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Re-envisioning a K-12 Early Warning System with School Climate Factors

Published: 01 June 2022 Publication History

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.

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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. In this video presentation, we report background and 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. 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. The study suggests the promise of integrating these features into early warning systems to help schools change their practices to better support student well-being.

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  • (2024)Investigação da Evasão Estudantil por meio da Mineração de Dados e Aprendizagem de Máquina: Um Mapeamento SistemáticoRevista Brasileira de Informática na Educação10.5753/rbie.2024.346632(807-841)Online publication date: 10-Mar-2024

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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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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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. early warning system (EWS)
  2. school climate
  3. socioemotional learning (SEL)
  4. student information system

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  • Short-paper

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  • Schmidt Futures

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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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Overall Acceptance Rate 117 of 440 submissions, 27%

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View all
  • (2024)Investigação da Evasão Estudantil por meio da Mineração de Dados e Aprendizagem de Máquina: Um Mapeamento SistemáticoRevista Brasileira de Informática na Educação10.5753/rbie.2024.346632(807-841)Online publication date: 10-Mar-2024

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