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)|
|Title of host publication||L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|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
|Name||L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale|
|Conference||9th Annual ACM Conference on Learning at Scale, L@S 2022|
|City||New York City|
|Period||6/1/22 → 6/3/22|
Bibliographical noteFunding 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.
© 2022 ACM.
- early warning system (EWS)
- school climate
- socioemotional learning (SEL)
- student information system