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Supporting Students at Scale: Profiling Student Behaviors on Usage and Impact of the Student Support System

Published: 18 February 2025 Publication History

Abstract

Flexible, competency-based learning in large, high-workload undergraduate CS courses provide students options to leverage time, instructors, and other course resources for individualized learning. However, at scale the wide range of resources can overwhelm students and instead create impersonal classrooms. We describe our experiences with a structured student support system that helps students navigate learning in a large CS2 course. This study extends prior work on automated, flexible assignment extensions and introduces a structured approach to scheduling student support meetings, where staff suggest specific assignment work plans and course resources with individual students. Our work-in-progress findings suggest that student support meetings create otherwise missing channels of communications within the large class. We introduce a comprehensive profiling approach based on student support system usage to characterize the background and work patterns of students and how they leverage larger framework of course resources available. This work invites further discussion to understand the complex interactions between undergraduate student learning and adequate course support.

References

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Dana Benedicto, Jordan Schwartz, Narges Norouzi, and Yan Lisa. 2024. WIP: Automated Flexible Extensions for Improving Learning Equity in Large Scale Computing Classrooms. In 2024 IEEE Frontiers in Education Conference (FIE). Washington, D.C., USA.
[2]
CAST. 2024. Universal Design for Learning Guidelines Version 3.0. https://udlguidelines.cast.org/ Retrieved October 14 2024 from
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Huanyi Chen and Paul A.S. Ward. 2023. The Value of Time Extensions in Identifying Students Abilities. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023). Association for Computing Machinery, New York, NY, USA, 512--518. https://doi.org/10.1145/3587102.3588847
[4]
Maria Henri, Michael D Johnson, and Bimal Nepal. 2017. A review of competency-based learning: Tools, assessments, and recommendations. Journal of engineering education, Vol. 106, 4 (2017), 607--638.
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Cormac LaLiberte. 2023. Data science, computer science majors show biggest increase in student enrollment. The Daily Cardinal. (2023).
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Yuerou Tang, Jacob Yim, Jordan Schwartz, Madison Bohannan, Dana Benedicto, Charisse Liu, Armando Fox, Lisa Yan, and Narges Norouzi. 2024. Supporting Mastery Learning with Flexible Extensions. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2 (Portland, OR, USA) (SIGCSE 2024). Association for Computing Machinery, New York, NY, USA, 1834--1835. https://doi.org/10.1145/3626253.3635615

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      cover image ACM Conferences
      SIGCSETS 2025: Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2
      February 2025
      493 pages
      ISBN:9798400705328
      DOI:10.1145/3641555
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 18 February 2025

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      1. competency-based learning
      2. flexible extensions
      3. student support

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      Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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      SIGCSE TS 2025
      The 56th ACM Technical Symposium on Computer Science Education
      February 26 - March 1, 2025
      Pittsburgh , PA , USA

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