DKT2: Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Data (2501.14256v2)

Abstract: Knowledge Tracing (KT) is a fundamental component of Intelligent Tutoring Systems (ITS), enabling the modeling of students' knowledge states to predict future performance. The introduction of Deep Knowledge Tracing (DKT), the first deep learning-based KT (DLKT) model, has brought significant advantages in terms of applicability and comprehensiveness. However, recent DLKT models, such as Attentive Knowledge Tracing (AKT), have often prioritized predictive performance at the expense of these benefits. While deep sequential models like DKT have shown potential, they face challenges related to parallel computing, storage decision modification, and limited storage capacity. To address these limitations, we propose DKT2, a novel KT model that leverages the recently developed xLSTM architecture. DKT2 enhances applicable input representation using the Rasch model and incorporates Item Response Theory (IRT) for output interpretability, allowing for the decomposition of learned knowledge into familiar and unfamiliar knowledge. By integrating this knowledge with predicted questions, DKT2 generates comprehensive knowledge states. Extensive experiments conducted across three large-scale datasets demonstrate that DKT2 consistently outperforms 18 baseline models in various prediction tasks, underscoring its potential for real-world educational applications. This work bridges the gap between theoretical advancements and practical implementation in KT. Our code and datasets are fully available at https://github.com/zyy-2001/DKT2.

• The paper presents DKT2 as an advanced knowledge tracing model that leverages xLSTM for improved computational efficiency and memory stability on large datasets.
• It integrates Rasch embedding and IRT to generate nuanced student skill representations and comprehensive knowledge states.
• Experimental validation shows that DKT2 consistently outperforms 17 baseline models, highlighting its potential for personalized learning in intelligent tutoring systems.

Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Data

The paper "Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Data" proposes an advanced knowledge tracing model, DKT2, which overcomes some limitations present in existing deep learning models for knowledge tracing, particularly in the context of large-scale educational datasets. Knowledge Tracing (KT) is pivotal for Intelligent Tutoring Systems (ITS) as it models a student's knowledge state to predict future performance. This paper emphasizes the necessity of balancing predictive performance with practical applicability, a trade-off often compromised in recent models such as Attentive Knowledge Tracing (AKT).

Overview

DKT2 explores the novel xLSTM architecture to circumvent the computational and capacity challenges faced by earlier models like Deep Knowledge Tracing (DKT). By leveraging advanced techniques such as the Rasch model and Item Response Theory (IRT), DKT2 enhances input representations and introduces interpretability. The model is designed to predict not only whether a student will answer a question correctly but also to provide a comprehensive representation of the student's knowledge states.

Methodological Innovations

1. xLSTM Utilization: At the core of DKT2 is its use of xLSTM, which introduces stabilization and memory improvements over traditional LSTM. The xLSTM's features allow the model to revise storage decisions and improve parallelization, making it apt for processing large datasets effectively.
2. Rasch Embedding and IRT Integration: The model employs the Rasch model to encode questions and student skills, providing a nuanced representation of item difficulties. This is further supported by IRT, which aids in the decomposition of knowledge into familiar and unfamiliar domains, enhancing interpretability.
3. Comprehensive Knowledge State Generation: By integrating learned knowledge with predicted questions, DKT2 builds comprehensive knowledge states, offering a holistic view of the student's progress and potential learning trajectory.

Experimental Validation

The paper provides rigorous experimental results, indicating that DKT2 consistently outperforms 17 baseline models across three large-scale educational datasets. These results underscore the model's robustness in practical educational settings. Notably, DKT2 demonstrates the capability to maintain high performance even as it processes long sequences, which is a significant advancement in the applicability of KT models to real-world, large-scale data.

Implications and Future Directions

The introduction of DKT2 has practical implications, especially for ITS, by enabling more personalized learning experiences based on accurate knowledge state assessments. Theoretically, the model bridges the gap between machine learning capabilities and educational psychology, fostering a more holistic approach to modeling learning progress.

Looking forward, further research might explore the potential of xLSTM-based models in even broader application contexts, possibly integrating more sophisticated psychological models or addressing various types of learning materials and curricula. Additionally, the exploration of multi-concept prediction, as introduced in the paper, highlights an intriguing direction for future research, offering insights into holistic learning assessments and strategy optimizations.

Conclusion

This paper represents a significant step in knowledge tracing research by addressing key predecessor model limitations. The DKT2 model not only exemplifies technical innovation with the introduction of xLSTM but also stresses the necessity for models that are both practically applicable and comprehensive. It stands as a critical contribution to educational data mining and intelligent tutoring systems, emphasizing the alignment of theoretical advancements with practical implementation.