A time series interaction analysis method for building predictive models of learners using log data

As courses become bigger, move online, and are deployed to the general public at low cost (e.g. through Massive Open Online Courses, MOOCs), new methods of predicting student achievement are needed to support the learning process. This paper presents a novel method for converting educational log data into features suitable for building predictive models of student success. Unlike cognitive modelling or content analysis approaches, these models are built from interactions between learners and resources, an approach that requires no input from instructional or domain experts and can be applied across courses or learning environments.