Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/137587
Type: Conference paper
Title: Mastery Learning and Productive Failure: Examining Constructivist Approaches to teach CS1
Author: Izu, M.
Ng, D.
Weerasinghe, H.
Citation: Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group (PPIG 2022), 2023 / Holland, S., Petre, M., Church, L., Marasoiu, M. (ed./s), pp.168-178
Publisher: Psychology of Programming Interest Group (PPIG)
Publisher Place: Online
Issue Date: 2023
Conference Name: Psychology of Programming Interest Group (PPIG) (5 Sep 2022 - 9 Sep 2022 : Milton Kyenes, UK)
Editor: Holland, S.
Petre, M.
Church, L.
Marasoiu, M.
Statement of
Responsibility: 
Cruz Izu, Daniel Ng, Amali Weerasinghe
Abstract: The struggles of novices taking introductory computer science courses to master basic constructs and develop an understanding of the notional machine continues to drive computer science education in the search of new pedagogical approaches. This work examines in depth two recent proposals: mastery learning and productive failure. Both approaches are grounded by constructivism, which should reduce the challenges that CS1 students face when learning to code. By exploring the concepts that drive these pedagogical approaches, this study aims to make constructivism more accessible to CS0/CS1 teachers. The two approaches illustrate and highlight key concepts that support constructive learning. The main outcomes from this work are the concept maps generated for each pedagogical approach, along with descriptive tables of their concepts. Both approaches support constructive learning by utilising (1) adaptive instruction that aligns with the current constructed knowledge of students, and (2) the use of student’s failure as key to identify knowledge gaps and improve learning.
Keywords: constructivism; active learning; failure; prior knowledge
Rights: © 2022 Psychology of Programming Interest Group. All rights reserved.
Published version: https://www.ppig.org/papers/2022-ppig-33rd-izu/
Appears in Collections:Computer Science publications

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