# GritNet: Student Performance Prediction with Deep Learning

arXiv: https://arxiv.org/abs/1804.07405 · HTML: https://mlsanity.com/paper/1804.07405/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Time Series Analysis for Education: Methods, Applications, and Future Directions](https://mlsanity.com/paper/2408.13960/) (2024)
- [Augmenting deep neural networks with symbolic knowledge: Towards trustworthy and interpretable AI for education](https://mlsanity.com/paper/2311.00393/) (2023)
- [Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails](https://mlsanity.com/paper/2110.08268/) (2021)
- [Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance Prediction](https://mlsanity.com/paper/2107.10424/) (2021)
- [Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis](https://mlsanity.com/paper/2207.11776/) (2022)
- [DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction](https://mlsanity.com/paper/2302.11569/) (2023)
- [Deep Learning for Educational Data Science](https://mlsanity.com/paper/2404.19675/) (2024)
- [Students Success Modeling: Most Important Factors](https://mlsanity.com/paper/2309.13052/) (2023)
- [Trustworthy Intelligent Education: A Systematic Perspective on Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2601.21837/) (2026)

## Abstract (arXiv)

Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to facilitate timely educational interventions during a course. However, very few prior studies have explored this problem from a deep learning perspective. In this paper, we recast the student performance prediction problem as a sequential event prediction problem and propose a new deep learning based algorithm, termed GritNet, which builds upon the bidirectional long short term memory (BLSTM). Our results, from real Udacity students' graduation predictions, show that the GritNet not only consistently outperforms the standard logistic-regression based method, but that improvements are substantially pronounced in the first few weeks when accurate predictions are most challenging.