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Virginia Tech

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

Abstract

dc:description.abstract

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.

Degree

thesis:*
Name thesis:degree_name
Doctor of Philosophy
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Computer Science and Applications
Department dc:contributor.department
Computer Science
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Gusukuma, Luke Satoru
Chair dc:contributor.committeechair
  • Kafura, Dennis G.
Committee members dc:contributor.committeemember
  • Edwards, Stephen H.
  • Tilevich, Eli
  • Williams, Thomas O.
  • Bart, Austin Cory
  • Shaffer, Clifford A.
  • Rivers, Kelly

Subjects

dc:subject × 2

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:26893
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/99288

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Gusukuma, Luke Satoru. Misconception Driven Student Analysis Model: Applications of a Cognitive Model in Teaching Computing. doctoral thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99288