Models of Mastery Learning for Computing Education

The application of mastery learning, where students progress through their learning in a self-paced manner until they have mastered specific concepts, is considered appealing for teaching introductory programming courses. Despite its growing popularity in computing and its extensive use in other disciplines, there is no overview of the design of courses that use mastery learning. In this position paper, we present an overview of five mastery learning models and discuss examples of how these can be applied in practice, both in foundational programming as well as more advanced courses. Our analysis focuses on the student progression through the course, the assessment structure, and the support for self-paced learning, including for struggling students. This work provides a greater understanding of mastery learning and its application in a computing education context.

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Publication Details

Published
2025-02-12
DOI
https://doi.org/10.1145/3641554.3701868
Citations
4
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
6.77
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article

Models of Mastery Learning for Computing Education

Miranda C. Parker, Johan Jeuring, Claudia Szabo, Judy Sheard et al.
4 citations
Teaching and Learning Programming
6.77
article

Models of Mastery Learning for Computing Education

Miranda C. Parker, Johan Jeuring, Claudia Szabo, Judy Sheard, Lauri Malmi, Tobias Kohn, Michelle Friend
article en
4 citations

Abstract

The application of mastery learning, where students progress through their learning in a self-paced manner until they have mastered specific concepts, is considered appealing for teaching introductory programming courses. Despite its growing popularity in computing and its extensive use in other disciplines, there is no overview of the design of courses that use mastery learning. In this position paper, we present an overview of five mastery learning models and discuss examples of how these can be applied in practice, both in foundational programming as well as more advanced courses. Our analysis focuses on the student progression through the course, the assessment structure, and the support for self-paced learning, including for struggling students. This work provides a greater understanding of mastery learning and its application in a computing education context.

Karlsruhe Institute of Technology (DE), University of Nebraska at Omaha (US), Utrecht University (NL), San Diego State University (US), Monash University (AU), The University of Adelaide (AU), Aalto University (FI)
Quality Education
Openalex Percentile: Top 4%
Teaching and Learning Programming
6.77
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