# Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning

arXiv: https://arxiv.org/abs/2605.09369 · HTML: https://mlsanity.com/paper/2605.09369/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 9 of 9)

- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory](https://mlsanity.com/paper/1904.11738/) (2019)
- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [Geometric Relational Embeddings: A Survey](https://mlsanity.com/paper/2304.11949/) (2023)
- [A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations](https://mlsanity.com/paper/2402.01666/) (2024)
- [Automated Knowledge Component Generation for Interpretable Knowledge Tracing in Coding Problems](https://mlsanity.com/paper/2502.18632/) (2025)
- [FlatFormer: A Flat Transformer Knowledge Tracing Model Based on Cognitive Bias Injection](https://mlsanity.com/paper/2512.06629/) (2025)
- [CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models](https://mlsanity.com/paper/2505.17705/) (2025)
- [Probabilistic Hash Embeddings for Online Learning of Categorical Features](https://mlsanity.com/paper/2511.20893/) (2025)

## Abstract (arXiv)

Knowledge Tracing (KT) models students' knowledge states based on learning interactions to predict performance. While deep learning-based KT models have boosted predictive accuracy, most models rely on deterministic vector embeddings and opaque latent state transitions, limiting interpretability regarding how specific past behaviors influence predictions. To address this limitation, we propose Probabilistic Logical Knowledge Tracing (PLKT), an interpretable KT framework that formulates prediction as a goal-conditioned evidence reasoning process over historical learning behaviors. Instead of representing knowledge states as deterministic vector embeddings, PLKT employs robust Beta-distributed probabilistic embeddings to represent student knowledge states. This probabilistic foundation allows us to model the uncertainty of historical behaviors and perform explicit logical operations (e.g., conjunction), constructing transparent reasoning paths that reveal how specific past interactions contribute to the prediction. Extensive experiments show that PLKT outperforms state-of-the-art KT methods while achieving superior interpretability. Our code is available at https://anonymous.4open.science/r/PLKT-D3CE/.