Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?

TL;DR

This paper presents an ensemble of 22 models for knowledge tracing in education, achieving high accuracy, better explainability, and alignment with learning theories in predicting student responses.

Contribution

It introduces a novel ensemble approach combining diverse models to improve accuracy and explainability in knowledge tracing for educational data.

Findings

Ensemble model outperforms individual models in accuracy.

Combining diverse models enhances explainability and theoretical alignment.

Achieved top performance in the 2020 NeurIPS Education Challenge.

Abstract

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.