Abstract
Learning Management Systems (LMS) lack automated intelligent components that analyze data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaires related to a specific learning style model and cognitive psychometric tests have been used to identify such behavior. The problem with such methods is that a learner can give inaccurate information. The manual method is also time-consuming and prone to errors. Although literature reports complex models predicting learning styles, only a few have used machine learning methods such as an artificial neural network (ANN). The primary objective of this study was to design, develop, and evaluate a model based on machine learning for predicting learner behavior from LMS log records. Approximately 200,000 log records of 311 students who had accessed e-Learning courses for a 15-week semester were extracted from LMS to create a dataset. Machine learning concepts were identified from the log records. The dataset was split into training and testing sets. A model using the artificial neural network algorithm was designed and implemented using an r-studio programming language. The model was trained to predict learner behavior and classify each student. The prediction success rate of 0.63, 0.67, 0.64, 0.65, 0.26, 0.64 accuracy, precision, recall, f-score, kappa, and Area Under the Curve (AUC) respectively were recorded. This demonstrates that the model after full validation can be relied on to identify learner behavior.
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The students’ data set used in the study are considered sensitive and can only be shared upon seeking permission from the University of Nairobi management.
References
Abdelhadi, A., Ibrahim, Y. and Nurunnabi, M. (2019). Investigating engineering student learning style trends by using multivariate statistical analysis. Education Sciences, 9(1). https://doi.org/10.3390/educsci9010058.
Ahmad, Z., & Shahzadi, E. (2018). Prediction of students. Academic Performance using Artificial Neural Network, 40(3), 157–164.
Aissaoui, O. El et al. (2018). A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education and Information Technologies. https://doi.org/10.1007/s10639-018-9820-5.
Andersen, H. V., & Sorensen, E. K. (2017). Enhancing understanding. Flow and Self - Efficacy in Learners with developmental and attention Difficulties Through ICT - Based Interventions, 20(1), 153–174.
Azadeh, A. and Behshtipour, B. (2008) ‘The effect of neural network parameters on the performance of neural network forecasting’, in IEEE International Conference on Industrial Informatics (INDIN). IEEE, pp. 1498–1505. https://doi.org/10.1109/INDIN.2008.4618341.
Begum, M. R. and David, K. (2017). Discovering student learning style using min max cascade neural network. Indian Journal of Science and Technology, 10(25), 1–15. https://doi.org/10.17485/ijst/2017/v10i25/110081.
Bernard, J. et al. (2015) ‘Using artificial neural networks to identify learning styles’, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp. 541–544. https://doi.org/10.1007/978-3-319-19773-9_57.
Blakemore, T., McCray, P. and Coker, C. (1984) A guide to learning style assessment. Research and Training Center Research Report.
Carver, C., Howard, R., & Lane, W. (1999). Addressing different learning styles through course hypermedia. IEEE Transactions on Education, 42(May), 33–38.
Chang, Y. H., et al. (2016). Yet another adaptive learning management system based on Felder and Silverman’S learning styles and Mashup. Eurasia Journal of Mathematics, Science and Technology Education, 12(5), 1273–1285. https://doi.org/10.12973/eurasia.2016.1512a.
Don, M. (2014) List Of Corporate Learning Management Systems. Available at: https://elearningindustry.com/list-corporate-learning-management-systems.
Felder, R. M. (1988) ‘Learning and Teaching Styles in Engineering Education’, engineering education, 78(7), pp. 674–681. Available at: http://www4.ncsu.edu/unity/lockers/users/f/felder/public/Papers/LS-1988.pdf (Accessed: 11 December 2017).
Felder, R. M., & Spurlin, J. (2005). Applications, reliability, and validity of the index of learning styles. International Journal of Engineering, 21(1), 113–112.
Ferreira, L. D. et al. (2019). A comparative analysis of the automatic modeling of Learning Styles through Machine Learning techniques. In: Proceedings - Frontiers in Education Conference, FIE. IEEE, pp. 1–8. https://doi.org/10.1109/FIE.2018.8659191.
Fleming, N. D. (2014) The VARK Modalities. Available at: https://web.archive.org/web/20150314235648/http://vark-learn.com/introduction-to-vark/the-vark-modalities/.
Gautam, D. et al. (2018) ‘Automated speech act categorization of chat utterances in virtual internships’, International Educational Data Mining Society., pp. 341–347. Available at: http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_115.pdf.
Graf, S., Liu, T. C., Kinshuk, Chen, N. S., & Yang, S. J. H. (2009). ‘Learning styles and cognitive traits - their relationship and its benefits in web-based educational systems’, Computers in Human Behavior. Elsevier Ltd, 25(6), 1280–1289. https://doi.org/10.1016/j.chb.2009.06.005.
Greer, J. et al. (1998) ‘The intelligent helpdesk: Supporting peer-help in a university course’, in International Conference on Intelligent Tutoring Systems, pp. 494–503.
Holzinger, A. and Pasi, G. (2013) ‘An Interactive Course Analyzer for Improving Learning Styles Support Level’, in HCI-KDD 2013, LNCS 7947. Verlag Berlin Heidelberg 2013: Springer, pp. 136–147.
Honey, P., & Mumford, A. (1982). The manual of learning styles. Berkshire: Peter Honey.
Honeychurch, S. et al. (2017) ‘Learners on the periphery: Lurkers as invisible learners’, European Journal of Open, 20(1), pp. 191–1027. Available at: http://www.eurodl.org/?p=current&sp=full&article=752.
Kellogg, R. T. (2012) Fundamentals of Cognitive Psychology. 2nd eds. London: Sage.
Khamis, A. (2001). The Effects of Outliers Data on Neural Network Performance. Journal of Applied Sciences, 1394–1398. https://doi.org/10.3923/jas.2005.1394.1398.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers and Education, 106. https://doi.org/10.1016/j.compedu.2016.12.006.
Kolb, D. (2015) Experiential Learning: Experience as the Source of Learning and Development. 2nd eds. Pearson education.
Kuljis, J. and Liu, F. (2005) ‘A comparison of learning style theories on the suitability for elearning’, in IASTED International Conference on Web Technologies, Applications, and Services, Calgary, Alberta, Canada, July 4–6, 2005. Alberta.
Myers, I. B. (1995). Gifts differing: Understanding personality type. Reprint: Davies-Black Publishing.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles concepts and evidence. Psychological Science in the Public Interest, Supplement., 9, 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x.
Peter, S. E., Bacon, E., & Dastbaz, M. (2010). Adaptable, personalized e-learning incorporating learning styles. Campus-Wide Information Systems, 27(2), 91–100. https://doi.org/10.1108/10650741011033062.
Sweta, S., & Lal, K. (2016). Learner model for automatic detection of learning style using FCM in adaptive E-learning system. IOSR Journal of Computer Engineering, 18(2), 18–24. https://doi.org/10.9790/0661-1802041824.
Winter, M. and McCalla, G. (2003) ‘An analysis of group performance in terms of the functional knowledge and teamwork skills of group members’, in Workshop on User and Group Models for Web-based Collaborative Environments, 9th International Conference on User Modeling (UM 2003), p. 35.
Xie, H., et al. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers and Education, 140. https://doi.org/10.1016/j.compedu.2019.103599.
Yen, C.-H., et al. (2015). An analytics-based approach to managing cognitive load by using log data of learning management systems and footprints of social media. Educational Technology & Society, 18(4), 141–158.
Yu, C.-H., Wu, J. and Liu, A.-C. (2019) ‘Predicting learning outcomes with MOOC clickstreams’, Education Sciences, 9(104). Available at: www.mdpi.com/journal/education.
Zheng, A. (2015) Evaluating machine learning models. First Edit. O’Reilly Media, Inc.
Zhong-Lin, L. and Barbara, A. D. (2007) ‘Cognitive psychology’, Scholarpedia.
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This is to express my appreciation to the University of Nairobi management for granting access to Learning Management System data.
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Lwande, C., Oboko, R. & Muchemi, L. Learner behavior prediction in a learning management system. Educ Inf Technol 26, 2743–2766 (2021). https://doi.org/10.1007/s10639-020-10370-6
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DOI: https://doi.org/10.1007/s10639-020-10370-6