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.
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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