“Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time,”
Abstract
Several hundred thousand students drop out of high school every year in the United States. Interventions can help those who are falling behind in their educational goals, but given limited resources, such programs must focus on the right students, at the right time, and with the right message. In this paper, we describe an incremental approach that can be used to select and prioritize students who may be at risk of not graduating high school on time, and to suggest what may be the predictors of particular students going off-track. These predictions can then be used to inform targeted interventions for these students, hopefully leading to better outcomes.
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Metrics at k are more commonly used in ML for public policy to address real-world resource constraints.
“Importantly, we included metrics at a certain percentage of the population (often referred to as metrics at k) because they are more commonly used in ML for public policy in order to specifically addresses real-world resource constraints [2,12,66].”
From It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy (2022) · cites this paper as [2]
Selecting an appropriate set of features and designing explainable models has been important as these later inform intervention.
“Selecting an appropriate set of features and designing explainable models has been important as these later inform intervention (Aguiar et al., 2015).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Aguiar et al., 2015
Cites
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“Recent research has found these diverse causes often manifest themselves through common sets of indicators routinely recorded by schools such as students’ attendance, behavior, and coursework [5, 6].”
BackgroundWhy Students Drop Out of School: A Review of 25 Years of Research
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The Price We Pay: Economic and Social Consequences of Inadequate Education
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School Dropout as Predicted by Peer Rejection and Antisocial Behavior
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Do We Know Whom to Serve? Issues in Using Risk Factors to Identify Dropouts
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Why Tenth Graders Fail to Finish High School: A Dropout Typology Latent Class Analysis
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Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review
Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M. (2022) · IEEE Access · doi:10.1109/access.2022.3179356 · Cited by 185 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
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Explainable AI In Education : Current Trends, Challenges, And Opportunities
Rachha, A., & Seyam, M. (2023) · SoutheastCon 2023 · doi:10.1109/southeastcon51012.2023.10115140 · Cited by 68 · see citing works
Rodolfa, K. T., Lamba, H., & Ghani, R. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00396-x · Cited by 10 · see citing works
- Authors
- Aguiar, E., Lakkaraju, H., Bhanpuri, N., Miller, D., Yuhas, B., & Addison, K. L.
- Affiliations
- University of Notre Dame (US)
- Stanford University (US)
- University of Chicago (US)
- Northwestern University (US)
- Yuhas Group Baltimore, MD
- Montgomery County Public Schools (US)
- Venue
- Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
- Publisher
- ACM
- Published
- 2015-03-16
- Pages
- 93-102
- OpenAlex
- W2034213054
- Cited by
- 52
- References
- 12
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 8 · 2020: 4 · 2019: 5 · 2018: 7 · 2017: 4 · 2016: 1 · 2015: 1
- FWCI
- 3.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Educational Outcomes and Influences
- Social Sciences › Education › Higher Education Research Studies
- Keywords
- high school dropout, at-risk students, student selection, intervention prioritization, predictors of dropout, targeted interventions, graduation on time, incremental method
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