Understanding Time-Based Trends in Stakeholders’ Choice of Learning Activity Type Using Predictive Models

Abstract

The learning analytics communities, as well as most learning analytics research, have not frequently focused on time-based trends in the same virtual learning environment over different years of deployment, or on temporal trends in the selection of different activity types over a typical day. This paper contributes to this debate and provides a novel approach to learning analytics using a multinomial logit model for modeling the probabilities of students’ choice of learning activities during the hours of the day over several academic years. An abstraction called activity is introduced, which categorizes individual student’s log accesses to the virtual learning environment into more semantically meaningful categories. Consequently, the activity represents a sequence of semantically meaningful Web accesses related to a particular activity or task that a student of the virtual learning environment performs. This paper includes a comprehensive explanation of the model and an evaluation of the model. This paper introduces a case study, which shows that the multinomial logit model can give useful insight into the course schedule, as it shows what the peak times are for different types of activities. This paper also discusses the possible implications of the results in the context of virtual learning environment management and content improvement at the institutional level.

Keywords

• Computational and artificial intelligence
• learning management systems
• predictive models
• Web mining