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

arXiv: https://arxiv.org/abs/2101.08349 · HTML: https://mlsanity.com/paper/2101.08349/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 4 of 4)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Survey of Explainable Knowledge Tracing](https://mlsanity.com/paper/2403.07279/) (2024)
- [Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability](https://mlsanity.com/paper/2112.15072/) (2021)
- [Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models](https://mlsanity.com/paper/2203.10170/) (2022)
- [Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation](https://mlsanity.com/paper/2210.15430/) (2022)

## Abstract (arXiv)

Interactive Educational Systems (IES) enabled researchers to trace student knowledge in different skills and provide recommendations for a better learning path. To estimate the student knowledge and further predict their future performance, the interest in utilizing the student interaction data captured by IES to develop learner performance models is increasing rapidly. Moreover, with the advances in computing systems, the amount of data captured by these IES systems is also increasing that enables deep learning models to compete with traditional logistic models and Markov processes. However, it is still not empirically evident if these deep models outperform traditional models on the current scale of datasets with millions of student interactions. In this work, we adopt EdNet, the largest student interaction dataset publicly available in the education domain, to understand how accurately both deep and traditional models predict future student performances. Our work observes that logistic regression models with carefully engineered features outperformed deep models from extensive experimentation. We follow this analysis with interpretation studies based on Locally Interpretable Model-agnostic Explanation (LIME) to understand the impact of various features on best performing model pre-dictions.