Abstract
The Every Student Succeeds Act (ESSA) prescribes holistic measures of schools for student success and well-being. However, many early warning systems rely exclusively on the "Attendance, Behavior, Course"(ABC) taxonomy, which misses potentially crucial determinants such as school climate and students' socioemotional learning. We report early findings from a larger project that aims to apply machine learning methods to improve an early warning system by incorporating factors related to school climate and socioemotional learning. These preliminary analyses suggest that a culturally inclusive, socially supportive, and emotionally and physically safe school climate is related to academic success and fewer engagement/behavior problems at the school level. They suggest the promise of integrating these features into early warning systems to help schools change their practices to better support student well-being.
Original language | English (US) |
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Title of host publication | L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale |
Publisher | Association for Computing Machinery, Inc |
Pages | 405-408 |
Number of pages | 4 |
ISBN (Electronic) | 9781450391580 |
DOIs | |
State | Published - Jun 1 2022 |
Event | 9th Annual ACM Conference on Learning at Scale, L@S 2022 - New York City, United States Duration: Jun 1 2022 → Jun 3 2022 |
Publication series
Name | L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale |
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Conference
Conference | 9th Annual ACM Conference on Learning at Scale, L@S 2022 |
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Country/Territory | United States |
City | New York City |
Period | 6/1/22 → 6/3/22 |
Bibliographical note
Funding Information:This project is funded by Schmidt Futures. We offer a special thanks to the Nevada Department of Education and American Institutes for Research (AIR) for data preparation, early partnership, and feedback on this project.
Publisher Copyright:
© 2022 ACM.
Keywords
- early warning system (EWS)
- school climate
- socioemotional learning (SEL)
- student information system