Uncertainty-aware Knowledge Tracing (2501.05415v2)

Abstract: Knowledge Tracing (KT) is crucial in education assessment, which focuses on depicting students' learning states and assessing students' mastery of subjects. With the rise of modern online learning platforms, particularly massive open online courses (MOOCs), an abundance of interaction data has greatly advanced the development of the KT technology. Previous research commonly adopts deterministic representation to capture students' knowledge states, which neglects the uncertainty during student interactions and thus fails to model the true knowledge state in learning process. In light of this, we propose an Uncertainty-Aware Knowledge Tracing model (UKT) which employs stochastic distribution embeddings to represent the uncertainty in student interactions, with a Wasserstein self-attention mechanism designed to capture the transition of state distribution in student learning behaviors. Additionally, we introduce the aleatory uncertainty-aware contrastive learning loss, which strengthens the model's robustness towards different types of uncertainties. Extensive experiments on six real-world datasets demonstrate that UKT not only significantly surpasses existing deep learning-based models in KT prediction, but also shows unique advantages in handling the uncertainty of student interactions.

• The paper introduces an uncertainty-aware knowledge tracing model that leverages stochastic embeddings to capture both epistemic and aleatory uncertainties.
• It employs a Wasserstein self-attention mechanism to robustly estimate knowledge state transitions by focusing on global distribution shifts.
• Experimental results across six datasets show superior AUC and accuracy compared to DKT, SAKT, and AKT, highlighting enhanced personalization in learning.

Uncertainty-aware Knowledge Tracing: A Comprehensive Analysis

This paper presents a novel approach to Knowledge Tracing (KT), termed Uncertainty-aware Knowledge Tracing (UKT). The central thesis is to incorporate uncertainty handling in modeling student learning processes through a stochastic representation framework. Traditional KT models primarily rely on deterministic embeddings, underestimating the inherent uncertainties in student interactions. This research introduces stochastic distribution embeddings and a Wasserstein self-attention mechanism to effectively manage the variability and transitions in knowledge states, thus enhancing prediction fidelity.

Theoretical and Methodological Contributions

1. Stochastic Embedding Framework: By employing stochastic distribution embeddings, UKT departs from the conventional deterministic models. It captures the learning dynamics using Gaussian distributions characterized by mean and covariance, which represent the student's base knowledge and inherent uncertainties, respectively. This dual-faceted representation allows UKT to model epistemic uncertainty, accounting for the cognitive differences among learners, and aleatory uncertainty that arises from random events during learning, such as guessing.
2. Wasserstein Self-Attention Mechanism: The use of Wasserstein distance in the self-attention mechanism is a distinguishing feature of this work. This approach not only ensures a robust estimation of knowledge state transitions but also amplifies the model's sensitivity to global distribution shifts rather than pointwise discrepancies—an essential aspect for accurately capturing the learning trajectory over time.
3. Aleatory Uncertainty-aware Contrastive Learning: UKT introduces an innovative contrastive learning loss that fortifies the model against aleatory uncertainty. By generating negative samples simulating lucky guesses and careless mistakes, the model learns to differentiate these random fluctuations from true knowledge variability, thus reducing potential bias in knowledge state estimations.

Empirical Evaluation and Results

The paper reports extensive experimentation across six real-world datasets, demonstrating UKT's superiority over existing deep learning KT models, including the well-regarded DKT, SAKT, and AKT frameworks. Notably, UKT exhibits robust AUC and accuracy performance, reaffirming its capability to handle uncertainties effectively in diverse educational settings. These findings highlight the model's potential for enhanced personalization and adaptability in educational technologies.

Implications and Future Prospects

From a practical standpoint, UKT’s approach to embracing and elucidating uncertainties presents an advancement in educational assessment models. This development has significant implications for online learning platforms aiming to provide individualized learning experiences. Moreover, the incorporation of stochastic processes into KT underscores an emergent trend towards probabilistic modeling frameworks in educational data mining, widening the scope for future exploration in adaptive learning systems.

In future work, exploring the application of UKT in multimodal learning environments could further extend its applicability, accommodating different types of uncertainties arising from a richer data landscape. Additionally, the integration with reinforcement learning paradigms may offer a promising avenue for enhancing learning interventions and feedback mechanisms, ultimately converging on a more coherent theory of adaptive learning support.

In summary, this paper provides a methodological breakthrough in KT by systematically incorporating uncertainty into the learning models, enhancing both theoretical understanding and practical effectiveness in educational assessments. UKT stands as a testament to the potential of sophisticated probabilistic models in reshaping the landscape of intelligent educational systems.