Towards Automated Interactive Tutoring - Focussing on Misconceptions and Adaptive Level-Specific Feedback
Lea Jell, Corinna List, Michael F. Kipp · 2023
Programming is an essential cross-disciplinary skill, yet teaching it effectively in large classes can be challenging due to the need for close feedback loops. Identifying and addressing common misconceptions is particularly important during the initial stages of learning to program. While automated interactive tutoring systems have the potential to offer personalized tutoring at scale, current systems tend to emphasize errors and predefined solutions rather than focusing on common misconceptions. In this study, we introduce a novel platform centered on addressing misconceptions in programming education. We describe methods for detecting misconceptions using Abstract Syntax Trees (AST) and providing tailored, level-specific feedback to emulate human-like tutoring. As an empirical basis for this project, we gathered data from various introductory programming courses. Additionally, we advocate for the establishment of a repository of common misconceptions, offering examples derived from both the literature and our own data. Investigating misconceptions can ultimately enhance the teaching strategies of both human educators and AI agents, such as GPT, in guiding learners effectively.