A Student Learning Style Auto-Detection Model in a Learning Management System
Received: 3 February 2023 | Revised: 14 April 2023 | Accepted: 22 April 2023 | Online: 2 June 2023
Corresponding author: Raja Rina Raja Ikram
Abstract
Learning style plays an important role in enabling students to have an efficient learning process. This paper proposes an auto-detection model of student learning styles in learning management systems based on student learning activities. A literature review was conducted to investigate the components of online learning activities. The search terms used were "online learning activities", "learning management systems", and "Felder-Silverman Learning Style Model (FSLSM)." A combination of the search terms above was also executed to enhance the search process. Based on the results of the review, eleven classes of online learning activities were identified, namely forum, chat, mail, reading materials, exam delivery time, exercises, access to examples, answer changes, learning materials, exam results, and information access. The online learning activities identified were then mapped to the Felder-Silverman model based on four model dimensions: processing, perception, input, and understanding. The proposed model shows the attributes of the online learning activities based on the dimensions in the FSLSM. The proposed model can assist educators to improve learning content according to the suitability of students and recommend appropriate learning materials to students based on their characteristics and preferences. Future studies include the use of machine learning algorithms such as decision trees to auto-detect student learning styles in learning management systems.
Keywords:
student learning style, Felder-Silverman learning management system, auto detectionDownloads
References
S. S. T. Alatawi et al., "A New Model for Enhancing Student Portal Usage in Saudi Arabia Universities," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7158–7171, Jun. 2021. DOI: https://doi.org/10.48084/etasr.4132
M. A. Al-Shargabi, "An Integrated Decision Support Model For Enhancing Continuous Improvement Of Academic Programs," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4835–4841, Oct. 2019. DOI: https://doi.org/10.48084/etasr.3079
M. Alsuwaiket, A. H. Blasi, and R. A. Al-Msie’deen, "Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4287–4291, Jun. 2019. DOI: https://doi.org/10.48084/etasr.2794
Y. Thamilarasan and R. R. R. Ikram, "MyMUET: A Design of a Mobile based Crowdsourced Assessment for Malaysia University English Test with Non-Synchronous Participant Interaction," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 2971–2977. DOI: https://doi.org/10.35940/ijitee.K2277.0981119
N. A. Kamarudin, R. R. R. Ikram, F. N. Azman, L. Salahuddin, and H. T. Jaya, "A design of gamification Artificial Intelligence coding activities to improve cognitive skills among primary students," in Proceedings of Mechanical Engineering Research Day 2022, Aug. 2022, pp. 191–192.
A. Sarabi-Asiabar et al., "The Relationship Between Learning Style Preferences and Gender, Educational Major and Status in First Year Medical Students: A Survey Study From Iran," Iranian Red Crescent Medical Journal, vol. 17, no. 1, Dec. 2014, Art. no. e18250. DOI: https://doi.org/10.5812/ircmj.18250
M. Shuib and S. N. Azizan, "Learning Style Preferences among Male and Female ESL Students in Universiti-Sains Malaysia," Journal of Educators Online, vol. 12, no. 2, pp. 103–141, Jul. 2015, doi: 10.9743/jeo.2015.2.3. DOI: https://doi.org/10.9743/JEO.2015.2.3
N. D. A. Halim, A. Jusoh, M. Mokhtar, and A. H. Abdullah, "Effectiveness of Using BizzApss in Enhancing Students’ Achievement with Different Learning Styles," in RCEE Conference Proceedings, 2016, vol. 1, pp. 260–264.
A. Viloria, I. R. Petro Gonzalez, and O. B. Pineda Lezama, "Learning Style Preferences of College Students Using Big Data," Procedia Computer Science, vol. 160, pp. 461–466, Jan. 2019. DOI: https://doi.org/10.1016/j.procs.2019.11.064
A. Seyal, Y. Mey, M. Matusin, nor zainah Siau, and A. Rahman, "Understanding Students Learning Style and Their Performance in Computer Programming Course: Evidence from Bruneian Technical Institution of Higher Learning," International Journal of Computer Theory and Engineering, vol. 7, no. 3, pp. 241–247, Jun. 2015. DOI: https://doi.org/10.7763/IJCTE.2015.V7.964
P. Ramírez-Correa, J. Alfaro-Pérez, and M. Gallardo, "Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques," Applied Sciences, vol. 11, no. 22, Jan. 2021, Art. no. 10505. DOI: https://doi.org/10.3390/app112210505
I. Katsaris and N. Vidakis, "Adaptive e-learning systems through learning styles: A review of the literature," Advances in Mobile Learning Educational Research, vol. 1, no. 2, pp. 124–145, Oct. 2021. DOI: https://doi.org/10.25082/AMLER.2021.02.007
R. Felder, "Learning and Teaching Styles in Engineering Education," Journal of Engineering Education, vol. 78, no. 7, pp. 674–681, Jan. 1988.
A. Hidayat, K. Adi, and B. Surarso, "Determine Felder Silverman Learning Style Model using Literature Based and K-Means Clustering," in 2021 Sixth International Conference on Informatics and Computing (ICIC), Jakarta, Indonesia, Aug. 2021, pp. 1–6. DOI: https://doi.org/10.1109/ICIC54025.2021.9633010
P. García, A. Amandi, S. Schiaffino, and M. Campo, "Using Bayesian Networks to Detect Students’ Learning Styles in a Web-based education system," Proceedings of ASAI 2005, Argentine Symposium on Artificial Intelligence, Jan. 2005.
P. García, S. Schiaffino, and A. Amandi, "An enhanced Bayesian model to detect students’ learning styles in Web-based courses," Journal of Computer Assisted Learning, vol. 24, no. 4, pp. 305–315, 2008. DOI: https://doi.org/10.1111/j.1365-2729.2007.00262.x
R. R. Maaliw III and M. A. Ballera, "Classification of Learning Styles in Virtual Learning Environment Using J48 Decision Tree," in 14th International Conference on Cognition and Exploratory Learning in Digital Age, Oct. 2017, pp. 149–156.
J. Bernard, T.-W. Chang, E. Popescu, and S. Graf, "Using Artificial Neural Networks to Identify Learning Styles," in Artificial Intelligence in Education, 2015, pp. 541–544. DOI: https://doi.org/10.1007/978-3-319-19773-9_57
L. Assis, A. C. Rodrigues, A. Vivas, C. G. Pitangui, C. M. Silva, and F. A. Dorça, "Relationship Between Learning Styles and Learning Objects: A Systematic Literature Review," International Journal of Distance Education Technologies, vol. 20, no. 1, pp. 1–18, 2022. DOI: https://doi.org/10.4018/IJDET.296698
J. Feldman, A. Monteserin, and A. Amandi, "Automatic detection of learning styles: state of the art," Artificial Intelligence Review, vol. 44, no. 2, pp. 157–186, Aug. 2015. DOI: https://doi.org/10.1007/s10462-014-9422-6
A. E. Labib, J. H. Canós, and M. C. Penadés, "On the way to learning style models integration: a Learner’s Characteristics Ontology," Computers in Human Behavior, vol. 73, pp. 433–445, Aug. 2017. DOI: https://doi.org/10.1016/j.chb.2017.03.054
Downloads
How to Cite
License
Copyright (c) 2023 Amirah Binti Rashid, Raja Rina Raja Ikram, Yarshini Thamilarasan, Lizawati Salahuddin, Noor Fazilla Abd Yusof, Zakiah Binti Rashid
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
