# SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

arXiv: https://arxiv.org/abs/2010.12042 · HTML: https://mlsanity.com/paper/2010.12042/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 30 of 57)

- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models](https://mlsanity.com/paper/2206.11460/) (2022)
- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [A Survey of Knowledge Tracing: Models, Variants, and Applications](https://mlsanity.com/paper/2105.15106/) (2021)
- [Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing](https://mlsanity.com/paper/2201.09020/) (2022)
- [Question Generation for Adaptive Education](https://mlsanity.com/paper/2106.04262/) (2021)
- [Adaptive and Personalized Exercise Generation for Online Language Learning](https://mlsanity.com/paper/2306.02457/) (2023)
- [Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing](https://mlsanity.com/paper/2108.08105/) (2021)
- [Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks](https://mlsanity.com/paper/2206.03545/) (2022)
- [Forgetting-aware Linear Bias for Attentive Knowledge Tracing](https://mlsanity.com/paper/2309.14796/) (2023)
- [Predictive, scalable and interpretable knowledge tracing on structured domains](https://mlsanity.com/paper/2403.13179/) (2024)
- [ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback](https://mlsanity.com/paper/2107.14035/) (2021)
- [MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing](https://mlsanity.com/paper/2208.12615/) (2022)
- [Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks](https://mlsanity.com/paper/2410.10829/) (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)
- [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)
- [Automated Knowledge Component Generation for Interpretable Knowledge Tracing in Coding Problems](https://mlsanity.com/paper/2502.18632/) (2025)
- [Riiid! Answer Correctness Prediction Kaggle Challenge: 4th Place Solution Summary](https://mlsanity.com/paper/2102.04250/) (2021)
- [LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences](https://mlsanity.com/paper/2105.06266/) (2021)
- [Large Language Model Augmented Exercise Retrieval for Personalized Language Learning](https://mlsanity.com/paper/2402.16877/) (2024)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)
- [MUSE: Multi-Scale Temporal Features Evolution for Knowledge Tracing](https://mlsanity.com/paper/2102.00228/) (2021)
- [GPT-based Open-Ended Knowledge Tracing](https://mlsanity.com/paper/2203.03716/) (2022)
- [Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing](https://mlsanity.com/paper/2310.01180/) (2023)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)
- [An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing](https://mlsanity.com/paper/2111.04497/) (2021)
- [KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students](https://mlsanity.com/paper/2402.12291/) (2024)
- [Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems](https://mlsanity.com/paper/2405.05136/) (2024)
- [Interpreting Latent Student Knowledge Representations in Programming Assignments](https://mlsanity.com/paper/2405.08213/) (2024)

## Abstract (arXiv)

We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Following the architecture of SAINT, SAINT+ has an encoder-decoder structure where the encoder applies self-attention layers to a stream of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to streams of response embeddings and encoder output. Moreover, SAINT+ incorporates two temporal feature embeddings into the response embeddings: elapsed time, the time taken for a student to answer, and lag time, the time interval between adjacent learning activities. We empirically evaluate the effectiveness of SAINT+ on EdNet, the largest publicly available benchmark dataset in the education domain. Experimental results show that SAINT+ achieves state-of-the-art performance in knowledge tracing with an improvement of 1.25% in area under receiver operating characteristic curve compared to SAINT, the current state-of-the-art model in EdNet dataset.