Intelligent Agents that Support Students with Self-Study

Abstract

University students are expected to study on their own for large amounts of time. However a lot of these hours are not spent effectively by students. Eventually students who have trouble with self-studying in an effective manner may end up failing courses because of this. When students realise they have a (self-study) problem they can seek aid by contacting the academic counsellor. However, there are problems with this workflow: students often need time to acknowledge they are having issues. Then, even when students contact the academic counsellor to get help, it is a difficult task to provide personalised support for each student.
In this thesis we investigate the feasibility of a self-study support agent that can assist students with feedback on their self-study behavior. This agent can support students next to the academic counsellors. An agent can continuously track students and provide immediate feedback, whereas counsellors have a limited amount of time available (per student). As part of this thesis we conduct a focus group with academic counsellors, organise a workshop with first year students to create a design for a prototype self-study support agent. Thereafter we implement this prototype and use this in an experiment where the activities of several first year students are tracked over the period of two weeks. Then we analyse the data collected with our prototype agent. In doing so we show that the concept of the self-study support agent is feasible. We envision that future work can realise actual deployment of a self-study support agent.