# Dual-State Personalized Knowledge Tracing with Emotional Incorporation

arXiv: https://arxiv.org/abs/2405.16799 · HTML: https://mlsanity.com/paper/2405.16799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 74 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 19 of 19)

- [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)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [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)
- [Knowledge Tracing with Sequential Key-Value Memory Networks](https://mlsanity.com/paper/1910.13197/) (2019)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [Exploiting Cognitive Structure for Adaptive Learning](https://mlsanity.com/paper/1905.12470/) (2019)
- [Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations](https://mlsanity.com/paper/2302.06885/) (2023)
- [Engaging with Massive Online Courses](https://mlsanity.com/paper/1403.3100/) (2014)
- [Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing](https://mlsanity.com/paper/2108.08105/) (2021)
- [Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis](https://mlsanity.com/paper/2210.08169/) (2022)
- [Adaptive Learning Material Recommendation in Online Language Education](https://mlsanity.com/paper/1905.10893/) (2019)

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

- [Benchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset](https://mlsanity.com/paper/2607.10238/) (2026)

## Abstract (arXiv)

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learning-based Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance.