# Enhancing knowledge tracing robustness for new question cold start in Intelligent Tutoring Systems

arXiv: https://arxiv.org/abs/2512.07179 · HTML: https://mlsanity.com/paper/2512.07179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 78 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://mlsanity.com/paper/2202.13669/) (2022)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [Recurrent Neural Networks (RNNs): A gentle Introduction and Overview](https://mlsanity.com/paper/1912.05911/) (2019)
- [A Survey on Optical Character Recognition System](https://mlsanity.com/paper/1710.05703/) (2017)
- [Generative AI and Its Impact on Personalized Intelligent Tutoring Systems](https://mlsanity.com/paper/2410.10650/) (2024)
- [SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model](https://mlsanity.com/paper/2407.01245/) (2024)
- [Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments](https://mlsanity.com/paper/1812.09628/) (2018)
- [Enhancing Knowledge Tracing with Concept Map and Response Disentanglement](https://mlsanity.com/paper/2408.12996/) (2024)
- [Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information](https://mlsanity.com/paper/2409.20167/) (2024)
- [Knowledge Tracing in Programming Education Integrating Students' Questions](https://mlsanity.com/paper/2502.10408/) (2025)

## Abstract (arXiv)

Intelligent Tutoring Systems (ITS) provide personalized learning paths by diagnosing learners' proficiency. Knowledge Tracing (KT) models play a central role in this diagnosis by estimating learners' evolving knowledge states. However, in real-world ITS services, diagnostic reliability may decrease when newly introduced questions have no prior interaction history. This study aims to empirically identify the key features that support KT model robustness under the question cold start situation. To this end, we designed Practical Integrated Cross-consistent Knowledge Tracing (PICKT), which integrates multiple types of features, and examined which feature contributes to robustness under the question cold start situation. Among these features, we further analyzed difficulty, texts, and relational information derived from the knowledge map, which have been identified as important factors in prior KT research, to examine how each contributes to robustness under the question cold start situation. The results showed that difficulty feature was particularly informative for highly challenging questions, where the likelihood of a correct response rate was remarkably low. Meanwhile, the fused texts and knowledge map features supported robustness by enabling the model to estimate the representation of unseen questions by leveraging semantically and structurally similar questions observed during training. These findings suggest that maintaining KT robustness in ITS applications requires prioritization of feature annotation aligned with the characteristics of educational services.