<ul><li>Educational stakeholders are interested in sparse, delayed student outcomes like end-of-year statewide exams.</li><li>Prior work has focused on using long-term usage data to predict outcomes, but this study investigates using short-term log data to predict students' end-of-school year assessments.</li><li>The study utilizes datasets from students in Uganda using a literacy game product and students in the US using two mathematics tutoring systems.</li><li>Findings suggest that 2-5 hours of log usage data can provide valuable insight into students' long-term performance.</li></ul>

Predicting Long-Term Student Outcomes from Short-Term EdTech Log Data

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