted : 09 October 2015

Published : 09 February 2016

DOI : https://doi.org/10.1186/s41239-016-0008-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Environments

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• PMC10374043

Influence of learning styles on student performance in self-instructional courses

Alana oliveira.

1 Computer Engineering, Federal University of Maranhao, Sao Luis, MA, Brazil

Vitoria Spinola

2 USP School of Dentistry, University of Sao Paulo, Sao Paulo, SP, Brazil

Deise Garrido

Mario meireles teixeira.

3 Department of Informatics, Federal University of Maranhao, Sao Luis, MA, Brazil

Carlos Salles

Ana estela haddad, associated data.

All relevant data are within the paper and its Supporting information files.

This study aimed to analyze the learning style of dentistry students in self-instructional courses to assist in pedagogical planning and to choose the most appropriate educational resources for the students’ learning profile. A sample of 122 students who responded to the Learning Styles Questionnaire was analyzed. For statistical purposes, correlation analysis, chi-square test, odds ratio, and Student’s t-test were performed. In the analyzed sample, there was a higher prevalence of students in the theoretical and reflector styles, and a lower prevalence of students in the activist and pragmatic styles. An analysis of educational resources demonstrated the predominance of theoretical and reflective content. The data show a statistically significant reduction of about 74% in the chances of passing for the activist-pragmatists group compared to other students ( χ 2 (1, N = 122) = 5.795, p < 0.05, odds ratio = 0.26). On the other hand, reflector students who exhibited a lower preference for the activist style had a higher chance of course completion, with a 3.33-fold increase in the likelihood of passing the course ( χ 2 (1, N = 122) = 5.637, p < 0.05, odds ratio = 3.33). These findings highlight the importance of considering students’ learning styles in educational planning and resource selection to optimize student performance. Further research is warranted to explore the implications of these findings and to investigate additional factors that may influence student success in self-instructional courses.

Introduction

Health education is a multifaceted field in which different conceptions converge, reflecting different understandings of the world and requiring a vision of different sciences. From this perspective, it is important to rethink the concepts on which higher education is based, whose central point is no longer teaching but the teaching-learning process [ 1 , 2 ].

Autonomous learning gives more importance to the mastery of teaching tools than mere accumulation of content, starting with educational resources capable of generating significant knowledge and facilitating the insertion of students in the learning process [ 3 ]. Learning analytics provide tools for understanding and optimizing learning and the environment in which it happens [ 4 , 5 ].

Among the characteristics of the teaching process, learning styles stand out in this article, where mechanisms capable of classifying students according to the most favorable way for their learning are sought; that is, identifying the most appropriate way for students to learn. The challenges range from understanding how each student learns best to the volume and nature of the content to be offered, its frequency, and the display of what is relevant–that is, offering personalized teaching at massive levels. Therefore, collecting data on student profiles, particularly their learning styles, is an important step in any data-driven decision-making process to assist in planning and executing massive open online courses (MOOCs).

The relationship between learning styles, academic performance, and adequacy of instructional materials has received significant attention in educational research. Previous studies suggest that students have different preferences and ways of absorbing and processing information, which can impact their academic performance. By understanding and considering students’ learning styles, it is possible to adapt instructional materials and teaching strategies to better meet individual needs. This creates a more personalized and engaging learning environment, providing students with greater opportunities for academic success. Therefore, investigating the relationship between learning styles, academic performance, and adequacy of instructional materials has significant implications for the development of more effective educational approaches and for enhancing the quality of students’ learning experience.

It is clear that teachers are not advised to diagnose every student. On the other hand, attention to learning styles brings about a variety of teaching practices. Information regarding students, their backgrounds, experiences, and learning styles can be effectively used in the classroom [ 6 ], whether face-to-face or distant. However, the choice of learning style is not unanimous, as pointed out by [ 7 , 8 ].

In a study, Lowery [ 9 ] points out resources and specificities to be used in teaching and assessment in order to contemplate all learning styles in the classroom. Different media types are perceived differently by students according to their learning styles and Lowery state that the right selection of educational media can improve students’ learning achievement.

In a related work, we propose adaptations in the interface of a virtual environment according to the students’ learning style, thus seeking to provide a differentiated and potentially more motivating user experience [ 10 ] and to improve students’ performance as indicated by [ 11 ].

In this work, it is assumed that a more accurate mapping between students’ learning preferences and the educational content provided has the potential to increase their performance in carrying out courses. It should be noted that the purpose of learning styles is not to match the instruction provided to each student’s learning style preferences, but rather to teach in a way that balances students’ preferences for different learning styles [ 12 ]. This approach can be applied to different educational contexts.

Particularly, self-instructional courses have been used since 2010 to train professionals in the Unified Health System (SUS), the Brazilian Public Health System, through the Open University of SUS (UNASUS). Since 2016, UNASUS/UFMA, in partnership with NuTes FOUSP, the Teledentistry and Telehealth Center of the School of Dentistry at the University of São Paulo (FOUSP), have been developing MOOCs for the continuing education of SUS dentists.

Our research aimed to analyze the learning style of students of self-instructional courses in the area of dentistry to assist in the pedagogical planning of these courses and to select the most appropriate educational resources directed toward the students’ learning profile. To this end, we conducted a narrative review of the literature, focusing on key articles that discuss the application of learning styles and their impact on performance in online courses. This review served to provide a theoretical foundation for our study and to identify relevant trends and gaps in the current body of knowledge.

This study aimed to (i) Identify the learning profile of the course’s target audience; (ii) Identify the profile of the course’s educational resources; (iii) Perform a compatibility analysis of the educational resources present in the course with the students’ profile.

The insights gained from this work have the potential to contribute to the continuous development of the pedagogical team at UNASUS/UFMA. This research offers valuable perspectives, encouraging a thoughtful evaluation of existing pedagogical approaches and suggesting avenues for further growth.

Theoretical background

Learning styles.

Each human being has their own way of learning, those who learn by doing and those who observe, those who are multitasking, and those who need to focus on one task at a time. Several studies intend to classify students according to the way most favorable to their learning, that is, according to their learning style, seeking to identify the best way for a student to assimilate the knowledge that is transmitted.

Kolb’s learning styles and experiential learning cycle

For Kolb [ 13 , 14 ], learning is the process by which knowledge is created through the transformation of experience. And knowledge is not something that can simply be transmitted or acquired, it is the result of a process and can be created and recreated continuously. Kolb also believes that people can be classified according to their way of learning into learning styles (or preferences) as diverging, converging, assimilating, and accommodating. This classification can be used to provide teachers with information so that they consider the best way their students can learn and thus be able to achieve better success in their teaching.

Honey and Mumford learning styles

Honey and Mumford have identified, based on Kolb’s work, four learning styles or preferences: Activist, Theorist, Pragmatist, and Reflector. The authors recommend that, to maximize personal learning, each student should know their own learning style and then look for opportunities to learn using that style.

The four learning styles characterized by Honey and Mumford are activist, reflective, theorist and pragmatic [ 15 ]. Activists do not show prejudice when entering new experiences nor do they show the ability to work with people. Reflectors show a preference for looking at experiences from different angles and need time to reflect on their circumstances before establishing a course of action. According to theorists, learning depends on their understanding of concepts, models, and theories, presenting their ability to synthesize and analyze information. Finally, pragmatists must understand the practical benefits of applying theory in the real world, and feel comfortable following a predefined course of action.

Related work

The relationship between students’ learning styles and academic performance has been an ongoing topic of interest across various fields of study. Numerous studies have explored this relationship across a variety of contexts and through diverse educational modalities, from traditional in-person education to emerging modalities enabled by advancements in technology. These include distance education, online learning, blended learning, and flipped classrooms, among others.

Regardless of the context or modality, a common theme emerges in the literature: the importance of tailoring instruction to the student’s learning style. A body of research suggests that by aligning the teaching strategy with the student’s preferred learning style, one can enhance student engagement, understanding of material, and ultimately, academic performance [ 16 , 17 ].

However, it’s important to note that the relationship between learning styles and academic performance is not simple or direct. Some studies suggest that different learning styles may be more or less effective in different contexts or subjects. An illustrative example of this complex relationship between learning styles and academic performance is found in the study conducted by [ 16 ]. They applied the Kolb’s learning style model, to investigate this relationship within the context of an accounting course for non-accounting students. The findings revealed that students favoring the Pragmatist and Theorist learning styles outperformed their peers in the accounting course, while those with a preference for the Activist learning style lagged behind.

In [ 18 ], Shamsuddin and Kaur conducted a study exploring the impact of blended learning on student outcomes in relation to their learning styles. They employed Kolb’s Learning Style Inventory to determine the learning styles of 119 students pursuing a degree in Information Technology. The study primarily found a majority of students falling into the Convergent learning style, followed by Divergent, Accommodator, and Assimilator. However, the research did not uncover a meaningful correlation between students’ learning styles and their perceptions of blended learning.

EL-BISHOUTY et al. investigated the application of the Felder and Silverman learning style model in designing online courses [ 17 ]. They developed an interactive course analyzer tool that helps teachers assess the level of support provided by their courses for different learning styles. The findings indicated that designing courses with specific learning styles in mind can enhance learning outcomes for students with those particular styles.

Materials and methods

Study design and instruments.

This was a descriptive, cross-sectional, quantitative study. In this article, a methodology centered on learning styles is presented according to the Honey-Mumford taxonomy [ 19 , 20 ].

It is important to mention that reliance on participants’ self-reports may introduce inherent subjectivity and potential biases into the data collection process. We acknowledge the potential for discrepancies between self-perceptions and actual learning characteristics, as well as the influence of motivational factors on students’ perceptions and study decisions. This limitation is not unique to our study though but is a common challenge in research that relies on questionnaires or self-assessment tools. However, Flemimg and Baume [ 21 ] believe that the use of questionnaires to define learning styles can be useful, but its real value lies in the self-knowledge it can generate to each person when analyzing the score obtained. This classification can serve as input for teachers so that they can choose the most appropriate activities for each modality.

The analysis was based on secondary data collected directly from LMS Moodle and primary data obtained from the Learning Styles Questionnaire (LSQ) [ 22 , 23 ]. In summary, the LSQ consists of 80 yes/no answer questions, allowing respondents to indicate their agreement with each statement that aligns with their learning preferences. The questionnaire covers various aspects related to learning approaches and behaviors associated with the four learning styles: Activist, Theorist, Pragmatist, and Reflector.

Upon completion of the LSQ, a score is obtained for each learning style. This score reflects the student’s intensity or preference level for that particular style. The scoring ranges from very low preference to very strong preference. By analyzing the responses, we were able to determine the dominant learning style preferences of the participants in our study.

It is important to note that the LSQ has been widely used and recognized as a valid and reliable instrument for assessing learning style preferences. The questionnaire items are based on theoretical constructs and empirical evidence related to the different learning styles proposed by Honey and Mumford. This information serves as input for checking the compatibility analysis of the educational resources present in the course, in relation to the students’ profile.

Finally, the chi-square test is carried out in order to investigate whether there is an association between learning styles and passing this course. By performing the student’s t test, it is verified whether there is a statistically significant difference between the performance (final grade) of students with a profile compatible with the course and those with an incompatible profile.

Sample and data collection

The data collected were from students of the course “Dental Care for Patients with CNCDs in Primary Care: Diabetes, Hypertension and Chronic Kidney Disease”, which addresses topics related to healthcare networks for people with chronic diseases, especially chronic kidney disease (CKD), systemic arterial hypertension (SAH), and diabetes mellitus (DM).

The course was offered in three editions, with the first and second editions having 7,618 and 5,471 students, respectively. The third edition had a total of 19,958 enrolled students. For the purposes of this study, the participants invited to respond to the questionnaire were those who had already completed the course, totaling 8,763 students, including both those who passed and those who failed. Invitations were sent via email along with informational materials explaining each learning style, a list of activities more favorable or unfavorable for each style, and recommendations for suitable materials based on learning style. The questionnaire was made available for a period of one month, during which 225 students had access to the form. However, only 122 students completed the questionnaire in its entirety.

Students were categorized as theorists, reflectors, pragmatists, and activists, after having completed a questionnaire made available as a web tool, applied to groups of students of a self-instructional course.

According to the number of responses in agreement, the preference for each learning style was determined on five levels: very low, low, moderate, high, or very high.

Statistical analysis

Data were analyzed using version 28 of the Statistical Package for the Social Sciences (SPSS). Correlation analysis, chi-square, odds ratio and student’s t test were performed and the significance level was set at 5% (p < 0.05).

A statistical correlation analysis was conducted to identify profiles that overlapped. A chi-square test was conducted to investigate whether there was an association between learning styles and passing the course. The Student’s t-test was used to verify whether there was a statistically significant difference between the performance (final grade) of students with a profile compatible with the course and those with an incompatible profile. While the data did not follow a normal distribution, we chose to use the t-test with bootstrap resampling (1000 samples; 95% BCa confidence interval) as a robust alternative to test for differences between groups. Bootstrap resampling is a well-established method for generating reliable estimates and confidence intervals, even when the underlying data distribution is unknown or non-normal [ 24 – 26 ]. As such, we believe that the results obtained through this approach are valid and can provide useful insights into the relationship between the variables under study.

Ethical statement

This project was approved (approval no. 3.809.169) by the Research Ethics Committee of the Universidade Federal do Maranhao (UFMA).

All participants of this research have expressed their consent electronically by agreeing to a consent form available on the Moodle LMS platform. All participants’ data were fully anonymized prior to the analysis.

Matching types of resources to learning styles

The course addresses topics related to the Health Care Networks for People with Chronic Diseases, in particular Chronic Kidney Disease (CKD), Systemic Arterial Hypertension (SAH) and Diabetes Mellitus (DM). Having as an educational objective the understanding of the epidemiology of these chronic diseases as well as the diagnosis, treatment and dental management, in order to contribute to a better service, respecting the specificities and needs of this line of care.

This course is comprised of 14 resources, including welcome video, course expectation collection form, pre-test and post-test status questionnaires, pre- and post-test benchmarking and evaluation. Those resources are common to all courses and not specific educational content unique to the dentistry course under investigation.

The number of resources by learning style for each topic in the analyzed course is summarized ( Table 1 ). Table summarization was based on the interface adaptation model presented in [ 10 ], which lists the most appropriate types of educational resources for each learning style. Activist students benefit from resources, such as competition, challenges, movies, forums, infants, games, and mind maps. Reflectors preferred handouts, digital books, articles, documentaries, movies, exercise lists, podcasts, riddles, video classes, and webcasts. Theorists prefer handouts, digital books, articles, documentaries, video classes, video demos, and webcasts. Finally, pragmatists were most comfortable with documentaries, movies, infographics, games, workbooks, and tutorials. It is clearly observed that the educational resources produced for the analyzed course are directed only to the theoretical and reflector profiles.

Sample characterization

The graph quantifies the students according to their level of preference for each learning style ( Fig 1 ). Reflector style was predominant, with approximately 81% (99/122) showing a high or very high preference. The smallest trend was for the activist style, with only 25.4% (31/122) of participants at high and very high preference levels.

Statistical correlation between learning styles

The reflector learning style is crossed with other styles ( Fig 2 ). In it, it can be seen that the first heat map ( Fig 2a ) highlights a moderate to very low activist preference combined with a strong to very strong preference for reflectors in 61.47% (75/122) of the participants. Additionally, when cross-referenced with theorist learning style ( Fig 2b ), the heat map shows a concentration of strong and very strong reflector preference combined with an increasing moderate to very strong theorist preference in 74.59% (91/122) of participants. This indicated a combined preference for these two styles among the participants. A similar behavior occurred when the cross between reflexive and pragmatist ( Fig 2c ) was verified, with a strong and very strong reflexive preference combined with a moderate to very strong pragmatist preference in 64.75% (79/122) of the participants.

A moderate to very low activist preference is noticed, combined with a moderate to very high preference of theorists in 68.03% (83/122) of the participants ( Fig 3a ).

Finally, a centrally distributed preference between activists and pragmatists stood out in 64.75% (79/122) of the participants ( Fig 4 ).

The obtained correlation coefficients were analyzed ( Table 2 ). The variables “activist”, “pragmatist”, and “theorist” are significantly correlated (p < 0.01 and p < 0.05). The variable “reflector” was statistically correlated only with the “theorist” learning style (p < 0.01). Statistical analysis of the learning styles showed a positive correlation between reflectors and theorists, theorists, and pragmatists, and between activist and pragmatist styles. A significant negative correlation between the activist and theorist styles was also observed for the studied sample.

*p<.05;

**p<.01;

n.s. = non-significant correlation

Association between learning styles and approval

Chi-square tests of independence (2x2) were performed ( Table 3 ) to investigate whether there was an association between learning style (reflectors, theorists, activists, and pragmatists) and passing the course. Thus, significant associations were found between the activist and pragmatist styles and passing the course.

**p<0.05;

χ 2 = chi-square; dof = degrees of freedom;

For the activist style, chi-square ( χ 2 (1) = 6.737, p = 0.009; Φ = -0.235) and odds ratio analyses showed that activist students experienced a reduction of 73.17% in the chance of passing the analyzed course compared to the non-activist ones. This is the same as saying that students who are not activists are 3.73 times more likely to pass the course when compared to activist students.

As for pragmatist style, the chi-square ( χ 2 (1) = 4.176, p = 0.041; Φ =-0.185) and odds ratio analysis showed that pragmatist students had a 64.44% reduction in the chance of passing the analyzed course when compared to non-pragmatist students. This is the same as saying that students who are not pragmatist are 2.81 times more likely to pass the course when compared to pragmatist students.

Another chi-square test of independence (2x2) was conducted, considering the combination of activist and pragmatist styles, which are considered incompatible with the course, to investigate whether there was an association between the combination of these two styles and student approval. A significant association was found between these combined styles (activist-pragmatist) and approval ( χ 2 (1) = 5.795, p < 0.05; Φ = 0.218). Odd-ratio analysis showed that activist-pragmatist students had a 73.91% reduction in the chance of passing the analyzed course compared to non-activist-pragmatist students.

On the other hand, reflector students with a low preference for the activist style are 3.33 times more likely to pass the course when compared to those who are not “low activist reflectors” ( χ 2 (1)= 5.637, p < 0.05; Φ = -0.215).

This suggests that being “reflective low-active” is associated with a higher likelihood of course approval. However, it is important to note that this is an association and does not necessarily imply causation. Other factors not included in this study may also influence these results.

Impact of learning style on course performance (final grade)

Additionally, a Student’s t-test was performed for independent samples ( Table 4 ) to investigate the extent to which course performance (final grade) differed between students with a learning style consistent with the course and those who did not. The results showed that active students had statistically lower performance (M = 72.26; SD = 18.923) than non-active students (M = 80.00; SD = 14.832) (t(120) = -2.33, p < 0.05). The effect size of the difference was medium (Cohen’s d = 0.49).

Note: SD = standard deviation; dof = degrees of freedom;

Health education encompasses a broad range of factors that extend beyond the mere acquisition of knowledge passed down from previous generations. They must also be able to transpose such knowledge for use in situations of daily professional practice. In the context of self-instructional distance courses (MOOCs), this task seems even more challenging because, most of the time, these courses provide theoretical material with few activities that approach the problems of practice.

Pedagogically, some studies categorize individuals according to learning styles, grouping common characteristics that allow a better understanding of how each student learns, how they receive and interact with different content. This approach becomes even more crucial when designing instructional strategies for massive courses. In line with this, our analysis examined the distribution of course resources based on learning styles. Furthermore, the findings revealed that the materials provided in the course predominantly aligned with the reflector and theorist profiles ( Table 1 ), while the activist and pragmatic profiles were not adequately addressed. This discrepancy highlights the need for a more comprehensive and inclusive approach to cater to the diverse learning preferences of students in massive courses.

When examining the learning profile of the course’s target audience,it is noteworthy that a significant proportion of the students (approximately 81% and 64% of the participants, respectively) exhibit a preference for the reflector and theorist learning styles. Moreover, the pragmatist style also demonstrates a notable presence among the students to some extent ( Fig 1 ). These findings indicate that the majority of the researched students display moderate to very high levels of preference for these particular learning profiles. There is also shows a moderate to very low activist preference combined with a moderate to very strong theorist preference for 90% of the participants ( Fig 3 ). Therefore, our findings have demonstrated a certain degree of orthogonality between the students’ preferences regarding the theorist and activist profiles, which was evidenced visually and by statistical correlation analysis.

In general, education in Brazil aims to provide a solid foundation of theoretical knowledge, but there is also a growing recognition of the importance of more practical and contextualized approaches to promote meaningful learning and the development of relevant skills for the modern world [ 27 ], with little emphasis on practice, which leads individuals to develop such learning preferences over others. This emphasis on theory may have shaped the learning preferences of individuals, potentially contributing to the prevalence of reflector and theorist styles in our sample. However, this does not mean that they could not take advantage of didactic material that proposes more practical activities; they just were not trained to do so.

We investigated whether students’ learning styles influence their chances of passing the course, building upon the previous findings regarding the prevalence of learning styles in our sample. The results of the chi-square analysis and odds ratio analysis indicate a strong correlation between having an activist, pragmatist, or combined profile and a reduced likelihood of passing the analyzed course, with approximately a 73% decrease in the chances of passing. On the other hand, it was shown that students with a reflector and not a very active profile were more than three times more likely to pass than those with divergent profiles. We believe that this occurs precisely because of the non-conformity of the predominant learning style in these students with that identified in the educational resources. This demonstrates the importance of having varied resources to meet different student profiles and to develop their skills in different shades.

In addition, through Student’s t-test, it was found that the final grade of activist students in the course differed between students with a learning style compatible with the educational resources used and those who were not. Students with an activist profile presented a statistically lower performance than those with a non-activist profile.

In Tahir et al.’s study, the Pragmatist and Theorist styles were linked to superior performance in an accounting course. In our study, students with theoretical and reflective learning styles performed better. This discrepancy might be due to differences in the nature of the subjects studied—accounting and dentistry—suggesting that the impact of learning styles can vary depending on the discipline.

Contrasting Shamsuddin and Kaur’s (2020) findings, our study links student learning styles significantly to academic performance in self-instructional courses. While the previous study did not find a substantial correlation between learning styles and perceptions of blended learning, our findings reveal a significant association between learning styles and course outcomes.

The results in EL-BISHOUTY et al. (2019) suggest that considering students’ learning styles in course design can enhance learning outcomes for those specific styles. This finding aligns with our study, which indicates a significant association between learning styles and students’ performance in dentistry courses.

In this study, we collected data on the learning styles of students in a self-instructional course in dentistry offered by UNASUS/UFMA in partnership with NuTes FOUSP. By categorizing the educational resources of the course according to students’ learning styles, we were able to identify profiles of students with the highest probability of success in the course. Our statistical analysis revealed that students with educational resources that were compatible with their learning styles had significantly higher pass rates than those with incompatible profiles.

These findings are consistent with previous research that has shown a strong relationship between learning styles and academic success. In particular, studies have shown that students who receive instruction that is tailored to their learning style tend to perform better than those who do not. Our study contributes to this body of literature by providing evidence that this relationship holds true in the context of a self-instructional course in dentistry.

However, it is important to note that our study has some limitations. For example, we relied on self-reported measures of learning styles, which may not accurately capture the complexity of individual learning preferences. Additionally, our sample was limited to a single course at a single institution, which may limit the generalizability of our findings.

Despite these limitations, our study has important implications for the design and delivery of educational content in the context of self-instructional courses. By considering the learning styles of students and tailoring educational resources to their preferences, it may be possible to improve student engagement, satisfaction, and ultimately, academic success.

This perspective is supported by our results, which indicate that by understanding the student’s learning style and adjusting the educational content to that style, whether through automatic adaptive means or through recommendations to students and teachers, academic performance can be improved overall.

Future research could build on these findings by exploring the effectiveness of different approaches to tailoring educational content to individual learning styles, as well as the factors that may influence the relationship between learning styles and academic success in different contexts.

This study presents opportunities for improving the development of educational content for new self-instructional courses that are better tailored to the target audience. In future work, we aim to expand this study to encompass other dentistry courses and areas in order to validate the identified trends.

Further investigations can explore the relationship between learning styles and additional measures of student success, such as retention rates or academic performance in subsequent courses. This would allow for a more refined design of educational content and support services to better cater to the diverse needs of learners.

Conducting longitudinal studies can provide insights into how learning styles evolve over time and their correlation with long-term academic and professional outcomes. This approach can offer valuable information about the factors that contribute to student success and inform the development of personalized educational programs.

Utilizing advanced statistical techniques, such as machine learning algorithms, can reveal patterns and relationships in extensive datasets of student performance and learning style assessments. This approach can uncover new insights and predictive models to improve the design and delivery of educational content.

Qualitative research methods, such as interviews or focus groups, can provide in-depth insights into how students with different learning styles experience and engage with educational content. This approach can identify specific challenges and opportunities for enhancing the design and delivery of educational materials and support services.

Lastly, it is crucial to develop and test new interventions or technologies that are specifically tailored to the needs of different learning styles. Examples include interactive tutorials or simulations, personalized feedback and coaching, and adaptive learning platforms that dynamically adjust content based on individual learners’ needs and preferences.

Supporting information

Acknowledgments.

The authors would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES) for supporting research at the Doctorate Program in Computer Science (DCCMAPI) at the Federal University of Maranhao (UFMA).

Funding Statement

The author(s) received no specific funding for this work.

Data Availability

ORIGINAL RESEARCH article

Students' metacognitive knowledge of learning-strategy effectiveness and their recall of teachers' strategy instructions provisionally accepted.

• 1 School of Natural Sciences and Health, Tallinn University, Estonia
• 2 Tallinn University, Estonia

The final, formatted version of the article will be published soon.

This study aimed to investigate students' metacognitive knowledge and reported use of surface and deep learning strategies. It also explored the extent to which students recall their teachers' recommendations for learning strategies and the relationship between these recollections and students' knowledge and reported use of strategies. A scenario-based questionnaire was used to set a learning goal in the area of biology. Students' metacognitive knowledge was assessed through perceived effectiveness and reported use of learning strategies. Additionally, open-ended questions allowed students to recall and report recommendations given by their teachers. We used personcentered methods to explore whether different types of recollections were related to reported strategy use. Among students who recollected that their teachers have recommended deep learning strategies, it was typical to value deep strategies higher than surface strategies and report using deep strategies. Also, it was atypical among those students to value surface level strategies and not use deep strategies.

Keywords: metacognitive knowledge, Learning Strategies, Teacher instruction, Configural frequency analysis (CFA), Strategy effectiveness

Received: 04 Oct 2023; Accepted: 22 Apr 2024.

Copyright: © 2024 Olop, Granström and Kikas. 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) or licensor 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: Mx. Joosep Olop, School of Natural Sciences and Health, Tallinn University, Tallin, Estonia

People also looked at