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Post Recommendation System Impact on Student Participation and Performance in an Online AI Graduate Course

Published: 01 June 2022 Publication History

Abstract

Embedding a post recommendation system in online course forums improves students' ability to find relevant course content. Yet, there is limited research quantifying how these interventions impact forum interactions and students' class performance. We randomly divide an on-line class in a Masters degree-seeking program into control and experimental sections. Midway through the class, for the experimental section, we introduce an information retrieval system that suggests relevant posts to students while they compose their new posts. The average number of initial posts in the discussion forum dropped by 55% for the experimental group compared to 21% in the control group. In post-hoc analysis, we classify students as having an active or passive (i.e., mostly lurking) forum participation style. Posts per student per assignment by passive participants in the experimental group decreased by 15%, while passive participants' posts in the control group increased by 200%. The number of answers given by instructors in the experimental group decreased twice as much as the control group after intervention, though the difference was not statistically significant. The reduction of posts was not associated with a decrease in academic performance (grades) for the experimental group. This line of research convinced the popular forum Piazza to implement a post recommendation system similar to that used in these experiments.

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Cited By

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  • (2024)Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ ScaleProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664667(376-382)Online publication date: 9-Jul-2024
  • (2023)Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming CourseApplied System Innovation10.3390/asi60500836:5(83)Online publication date: 21-Sep-2023
  • (2023)Managing the Chaos: Approaches to Navigating Discussion Forums for Instructional StaffProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3596197(406-410)Online publication date: 20-Jul-2023
  • Show More Cited By

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                cover image ACM Other conferences
                L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale
                June 2022
                491 pages
                ISBN:9781450391580
                DOI:10.1145/3491140
                This work is licensed under a Creative Commons Attribution International 4.0 License.

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                Published: 01 June 2022

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                Author Tags

                1. information retrieval
                2. online forums
                3. online learning

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                L@S '22
                L@S '22: Ninth (2022) ACM Conference on Learning @ Scale
                June 1 - 3, 2022
                NY, New York City, USA

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                View all
                • (2024)Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ ScaleProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664667(376-382)Online publication date: 9-Jul-2024
                • (2023)Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming CourseApplied System Innovation10.3390/asi60500836:5(83)Online publication date: 21-Sep-2023
                • (2023)Managing the Chaos: Approaches to Navigating Discussion Forums for Instructional StaffProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3596197(406-410)Online publication date: 20-Jul-2023
                • (2023)Personalized Recommendation System for Online Learning: An Opportunity2023 8th International Conference on Business and Industrial Research (ICBIR)10.1109/ICBIR57571.2023.10147613(128-132)Online publication date: 18-May-2023

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