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Increasing Students' Persistence in Computer Science through a Lightweight Scalable Intervention

Published: 07 July 2022 Publication History

Abstract

Research has shown that high self-assessment of ability, sense of belonging, and professional role confidence are crucial for students' persistence in computing. As grades in introductory computer science courses tend to be lower than other courses, it is essential to provide students with contextualized feedback about their performance in these courses. Giving students unambiguous and con- textualized feedback is especially important during COVID when many classes have moved online and instructors and students have fewer opportunities to interact. In this study, we investigate the effect of a lightweight, scalable intervention where students received personalized, contextualized feedback from their instructors after two major assignments during the semester. After each intervention, we collected survey data to assess students' self-assessment of computing ability, sense of belonging, intentions to persist in computing, professional role confidence, and the likelihood of stating intention to pursue a major in computer science. To analyze the effectiveness of our intervention, we conducted linear regression and mediation analysis on student survey responses. Our results have shown that providing students with personalized feedback can significantly improve their self-assessment of computing ability, which will significantly improve their intentions to persist in computing. Furthermore, our results have demonstrated that our intervention can significantly improve students' sense of belonging, professional role confidence, and the likelihood of stating an intention to pursue a major in computer science.

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Cited By

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  • (2025)Programming Self-Efficacy in CS: Adding Four Areas of Validity to the Steinhorst InstrumentProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701813(213-219)Online publication date: 12-Feb-2025
  • (2025)PICA: A Data-Driven Synthesis of Peer Instruction and Continuous AssessmentMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_1(3-17)Online publication date: 1-Jan-2025

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cover image ACM Conferences
ITiCSE '22: Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1
July 2022
686 pages
ISBN:9781450392013
DOI:10.1145/3502718
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2022

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Author Tags

  1. introductory computer science
  2. persistence in computing
  3. positive feedback
  4. self-assessment of computing ability

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View all
  • (2025)Programming Self-Efficacy in CS: Adding Four Areas of Validity to the Steinhorst InstrumentProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701813(213-219)Online publication date: 12-Feb-2025
  • (2025)PICA: A Data-Driven Synthesis of Peer Instruction and Continuous AssessmentMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_1(3-17)Online publication date: 1-Jan-2025

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