Learner behavior prediction in a learning management system
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Learner behavior prediction in a learning management system Charles Lwande 1
& Robert
Oboko 1 & Lawrence Muchemi 1
Received: 4 July 2020 / Accepted: 23 October 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Learning style . Cognitive style . Learning management system . Learner
modeling . Learner behavior . Machine learning . Neural network The corresponding authors: Robert Oboko helped with the analysis and interpretation of data while Lawrence Muchemi helped with the revision and editing of the manuscript.
* Robert Oboko [email protected] * Lawrence Muchemi [email protected] Charles Lwande [email protected]
1
School of Computing and Informatics, University of Nairobi, Box 30197, Nairobi 00100, Kenya
Education and Information Technologies
1 Introduction Several theories have attempted to explain how students conduct themselves during a learning session. The learning style and cognitive style both offer useful explanations of how learners behave during a learning process. Learning style is defined as an indicator of the way a learner observes, makes interactions with, and responds to learning content (Blakemore et al. 1984). Some examples of learning style models are the Felder-Silverman Learning Style Model (Felder 1988), (Honey and Mumford 1982), Myers Briggs Type Indicator(Myers 1995), Kolb learning model (Kolb 2015), and VARK learning styles (Fleming 2014). In the Felder-Silverman Learning Style Model (FSLSM), learning styles are grou
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