Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?
This work addresses the need for explainable and accurate knowledge tracing models in educational big data, though it appears incremental as it focuses on ensembling existing methods.
The authors tackled the problem of predicting student performance in the 2020 NeurIPS Education Challenge by combining 22 models, achieving higher accuracy than any individual model and improving explainability, alignment with learning theories, and predictive power.
In this paper, we describe our Knowledge Tracing model for the 2020 NeurIPS Education Challenge. We used a combination of 22 models to predict whether the students will answer a given question correctly or not. Our combination of different approaches allowed us to get an accuracy higher than any of the individual models, and the variation of our model types gave our solution better explainability, more alignment with learning science theories, and high predictive power.