Behaviour Prediction in a Learning Management System

Learning Management Systems (LMS) lack automated intelligent components that analyse data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaire related to a specific learning style and cognitive psychometric tests have been used to identify such behaviour. The problem such method is that a leaner can give inaccurate information, time consuming and prone to errors. Although literature reports complex models predicting leaning styles, only a few have used machine learning methods such as k-nearest neighbour (KNN). The primary objective of this study was to design, develop and evaluate a model based on machine learning model for predicting LS from LMS log records. Approximately 200,000 log records of 199 students who had accessed e-Learning course 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 set. A model using K-NN algorithm designed and implemented on using r-studio programming language. The model was trained to predict LS and classify each student based on FSLSM. From this, a model predicting learning behaviour based on the theory was developed and evaluated. Preliminary results are promising demonstrating the model after full validation can be relied on to identify the LS.