Modeling students' performances in activity-based e-learning from a learning analytics perspective: Implications and relevance for learning design

This paper reports the findings of a research using marks of students in learning activities of an online module to build a predictive model of performance for the final assessment of the module. The objectives were (1) to compare the performances of students of two cohorts in terms of continuous learning assessment marks and final learning activity marks and (2) to model their final performances from their learning activities forming the continuous assessment using predictive analytics and regression analysis. The findings of this study combined with other findings as reported in the literature demonstrate that the learning design is an important factor to consider with respect to application of learning analytics to improve teaching interventions and students' experiences. Furthermore, to maximise the efficiency of learning analytics in eLearning environments, there is a need to review the way offline activities are to be pedagogically conceived so as to ensure that the engagement of the learner throughout the duration of the activity is effectively monitored.