A Bayesian network student model to enhance university group assignments

Abstract

In this master thesis, a Bayesian Network student model, named DigiBnet artefact is built for a future Intelligent Tutoring System in the form of a chatbot that supports Business Information Systems (BIS) students at University of Applied Science Northwestern Switzerland (FHNW). First, an exhaustive literature review is conducted to reveal the research gap, followed by a detailed research methodology where the applied research method is explained. The DigiBnet artefact was built to predict the motivational level, the general knowledge level, the topic overview knowledge and the solution implementation capabilities of the students who work on their group assignment. The prediction was based on different observations that was collected from an imitated chatbot conversation with a teacher about a specific domain in the Digitalization of Business Processes (DigiBP) module. From these observations DigiBnet predicted the characteristics of the students regarding the above mentioned four aspects and selected a tutoring advice that would best fit to the student’s need. The results were shared with the students and their feedback was collected in a form of interviews. The main findings revealed that students agreed with DigiBnet’s assessment and they positively accepted its evaluation. It helped them self-reflect to their knowledge and motivational level. DigiBnet’s assessment would motivate the students the most, if they receive prompt feedback during the conversation, in case of a gap in their knowledge....