dropout-prediction

This paper proposes a three-stage diagnostic framework to identify why offline model selectors fail to beat the best single model, applying it to dropout prediction on edX clickstream data. The study finds that the bottleneck is local representational ambiguity rather than learner choice or distribution shift, recommending state redesign or new data collection over further algorithm tuning.