# Multi-granulariy Time-based Transformer for Knowledge Tracing

arXiv: https://arxiv.org/abs/2304.05257 · HTML: https://mlsanity.com/paper/2304.05257/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [LSTM-SAKT: LSTM-Encoded SAKT-like Transformer for Knowledge Tracing](https://mlsanity.com/paper/2102.00845/) (2021)
- [Last Query Transformer RNN for knowledge tracing](https://mlsanity.com/paper/2102.05038/) (2021)
- [Riiid! Answer Correctness Prediction Kaggle Challenge: 4th Place Solution Summary](https://mlsanity.com/paper/2102.04250/) (2021)
- [MUSE: Multi-Scale Temporal Features Evolution for Knowledge Tracing](https://mlsanity.com/paper/2102.00228/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)](https://mlsanity.com/paper/2405.09770/) (2024)

## Abstract (arXiv)

In this paper, we present a transformer architecture for predicting student performance on standardized tests. Specifically, we leverage students historical data, including their past test scores, study habits, and other relevant information, to create a personalized model for each student. We then use these models to predict their future performance on a given test. Applying this model to the RIIID dataset, we demonstrate that using multiple granularities for temporal features as the decoder input significantly improve model performance. Our results also show the effectiveness of our approach, with substantial improvements over the LightGBM method. Our work contributes to the growing field of AI in education, providing a scalable and accurate tool for predicting student outcomes.