Misconception Driven Student Analysis Model: Applications of a Cognitive Model in Teaching Computing

Feedback contextualized to curriculum content and misconceptions is a crucial piece in any learning experience. However, looking through student code and giving feedback requires more time and resources than an instructor typically has available, delaying feedback delivery. Intelligent Tutors for teaching Programming (ITPs) are designed to immediately deliver contextualized feedback of high quality to several students. However, they take significant effort and expertise to develop courses and practice problems, making them difficult to adapt to new situations. Because of this, the most frequently used feedback techniques for immediate feedback systems focus on highlighting incorrect output or pointing out errors in student code. These systems allow for quick development of practice problems and are easily adaptable to new contexts, however, the feedback isn't contextualized to curriculum content and misconceptions. This dissertation explores the implications of the Misconception-Driven Student Model (MDSM) as a model for developing alternatives to the aforementioned methods. I explore the implications and impact of MDSM with relation to feedback through the following thesis: Authoring feedback using a cognitive student model supports student learning of programming. In this dissertation I review relevant cognitive theory and feedback systems and two quasi-experimental studies examining the efficacy of MDSM.