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How Do Students Collaborate? Analyzing Group Choice in a Collaborative Learning Environment

Published: 05 March 2021 Publication History

Abstract

Collaborative learning has been effective and widely adopted in Computer Science education. Existing studies have controlled for group sizes by assigning members to determine the optimal collaboration environment, with some focusing on a peer-programming environment and others observing a wider range of sizes and tasks.
We analyzed collaboration trends through an observational study of 189 students in a large upper-level Computer Science algorithms course, which uses a less-constrained collaborative setting. In the course, the collaboration policy encourages students to choose their own groups for each assignment, up to four other students, offering insight into how groups evolve in size and membership when students are given the freedom to self-select. Since each student is required to submit their own individual work, we collected information about the grade and self-reported collaborators of each research participant for nine assignments, including written and coding homework.
Our results show that any collaboration improved individual performance on average. For programming assignments, groups of size four were optimal. Across both written and programming assignments, larger groups performed better, including chains of collaboration greater than the course policy allowed. However, sizes 4-5 performed best within the bounds of the policy. We also demonstrate that factors impacting collaboration include homework difficulty, time of grade release, students' relative performance with respect to the class, as well as the homework type.

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      cover image ACM Conferences
      SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
      March 2021
      1454 pages
      ISBN:9781450380621
      DOI:10.1145/3408877
      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: 05 March 2021

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      1. collaboration groups
      2. collaborative learning
      3. computer science education
      4. group formation

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      • (2024)Towards inclusivity in AI: A comparative study of cognitive engagement between marginalized female students and peersBritish Journal of Educational Technology10.1111/bjet.1346755:6(2557-2573)Online publication date: 23-Apr-2024
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