Consequential effects of using competing perspectives to predict learning style in e-learning systems

The learning processes have been significantly impacted by technology. Numerous learners have adopted technology-based learning systems as the preferred form of learning. It is then necessary to identify the learning styles of learners to deliver appropriate resources, engage them, increase their motivation, and enhance their satisfaction and learning outcomes. Adopting mixed method, this study evaluated the effects of using collaborative and automatic perspectives to predict VARK learning styles. Participants were first-year undergraduate Computer Science students. Questionnaires were administered and activity/event data were collected for analysis. Using Naive Bayes, J48, OneR, and SMO classifiers to extract patterns in activity/event data, the experimental results of the automatic perspective indicated improved classification of learning styles and performance of students compared with the collaborative technique. The prediction relevance of multimodal and read/write learning styles and accuracy of classification were better than other learning styles. Kinesthetic was not automatically identified. The students have different learning preferences in the same discipline; however, multimodal and read/write learning styles were dominant in both perspectives. Students who understood varied and simple formats of learning imagery objects were more likely to respond effectively to related assessments, and those who received varieties of course contents and information had their performance improved.