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Board 254: Developing Tools, Pedagogies, and Policies for Computer-Based Collaborative Learning Activities

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

14

DOI

10.18260/1-2--42700

Permanent URL

https://peer.asee.org/42700

Download Count

135

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Paper Authors

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Morgan M. Fong University of Illinois, Urbana - Champaign

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Morgan is a PhD student in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Her current research focuses on developing methods and analyzing cooperative learning in undergraduate computing courses.

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Liia Butler University of Illinois, Urbana - Champaign

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Abdussalam Alawini University of Illinois, Urbana - Champaign

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Dr. Abdussalam Alawini received a doctoral degree in Computer Science from Portland State in 2016. In his Ph.D., he built systems to help scientists manage their file-based datasets by predicting relationships among spreadsheet documents. Passionate about a career in academia, Dr. Alawini joined the University of Pennsylvania in 2016 as a postdoctoral researcher. As a postdoc, he developed data citation and data provenance systems for scientists. Dr. Alawini's research interests are broadly in databases, applied machine learning, and education. He is particularly interested in applying machine learning methods to improve classroom experience and education in general. He is also interested in building next-generation data management systems, including data provenance, citation, and scientific management systems.

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Geoffrey L. Herman University of Illinois, Urbana - Champaign Orcid 16x16 orcid.org/0000-0002-9501-2295

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Dr. Geoffrey L. Herman is the Severns Teaching Associate Professor with the Department of Computer Science at the University of Illinois at Urbana-Champaign.

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Mariana Silva University of Illinois, Urbana - Champaign

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Mariana Silva is a Teaching Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Silva is known for her teaching innovations and educational studies in large-scale assessments and collaborative learning. She has participated in two major overhauls of large courses in the College of Engineering: she played a key role in the re-structure of the three Mechanics courses in the Mechanical Science and Engineering Department, and the creation of the new computational-based linear algebra course, which was fully launched in Summer 2021. Silva research focuses on the use of web-tools for class collaborative activities, and on the development of online learning and assessment tools. Silva is passionate about teaching and improving the classroom experience for both students and instructors.

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Abstract

This paper summarizes milestones, lessons learned, and preliminary research findings for an NSF IUSE project awarded in Fall 2021.

Collaborative learning can improve student learning, student persistence, and the classroom climate. Most of the evidence-based practices for collaborative learning rely on the assumption of face-to-face interactions or asynchronous online activities. In this project, we have developed tools and pedagogies for synchronous computer-supported collaborative learning activities that can be used in online and in-person classes.

Tool development: We have integrated collaborative-learning features to PrairieLearn, which is an open-source online assessment platform that supports mastery learning. We first developed an option for group assessments, where multiple students can share the same work (and consequently the same grade) with real-time synchronization. Currently we are finalizing the development of two additional features: a) the ability to assign team roles within the assessment, where each role can have different access permissions, such as view and edit. The assignment of structured team roles reduces students’ tendency to default into domineering team leaders or passive free-loaders. b) instructor and student dashboards to facilitate collaborative learning activities, providing information about group formation, division of tasks, request for assistance, etc.

Adoption: Our teaching innovations were initially developed to support three undergraduate computer science courses offered at a large public research university. All three courses adopted a flipped classroom format, where students watch pre-recorded videos prior to class, and work on collaborative learning activities during class time. The pedagogies and tools developed for this project have been successfully implemented in online and in-person sections of the three courses. From our knowledge, the tools have been adopted as well by at least 15 other courses in four different institutions using PrairieLearn.

Synchronous versus asynchronous courses: We compared how students worked collaboratively under two different course offerings: synchronous with structured roles and asynchronous without structured roles. Our results indicate that students in the synchronous course have improvement in the performance of submissions by a half to a full letter grade and need one week less of in-class time to finish assessments. Moreover, groups from the synchronous course had more evenly distributed work among members of a team when compared to groups from the asynchronous course.

Sense of belonging: We have conducted pre- and post- surveys that included measures of student’s sense of belonging in relation to their instructors and classmates (whole class). Pre-course surveys indicate women tend to experience a lower sense of belonging compared to their male peers in lower-level courses. Post-course surveys indicate both women and men’s whole class sense of belonging statistically significantly increases, with men and women reporting similar levels of sense of belonging on average after completing our courses using collaborative learning. We additionally ask students about their sense of belonging in relation to their collaborative groups (within group). Our preliminary results indicate students’ within-group sense of belonging is statistically significantly higher than their whole class sense of belonging.

Fong, M. M., & Butler, L., & Alawini, A., & Herman, G. L., & Silva, M. (2023, June), Board 254: Developing Tools, Pedagogies, and Policies for Computer-Based Collaborative Learning Activities Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42700

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