Dual-State Personalized Knowledge Tracing with Emotional Incorporation

TL;DR

This paper introduces a novel knowledge tracing model that incorporates students' emotional states to improve personalized learning predictions, achieving state-of-the-art results across datasets.

Contribution

The paper presents a dual-state model that explicitly integrates emotional information into knowledge tracing, enhancing personalization and prediction accuracy.

Findings

Achieves state-of-the-art performance on multiple datasets.

Effectively models personalized emotional states.

Improves response prediction accuracy.

Abstract

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.