| Summary: | 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. === Doctor of Philosophy === 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.
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