# DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills

arXiv: https://arxiv.org/abs/1905.06873 · HTML: https://mlsanity.com/paper/1905.06873/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 27 of 27)

- [pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models](https://mlsanity.com/paper/2206.11460/) (2022)
- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [Option Tracing: Beyond Correctness Analysis in Knowledge Tracing](https://mlsanity.com/paper/2104.09043/) (2021)
- [Privacy-Preserving Synthetic Educational Data Generation](https://mlsanity.com/paper/2207.03202/) (2022)
- [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)
- [Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts](https://mlsanity.com/paper/2308.03377/) (2023)
- [GPT-based Open-Ended Knowledge Tracing](https://mlsanity.com/paper/2203.03716/) (2022)
- [No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths](https://mlsanity.com/paper/2308.03488/) (2023)
- [Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency](https://mlsanity.com/paper/2311.08594/) (2023)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)
- [Interpreting Latent Student Knowledge Representations in Programming Assignments](https://mlsanity.com/paper/2405.08213/) (2024)
- [Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information](https://mlsanity.com/paper/2409.20167/) (2024)
- [Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models](https://mlsanity.com/paper/2203.10170/) (2022)
- [A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability](https://mlsanity.com/paper/2302.08673/) (2023)
- [Evolvable Psychology Informed Neural Network for Memory Behavior Modeling](https://mlsanity.com/paper/2408.14492/) (2024)
- [Transferable Student Performance Modeling for Intelligent Tutoring Systems](https://mlsanity.com/paper/2202.03980/) (2022)
- [Prerequisite-driven Q-matrix Refinement for Learner Knowledge Assessment: A Case Study in Online Learning Context](https://mlsanity.com/paper/2208.12642/) (2022)
- [Generating News-Centric Crossword Puzzles As A Constraint Satisfaction and Optimization Problem](https://mlsanity.com/paper/2308.04688/) (2023)
- [Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model](https://mlsanity.com/paper/2309.12334/) (2023)
- [Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning](https://mlsanity.com/paper/2405.20079/) (2024)
- [Advancing Knowledge Tracing by Exploring Follow-up Performance Trends](https://mlsanity.com/paper/2508.08019/) (2025)
- [MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing](https://mlsanity.com/paper/2605.08697/) (2026)
- [Interpretable Memory Models for Spaced Repetition](https://mlsanity.com/paper/2610.10548/) (2026)

## Abstract (arXiv)

Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just a generic scheduler. However, the applicability of such adaptive schedulers seems to be limited to pure memorization, e.g. flashcards or foreign language learning. In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills. To this end, we choose to rely on a student model for inferring knowledge state and memory dynamics on any skill or combination of skills. We argue that no knowledge tracing model takes both memory decay and multiple skill tagging into account for predicting student performance. As a consequence, we propose a new student learning and forgetting model suited to our research problem: DAS3H builds on the additive factor models and includes a representation of the temporal distribution of past practice on the skills involved by an item. In particular, DAS3H allows the learning and forgetting curves to differ from one skill to another. Finally, we provide empirical evidence on three real-world educational datasets that DAS3H outperforms other state-of-the-art EDM models. These results suggest that incorporating both item-skill relationships and forgetting effect improves over student models that consider one or the other.