MAML-KT applies model-agnostic meta-learning to knowledge tracing so models initialize for rapid adaptation, yielding higher early accuracy than standard KT models on ASSIST datasets under controlled cold-start conditions.
A Self-Attentive model for Knowledge Tracing
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abstract
Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are \textit{relevant} to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average.
verdicts
UNVERDICTED 5representative citing papers
SAKT uses self-attention to focus on relevant prior KCs for performance prediction and reports 4.43% average AUC improvement over DKT and DKVMN on real datasets.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
PLKT models student knowledge with Beta probabilistic embeddings and performs explicit logical reasoning over historical interactions to deliver both accurate predictions and interpretable explanations in knowledge tracing.
StanBKT provides a unified Bayesian inference framework for BKT models supporting HMC, variational inference, and hierarchical variants, evaluated on ASSISTments and intervention datasets.
citing papers explorer
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MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning
MAML-KT applies model-agnostic meta-learning to knowledge tracing so models initialize for rapid adaptation, yielding higher early accuracy than standard KT models on ASSIST datasets under controlled cold-start conditions.
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A Self-Attentive model for Knowledge Tracing
SAKT uses self-attention to focus on relevant prior KCs for performance prediction and reports 4.43% average AUC improvement over DKT and DKVMN on real datasets.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
PLKT models student knowledge with Beta probabilistic embeddings and performs explicit logical reasoning over historical interactions to deliver both accurate predictions and interpretable explanations in knowledge tracing.
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StanBKT: Rethinking Parameter Estimation in Bayesian Knowledge Tracing
StanBKT provides a unified Bayesian inference framework for BKT models supporting HMC, variational inference, and hierarchical variants, evaluated on ASSISTments and intervention datasets.