Models of Mastery Learning for Computing Education

Claudia Szabo, Miranda C. Parker, Michelle Friend, Johan Jeuring, Tobias Kohn, Lauri Malmi, Judithe Sheard

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationSIGCSE TS 2025 - Proceedings of the 56th ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery
Pages1092-1098
Number of pages7
ISBN (Electronic)9798400705311
DOIs
Publication statusPublished - 18 Feb 2025
Event56th Annual SIGCSE Technical Symposium on Computer Science Education, SIGCSE TS 2025 - Pittsburgh, United States
Duration: 26 Feb 20251 Mar 2025

Publication series

NameSIGCSE TS 2025 - Proceedings of the 56th ACM Technical Symposium on Computer Science Education
Volume1

Conference

Conference56th Annual SIGCSE Technical Symposium on Computer Science Education, SIGCSE TS 2025
Country/TerritoryUnited States
CityPittsburgh
Period26/02/251/03/25

Bibliographical note

Publisher Copyright:
© 2025 Copyright held by the owner/author(s).

Keywords

  • competency-based learning
  • mastery learning
  • models of instruction

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