Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
The traditional focus of data-driven decision-making has been business applications. Other domains, like education, also have significant potential. For instance, various factors impact the likelihood of successfully graduating; advance identification of students "at risk" of not graduating allows administrators to intervene, increasing graduation likelihood. This Research-Practice Partnership applies Machine Learning (ML), including aspects of Fairness and Explainability, to identify High School students at risk of not graduating. We show that ML approaches can successfully predict such students, while Explainable ML techniques can shed light on the factors that contribute most to a reduced likelihood of graduation. With this information, school counselors can efficiently identify roadblocks and also follow up with "grey zone" students, i.e., students at risk of not graduating but who do not follow typical non-graduation patterns (and might not be on the radar of counselors).
Recommended Citation
Kelberlau, Darin; Agarwal, Sonia; Grimaldo, Jorge; Hall, Margeret; and Haas, Christian, "Utilizing Fair and Explainable Machine Learning to Analyze High School Graduation Likelihood" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/practitioner_insights/2
Utilizing Fair and Explainable Machine Learning to Analyze High School Graduation Likelihood
Hilton Waikoloa Village, Hawaii
The traditional focus of data-driven decision-making has been business applications. Other domains, like education, also have significant potential. For instance, various factors impact the likelihood of successfully graduating; advance identification of students "at risk" of not graduating allows administrators to intervene, increasing graduation likelihood. This Research-Practice Partnership applies Machine Learning (ML), including aspects of Fairness and Explainability, to identify High School students at risk of not graduating. We show that ML approaches can successfully predict such students, while Explainable ML techniques can shed light on the factors that contribute most to a reduced likelihood of graduation. With this information, school counselors can efficiently identify roadblocks and also follow up with "grey zone" students, i.e., students at risk of not graduating but who do not follow typical non-graduation patterns (and might not be on the radar of counselors).
https://aisel.aisnet.org/hicss-58/da/practitioner_insights/2