Using predictive analytics while ignoring teacher knowledge may misidentify students at risk of dropping out and negatively influence teacher views¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Ignoring the schooling-specific differences in analytics use may both misidentify dropout risk and harm how teachers view flagged students. → James Soland 2015

Evidence¶

James Soland 2015¶

James Soland. (2015). Is Moneyball the next big thing in education? Phi Delta Kappan 96(4), 64–67. https://www.nwea.org/research/publication/is-moneyball-the-next-big-thing-in-education/ doi:10.1177/0031721714561450

q1 · i? · theoretical · r2

The abstract warns, as an argument rather than a finding, that ignoring the differences "may lead not only to misidentifying students at risk of dropping out but also negatively influence how teachers view those students."

"Using predictive analytics in education while ignoring these differences may lead not only to misidentifying students at risk of dropping out but also negatively influence how teachers view those students."

Discussion¶

Related Claims¶

• AI-powered predictive analytics improves academic performance by enabling early identification of at-risk students and timely intervention — the general claim this finding bears on, with the studies behind it
• In descriptive logit models, demographic variables predict dropout at enrollment (e.g., males have a 60% higher chance of dropping out than females), but most lose significance once first-semester performance data is controlled — related
• Evidence is mixed on whether including demographic variables as predictors improves model performance in educational prediction — related
• Including demographic variables as predictors can reduce actionability by de-emphasizing correlated actionable risk factors and by discounting intervention effects — related
• Early-stage performance data is particularly important for predicting attrition, while demographic data has limited predictive value once performance data is available — related
• Predictive learning analytics can generate early warnings of dropout risk and trigger targeted interventions in adult education — related
• Attendance, behavior, and course performance (the ABC indicators) reliably predict which secondary students are at risk of dropping out — related