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Pilot Recommender System Enabling Students to Indirectly Help Each Other and Foster Belonging Through Reflections

Published: 21 March 2022 Publication History

Abstract

Without a sense of belonging, students may become disheartened and give up when faced with new challenges. Moreover, with the sudden growth of remote learning due to COVID-19, it may be even more difficult for students to feel connected to the course and peers in isolation. Therefore, we propose a recommendation system to build connections between students while recommending solutions to challenges. This pilot system utilizes students’ reflections from previous semesters, asking about learning challenges and potential solutions. It then generates sentence embeddings and calculates cosine similarities between the challenges of current and prior students. The possible solutions given by previous students are then recommended to present students with similar challenges. Self-reflection encourages students to think deeply about their learning experiences and benefit both learners and instructors. This system has the potential to allow reflections also to help future learners. By demonstrating that previous students encountered and overcame similar challenges, we could help improve students’ sense of belonging. We then perform user studies to evaluate this system’s potential and find that participants rated 70% of the recommended solutions as useful. Our findings suggest an increase in students’ sense of membership and acceptance, and a decrease in the desire to withdraw.

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Cited By

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  • (2024)Learner Modeling and Recommendation of Learning Resources using Personal Knowledge GraphsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636881(273-283)Online publication date: 18-Mar-2024
  • (2023)A Recommendation System for Nurturing Students’ Sense of BelongingArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-36336-8_20(130-135)Online publication date: 30-Jun-2023

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cover image ACM Other conferences
LAK22: LAK22: 12th International Learning Analytics and Knowledge Conference
March 2022
582 pages
ISBN:9781450395731
DOI:10.1145/3506860
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Published: 21 March 2022

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Author Tags

  1. educational recommender systems
  2. semantic similarity
  3. sense of belonging
  4. student success

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Overall Acceptance Rate 236 of 782 submissions, 30%

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View all
  • (2024)Learner Modeling and Recommendation of Learning Resources using Personal Knowledge GraphsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636881(273-283)Online publication date: 18-Mar-2024
  • (2023)A Recommendation System for Nurturing Students’ Sense of BelongingArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-36336-8_20(130-135)Online publication date: 30-Jun-2023

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