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Interactive Educational Systems (IES) enabled researchers to trace studentknowledge in different skills and provide recommendations for a better learningpath. To estimate the student knowledge and further predict their futureperformance, the interest in utilizing the student interaction data captured byIES to develop learner performance models is increasing rapidly. Moreover, withthe advances in computing systems, the amount of data captured by these IESsystems is also increasing that enables deep learning models to compete withtraditional logistic models and Markov processes. However, it is still notempirically evident if these deep models outperform traditional models on thecurrent scale of datasets with millions of student interactions. In this work,we adopt EdNet, the largest student interaction dataset publicly available inthe education domain, to understand how accurately both deep and traditionalmodels predict future student performances. Our work observes that logisticregression models with carefully engineered features outperformed deep modelsfrom extensive experimentation. We follow this analysis with interpretationstudies based on Locally Interpretable Model-agnostic Explanation (LIME) tounderstand the impact of various features on best performing modelpre-dictions.

Do we need to go Deep? Knowledge Tracing with Big Data