Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues
Early development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2021.728801/full |
| _version_ | 1818354596684234752 |
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| author | Anirudh Som Sujeong Kim Bladimir Lopez-Prado Svati Dhamija Nonye Alozie Amir Tamrakar |
| author_facet | Anirudh Som Sujeong Kim Bladimir Lopez-Prado Svati Dhamija Nonye Alozie Amir Tamrakar |
| author_sort | Anirudh Som |
| collection | DOAJ |
| description | Early development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most common approach, adopted by teachers to instill collaborative skills. However, it is difficult for a single teacher to observe multiple student groups and provide constructive feedback to each student. With growing student population and limited teaching staff, this problem seems unlikely to go away. Development of machine-learning-based automated systems for student group collaboration assessment and feedback can help address this problem. Building upon our previous work, in this paper, we propose simple CNN deep-learning models that take in spatio-temporal representations of individual student roles and behavior annotations as input for group collaboration assessment. The trained classification models are further used to develop an automated recommendation system to provide individual-level or group-level feedback. The recommendation system suggests different roles each student in the group could have assumed that would facilitate better overall group collaboration. To the best of our knowledge, we are the first to develop such a feedback system. We also list the different challenges faced when working with the annotation data and describe the approaches we used to address those challenges. |
| first_indexed | 2024-12-13T19:27:56Z |
| format | Article |
| id | doaj.art-ca226701d97b4a7da72f383f024eadcd |
| institution | Directory Open Access Journal |
| issn | 2624-9898 |
| language | English |
| last_indexed | 2024-12-13T19:27:56Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj.art-ca226701d97b4a7da72f383f024eadcd2022-12-21T23:33:59ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982021-10-01310.3389/fcomp.2021.728801728801Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral CuesAnirudh Som0Sujeong Kim1Bladimir Lopez-Prado2Svati Dhamija3Nonye Alozie4Amir Tamrakar5Center for Vision Technologies, SRI International, Menlo Park, CA, United StatesCenter for Vision Technologies, SRI International, Menlo Park, CA, United StatesCenter for Education Research and Innovation, SRI International, Menlo Park, CA, United StatesCenter for Vision Technologies, SRI International, Menlo Park, CA, United StatesCenter for Education Research and Innovation, SRI International, Menlo Park, CA, United StatesCenter for Vision Technologies, SRI International, Menlo Park, CA, United StatesEarly development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most common approach, adopted by teachers to instill collaborative skills. However, it is difficult for a single teacher to observe multiple student groups and provide constructive feedback to each student. With growing student population and limited teaching staff, this problem seems unlikely to go away. Development of machine-learning-based automated systems for student group collaboration assessment and feedback can help address this problem. Building upon our previous work, in this paper, we propose simple CNN deep-learning models that take in spatio-temporal representations of individual student roles and behavior annotations as input for group collaboration assessment. The trained classification models are further used to develop an automated recommendation system to provide individual-level or group-level feedback. The recommendation system suggests different roles each student in the group could have assumed that would facilitate better overall group collaboration. To the best of our knowledge, we are the first to develop such a feedback system. We also list the different challenges faced when working with the annotation data and describe the approaches we used to address those challenges.https://www.frontiersin.org/articles/10.3389/fcomp.2021.728801/fullk-12educationcollaboration assessmentfeedback systemdeep learningmachine learning |
| spellingShingle | Anirudh Som Sujeong Kim Bladimir Lopez-Prado Svati Dhamija Nonye Alozie Amir Tamrakar Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues Frontiers in Computer Science k-12 education collaboration assessment feedback system deep learning machine learning |
| title | Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues |
| title_full | Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues |
| title_fullStr | Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues |
| title_full_unstemmed | Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues |
| title_short | Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues |
| title_sort | automated student group collaboration assessment and recommendation system using individual role and behavioral cues |
| topic | k-12 education collaboration assessment feedback system deep learning machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2021.728801/full |
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