Categorizing E-Learner Attributes in Personalized E-learning Environments: A Systematic Literature Review

Document Type : Review Article

Authors

1 Department of Information Technology Management, School of Management, North of Tehran, Islamic Azad University, Tehran, Iran.

2 Department of Information Technology Engineering, Tarbiat Modares University, Tehran, Iran

3 Assistant professor, Department of Industrial Engineering, Islamic Azad University, Tehran, Iran;

4 School of Management, Islamic Azad University, Tehran, Iran

Abstract

Background: The development of learner models in learning management systems is among the most significant steps in designing personalized e-learning environments. The primary purpose of this modeling is to extract user characteristics in order to personalize the learning process based on learners’ needs, learning style, personality, and individual circumstances. Methods: The present study provides a review of published literature over the past 20 years in academic databases including IEEE, Sciencedirect, Wiley, and Springer. The search was limited to the studies on the personalization of e-learning environments based on learner characteristics, specifically the ones providing a reliable method for integrating these characteristics, as appropriate input variables, in the design of personalized e-learning systems. Results: This study proposed a new method of classifying the learner characteristics as the variables for designing a personalized e-learning system. A total of 111 papers were considered for analysis. In the end, 22 influential learner characteristics were extracted and classified into six subcategories, namely cognitive, motivational, behavioral, emotional, metacognitive aspects, and combined domains. The proposed classification method was also compared with available related categorizations to demonstrate this method’s advantage in designing a personalized e-learning environment. Conclusion: The findings represent the learning criteria that can be utilized in designing adaptive learning systems. Moreover, it can also aid other researchers in this field to achieve a better perspective in learner modeling. Applying these characteristics as input design variables in personalized e-learning systems can result in a better solution for personalization.

Keywords

References

Sheeba T, Krishnan R. Automatic detection of students learning style in Learning Management System. InSmart Technologies and Innovation for a Sustainable Future 2019 (pp. 45-53). Springer, Cham. doi:10.1007/978-3-030-01659-3_7
Kinley K, Tjondronegoro D, Partridge H, Edwards S. Modeling users' web search behavior and their cognitive styles. Journal of the Association for Information Science and Technology. 2014 Jun;65(6):1107-23. doi:10.1002/asi.23053
Ghorbani F, Montazer GA. Design a Personalized System Based On Learner's Individual Attributes And Behavioral ‎Signs In E-Learning Environment Phd Thesis. Tehran, Tarbiat Modares University; 2015           
Klement M, Dostál J, Marešová H. Elements of Electronic Teaching Materials with Respect to Student's Cognitive Learning Styles. Procedia-Social and Behavioral Sciences. 2014 Feb 7;112:437-46. doi:10.1016/j.sbspro.2014.01.1186
Zamzuri NH, Shahrom M, Kasim ES, Nasir HM, Mamat MN. The role of cognitive styles in influencing the users' satisfaction on e-learning system. Procedia-Social and Behavioral Sciences. 2012 Dec 10;67:427-35. doi:10.1016/j.sbspro.2012.11.347
Holmes J, Gathercole SE, Dunning DL. Adaptive training leads to sustained enhancement of poor working memory in children. Developmental science. 2009 Jul;12(4):F9-15. doi:10.1111/j.1467-7687.2009.00848.x  PMid:19635074
Kalyuga S, Sweller J. Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development. 2005 Sep 1;53(3):83-93. doi:10.1007/BF02504800
Lestari W, Nurjanah D, Selviandro N. Adaptive Presentation based on Learning Style and Working Memory Capacity in Adaptive Learning System. InCSEDU (1) 2017 (pp. 363-370).    
Pavalache-Ilie M, Cocorada S. Interactions of students' personality in the online learning environment. Procedia-Social and Behavioral Sciences. 2014 Apr 22;128:117-22. doi:10.1016/j.sbspro.2014.03.128
Tlili A, Essalmi F, Ayed LJ, Jemni M. A smart educational game to model personality using learning analytics. In2017 IEEE 17th International conference on advanced learning technologies (ICALT) 2017 Jul 3 (pp. 131-135). IEEE. doi:10.1109/ICALT.2017.65
Kim J, Lee A, Ryu H. Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics. 2013 Sep 1;43(5):450-61. doi:10.1016/j.ergon.2013.03.001
Carro RM, Sanchez-Horreo V. The effect of personality and learning styles on individual and collaborative learning: Obtaining criteria for adaptation. In2017 IEEE Global Engineering Education Conference (EDUCON) 2017 Apr 25 (pp. 1585-1590). IEEE. doi:10.1109/EDUCON.2017.7943060
Pelánek R. Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Modeling and User-Adapted Interaction. 2017 Dec;27(3):313-50. doi:10.1007/s11257-017-9193-2
Huang EY, Lin SW, Huang TK. What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers & Education. 2012 Jan 1;58(1):338-49. doi:10.1016/j.compedu.2011.08.003
Jeremić Z, Jovanović J, Gašević D. Student modeling and assessment in intelligent tutoring of software patterns. Expert Systems with Applications. 2012 Jan 1;39(1):210-22. doi:10.1016/j.eswa.2011.07.010
Baylari A, Montazer GA. Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications. 2009 May 1;36(4):8013-21. doi:10.1016/j.eswa.2008.10.080
Alepis E, Virvou M. User modelling: An empirical study for affect perception through keyboard and speech in a bi-modal user interface. InInternational Conference on Adaptive Hypermedia and Adaptive Web-Based Systems 2006 Jun 21 (pp. 338-341). Springer, Berlin, Heidelberg. doi:10.1007/11768012_45
Kallinen K, Ravaja N. Effects of the rate of computer-mediated speech on emotion-related subjective and physiological responses. Behaviour & Information Technology. 2005 Sep 1;24(5):365-73. doi:10.1080/01449290512331335609
Plass JL, Homer BD, Pawar S, Brenner C, MacNamara AP. The effect of adaptive difficulty adjustment on the effectiveness of a game to develop executive function skills for learners of different ages. Cognitive Development. 2019 Jan 1;49:56-67. doi:10.1016/j.cogdev.2018.11.006
Shabani MB. Different Learning Style Preferences of Male and Female Iranian Non-Academic EFL Learners. English Language Teaching. 2012;5(9):127-37. doi:10.5539/elt.v5n9p127
Noguti, V., Singh, S. Waller, D. S. (2019). "Gender ‎differences in motivations to use social networking sites", ‎Gender Economics: ‎Breakthroughs in Research and ‎Practice, pp. 1565-1580.‎ doi:10.4018/978-1-5225-6912-1.ch081
Van Nuland SE, Rogers KA. Anatomy, e-learning and visuospatial ability: considerations for future learners. International ‎Technology, Education and Development Conference. 2017 (pp. 1356) . doi:10.21125/inted.2017.0459
Xiao Z, Wauck H, Peng Z, Ren H, Zhang L, Zuo S, Yao Y, Fu WT. Cubicle: An adaptive educational gaming platform for training spatial visualization skills. In23rd International Conference on Intelligent User Interfaces 2018 Mar 5 (pp. 91-101). doi:10.1145/3172944.3172954
Tsai YH, Lin CH, Hong JC, Tai KH. The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education. 2018 Jun 1;121:18-29. doi:10.1016/j.compedu.2018.02.011
Tsai MJ. The model of strategic e-learning: Understanding and evaluating student e-learning from metacognitive perspectives. Journal of Educational Technology & Society. 2009 Jan 1;12(1):34-48.          
Elbasri H, Haddi A, Allali H. Improving E-learning by Integrating a Metacognitive Agent. International Journal of Electrical and Computer Engineering. 2018 Oct 1;8(5):3359. doi:10.11591/ijece.v8i5.pp3359-3367
Biswas G, Rajendran R, Mohammed N, Goldberg BS, Sottilare RA, Brawner K, Hoffman M. Multilevel Learner Modeling in Training Environments for Complex Decision Making. IEEE Transactions on Learning Technologies. 2019 Jun 17;13(1):172-85. doi:10.1109/TLT.2019.2923352
Woolf B, Burelson W, Arroyo I. Emotional intelligence for computer tutors. InWorkshop on modeling and scaffolding affective experiences to impact learning at 13th international conference on artificial intelligence in education, Los Angeles, California 2007 Jul.       
D'Mello S, Olney A, Williams C, Hays P. Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of human-computer studies. 2012 May 1;70(5):377-98. doi:10.1016/j.ijhcs.2012.01.004
Fatahi S. An experimental study on an adaptive e-learning environment based on learner's personality and emotion. Education and Information Technologies. 2019 Jul;24(4):2225-41.doi:10.1007/s10639-019-09868-5
Kanimozhi A, Raj VC. An adaptive e-learning environment centred on learner's emotional behaviour. In2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET) 2017 Feb 16 (pp. 1-5). IEEE. doi:10.1109/ICAMMAET.2017.8186752
Chen CM, Sun YC. Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners. Computers & Education. 2012 Dec 1;59(4):1273-85. doi:10.1016/j.compedu.2012.05.006
Tseng JC, Chu HC, Hwang GJ, Tsai CC. Development of an adaptive learning system with two sources of personalization information. Computers & Education. 2008 Sep 1;51(2):776-86. doi:10.1016/j.compedu.2007.08.002
Gutierrez F, Atkinson J. Adaptive feedback selection for intelligent tutoring systems. Expert Systems with Applications. 2011 May 1;38(5):6146-52. doi:10.1016/j.eswa.2010.11.058
Huang MX, Li J, Ngai G, Leong HV, Bulling A. Moment-to-moment detection of internal thought during video viewing from eye vergence behavior. InProceedings of the 27th ACM International Conference on Multimedia 2019 Oct 15 (pp. 2254-2262). doi:10.1145/3343031.3350573
Bauer M, Bräuer C, Schuldt J, Niemann M, Krömker H. Application of wearable technology for the acquisition of learning motivation in an adaptive e-Learning platform. InInternational Conference on Applied Human Factors and Ergonomics 2018 Jul 21 (pp. 29-40). Springer, Cham. doi:10.1007/978-3-319-94619-1_4
Hubackova S. Motivation in eLearning Motivation in language courses. Procedia-Social and Behavioral Sciences. 2014 Mar 19;122:353-6. doi:10.1016/j.sbspro.2014.01.1353
Saputro RE, Salam S, Zakaria MH, Anwar T. A gamification framework to enhance students' intrinsic motivation on MOOC. Telkomnika. 2019 Feb 1;17(1):170-8. doi:10.12928/telkomnika.v17i1.10090
Carole R, Hyokyeong LE. Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction.    
Law KM, Lee VC, Yu YT. Learning motivation in e-learning facilitated computer programming courses. Computers & Education. 2010 Aug 1;55(1):218-28. doi:10.1016/j.compedu.2010.01.007
Zhou M, Winne PH. Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction. 2012 Dec 1;22(6):413-9. doi:10.1016/j.learninstruc.2012.03.004
Chyung SY, Moll AJ, Berg SA. The role of intrinsic goal orientation, self-efficacy, and e-learning practice in engineering education. Journal of Effective Teaching. 2010;10(1):22-37.         
McCollum DL, Kajs LT. Applying goal orientation theory in an exploration of student motivations in the domain of educational leadership. Educational Research Quarterly. 2007 Sep;31(1):45-59.  
Shen D, Cho MH, Tsai CL, Marra R. Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education. 2013 Oct 1;19:10-7. doi:10.1016/j.iheduc.2013.04.001
Zarrin F, Montazer GA. Designing an intelligent tutoring system based on learners' selfefficacy and learning style ‎features. 7th International Conference on e-Learning and e-Teaching 2019              
Huang X, Mayer RE. Adding self-efficacy features to an online statistics lesson. Journal of Educational Computing Research. 2019 Jul;57(4):1003-37. doi:10.1177/0735633118771085
Saadé RG, Kira D. Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research. 2009 Jan 1;8(1):177-91. doi:10.28945/166
Shih HP. Using a cognition-motivation-control view to assess the adoption intention for Web-based learning. Computers & Education. 2008 Jan 1;50(1):327-37. doi:10.1016/j.compedu.2006.06.001
Ehlers UD. Quality in e-Learning from a Learner's Perspective. European Journal of Open, Distance and E-Learning, May 2004-Best Paper Award at the Third EDEN Research Workshop 2004, Oldenburg, Germany. Distances et médiations des savoirs. Distance and Mediation of Knowledge. 2018 Aug 9(23). doi:10.4000/dms.2707
Carmona C, Castillo G, Millán E. Discovering student preferences in e-learning. InProceedings of the international workshop on applying data mining in e-learning 2007 Sep (pp. 33-42).    
Lai CL, Hwang GJ, Liang JC, Tsai CC. Differences between mobile learning environmental preferences of high school teachers and students in Taiwan: A structural equation model analysis. Educational Technology Research and Development. 2016 Jun;64(3):533-54. doi:10.1007/s11423-016-9432-y
Normadhi NB, Shuib L, Nasir HN, Bimba A, Idris N, Balakrishnan V. Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education. 2019 Mar 1;130:168-90. doi:10.1016/j.compedu.2018.11.005
Brusilovsky P, Millán E. User models for adaptive hypermedia and adaptive educational systems. InThe adaptive web 2007 (pp. 3-53). Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-72079-9_1
Thalmann S. Adaptation criteria for preparing learning material for adaptive usage: Structured content analysis of existing systems. InSymposium of the Austrian HCI and Usability Engineering Group 2008 Nov 20 (pp. 411-418). Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-89350-9_29
Grimley M, Riding R. Individual differences and web-based learning. InCognitive and emotional processes in web-based education: Integrating human factors and personalization 2009 (pp. 1-24). IGI Global. doi:10.4018/978-1-60566-392-0.ch001
Kumar A, Singh N, Ahuja NJ. Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001. and 2016. International Journal of Cognitive Research in Science, Engineering and Education. 2017;5(2):83. doi:10.5937/ijcrsee1702083K
Felder RM, Silverman LK. Learning and teaching styles in engineering education. Engineering education. 1988 Apr 1;78(7):674-81. 
Honey P, Mumford A. Using your learning styles. Chartered Institute of Personnel and Development; 1986.       
Kolb DA, Osland J, Rubin IM, Rubin IM, Osland J. Organizational behavior: An experiential approach. Englewood Cliffs, NJ: Prentice-Hall; 1991.    
Sun KT, Lin YC, Yu CJ. A study on learning effect among different learning styles in a Web-based lab of science for elementary school students. Computers & Education. 2008 May 1;50(4):1411-22. doi:10.1016/j.compedu.2007.01.003
Montazer GA, Khoshniat H. E-Learners' Activity Categorization Based on Their Learning Styles Using ART Family Neural Network. International Journal of Information & Communication Technology Research. 2012 Apr 30;4(2):11-26.         
Ghorbani F, Montazer GA. Learners grouping in e-learning environment using evolutionary fuzzy clustering approach.           
Graf S, Kinshuk ZQ, Maguire P, Shtern V. An architecture for dynamic student modelling of learning styles in learning systems and its application for adaptivity. InIADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2010) 2010 Oct.          
García P, Amandi A, Schiaffino S, Campo M. Evaluating Bayesian networks' precision for detecting students' learning styles. Computers & Education. 2007 Nov 1;49(3):794-808. doi:10.1016/j.compedu.2005.11.017
Chen H, Yin C, Li R, Rong W, Xiong Z, David B. Enhanced learning resource recommendation based on online learning style model. Tsinghua Science and Technology. 2019 Oct 7;25(3):348-56. doi:10.26599/TST.2019.9010014
Cabada RZ, Estrada ML, García CA. EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Systems with Applications. 2011 Aug 1;38(8):9522-9. doi:10.1016/j.eswa.2011.01.145
Özpolat E, Akar GB. Automatic detection of learning styles for an e-learning system. Computers & Education. 2009 Sep 1;53(2):355-67. doi:10.1016/j.compedu.2009.02.018
Graf S, Liu TC. Identifying learning styles in learning management systems by using indications from students' behaviour. In2008 eighth ieee international conference on advanced learning technologies 2008 Jul 1 (pp. 482-486). IEEE. doi:10.1109/ICALT.2008.84
Bendall RC, Galpin A, Marrow LP, Cassidy S. Cognitive style: Time to experiment. Frontiers in Psychology. 2016 Nov 15;7:1786. doi:10.3389/fpsyg.2016.01786  PMid:27895616 PMCid:PMC5108774    
Witkin HA, Moore CA, Goodenough DR, Cox PW. Field-dependent and field-independent cognitive styles and their educational implications. Review of educational research. 1977 Mar;47(1):1-64. doi:10.3102/00346543047001001
Pithers RT. Cognitive Learning Style: a review of the field dependent-field independent approach. Journal of Vocational Education & Training. 2002;54(1):117-32. doi:10.1080/13636820200200191
Pask G. Styles and strategies of learning. British journal of educational psychology. 1976 Jun;46(2):128-48. doi:10.1111/j.2044-8279.1976.tb02305.x
Riding R, Cheema I. Cognitive styles-an overview and integration. Educational psychology. 1991 Jan 1;11(3-4):193-215. doi:10.1080/0144341910110301
Ghorbani F, Montazer GA. Swarm intelligence grouping of e-learners using fuzzy inspired PSO method. International Journal of Information and Communication Technology Research. 2014 Dec 15;6(4):41-7.             
Lusk DL, Evans AD, Jeffrey TR, Palmer KR, Wikstrom CS, Doolittle PE. Multimedia learning and individual differences: Mediating the effects of working memory capacity with segmentation. British Journal of Educational Technology. 2009 Jul;40(4):636-51. doi:10.1111/j.1467-8535.2008.00848.x
Corr PJ, Matthews G, editors. The Cambridge handbook of personality psychology. Cambridge University Press; 2020 Sep 3. doi:10.1017/9781108264822
Allemand M, Steiger AE, Hill PL. Stability of personality traits in adulthood. GeroPsych. 2013 Feb 27. doi:10.1024/1662-9647/a000080
Tlili A, Essalmi F, Jemni M, Chen NS. Role of personality in computer based learning. Computers in Human Behavior. 2016 Nov 1;64:805-13. doi:10.1016/j.chb.2016.07.043
Digman JM. Personality structure: Emergence of the five-factor model. Annual review of psychology. 1990 Feb;41(1):417-40. doi:10.1146/annurev.ps.41.020190.002221
Felder RM, Felder GN, Dietz EJ. The effects of personality type on engineering student performance and attitudes. Journal of engineering education. 2002 Jan;91(1):3-17.  doi:10.1002/j.2168-9830.2002.tb00667.x
Fatahi S, Moradi H, Kashani-Vahid L. A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments. Artificial Intelligence Review. 2016 Oct;46(3):413-29. doi:10.1007/s10462-016-9469-7
Ghorbani F, Montazer GA. E-learners' personality identifying using their network behaviors. Computers in Human Behavior. 2015 Oct 1;51:42-52. doi:10.1016/j.chb.2015.04.043
Kabassi K, Virvou M. Personalised adult e-training on computer use based on multiple attribute decision making. Interacting with computers. 2004 Feb;16(1):115-32. doi:10.1016/j.intcom.2003.11.006
Munoz-Organero M, Munoz-Merino PJ, Kloos CD. Adapting the speed of reproduction of audio content and using text reinforcement for maximizing the learning outcome though mobile phones. IEEE Transactions on Learning Technologies. 2011 Apr 5;4(3):233-8. doi:10.1109/TLT.2011.8
Höffler TN. Spatial ability: Its influence on learning with visualizations-a meta-analytic review. Educational psychology review. 2010 Sep 1;22(3):245-69. doi:10.1007/s10648-010-9126-7
Kollias O. Services Anthroposphere: Describing Services into Retailing with the Aid of Geosciences.         
Flavell JH. Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American psychologist. 1979 Oct;34(10):906. doi:10.1037/0003-066X.34.10.906
Good TL, editor. 21st century education: A reference handbook. Sage; 2008 Oct 2.          
Schraw G. Promoting general metacognitive awareness. Instructional science. 1998 Mar;26(1):113-25. doi:10.1023/A:1003044231033
Ceylan E, Harputlu L. Metacognition in reading comprehension. The Literacy Trek. 2015;1(1):28-36.          
Paireekreng W, Prexawanprasut T. An integrated model for learning style classification in university students using data mining techniques. In2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) 2015 Jun 24 (pp. 1-5). IEEE. doi:10.1109/ECTICon.2015.7206951
Andries AM. Positive and negative emotions within the organizational context. Global Journal of Human Social Science 2011; 11(9): .     
Lytras MD, Sicilia MA. The Knowledge Society: a manifesto for knowledge and learning. International Journal of Knowledge and Learning. 2005 Jan 1;1(1-2):1-1. doi:10.1504/IJKL.2005.006259
Tobarra L, Robles-Gómez A, Ros S, Hernández R, Caminero AC. Analyzing the students' behavior and relevant topics in virtual learning communities. Computers in Human Behavior. 2014 Feb 1;31:659-69. doi:10.1016/j.chb.2013.10.001
Vanslambrouck S, Zhu C, Lombaerts K, Philipsen B, Tondeur J. Students' motivation and subjective task value of participating in online and blended learning environments. The Internet and Higher Education. 2018 Jan 1;36:33-40. doi:10.1016/j.iheduc.2017.09.002
Ackerman C. What is Self-Efficacy Theory in Psychology? Definition & Examples. 20 April 2019 (accessed https://positivepsychologyprogram.com/self-efficacy).
Bloom BS. Taxonomy of educational objectives. Vol. 1: Cognitive domain. New York: McKay. 1956;20:24.
Interdisciplinary Journal of Virtual Learning in Medical Sciences
Volume 12, Issue 1
March 2021
Pages 1-21

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