Abstract
Mastery learning was first defined in the late 1960s, but despite the promise of a student-focused methodology to establish firm foundations for later studies, it has not been widely adopted. In this paper, we consider why. We consider the evidence for mastery learning, and explore the organisational, structural, staffing, and pedagogical needs and challenges. We find, immediately, that there are competing implementations for mastery learning in computing education. We propose a way of representing pedagogical theories that use aspects of mastery learning in a way that allows a clear separation of techniques that appear similar but are not identical, to ensure the most appropriate implementation for a given area of application. These techniques still share similar challenges for adoption. We build towards possible solutions by considering recent developments in generative AI - -and how these technologies could potentially provide automated assistants as a supporting component of mastery learning, in order to fully realise Bloom's vision.
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CITATION STYLE
Álvarez, C., Falkner, N., Kinnunen, P., Savelka, J., & Zhang, L. (2025). Show Me the Mastery Learning! Obstacles to Adoption and Opportunities for New Solutions. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE (Vol. 1, pp. 639–645). Association for Computing Machinery. https://doi.org/10.1145/3724363.3729104
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