31,938 research outputs found
Knowledge Tracing Challenge: Optimal Activity Sequencing for Students
Knowledge tracing is a method used in education to assess and track the
acquisition of knowledge by individual learners. It involves using a variety of
techniques, such as quizzes, tests, and other forms of assessment, to determine
what a learner knows and does not know about a particular subject. The goal of
knowledge tracing is to identify gaps in understanding and provide targeted
instruction to help learners improve their understanding and retention of
material. This can be particularly useful in situations where learners are
working at their own pace, such as in online learning environments. By
providing regular feedback and adjusting instruction based on individual needs,
knowledge tracing can help learners make more efficient progress and achieve
better outcomes. Effectively solving the KT problem would unlock the potential
of computer-aided education applications such as intelligent tutoring systems,
curriculum learning, and learning materials recommendations. In this paper, we
will present the results of the implementation of two Knowledge Tracing
algorithms on a newly released dataset as part of the AAAI2023 Global Knowledge
Tracing Challenge
Forgetting-aware Linear Bias for Attentive Knowledge Tracing
Knowledge Tracing (KT) aims to track proficiency based on a question-solving
history, allowing us to offer a streamlined curriculum. Recent studies actively
utilize attention-based mechanisms to capture the correlation between questions
and combine it with the learner's characteristics for responses. However, our
empirical study shows that existing attention-based KT models neglect the
learner's forgetting behavior, especially as the interaction history becomes
longer. This problem arises from the bias that overprioritizes the correlation
of questions while inadvertently ignoring the impact of forgetting behavior.
This paper proposes a simple-yet-effective solution, namely Forgetting-aware
Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite
its simplicity, FoLiBi is readily equipped with existing attentive KT models by
effectively decomposing question correlations with forgetting behavior. FoLiBi
plugged with several KT models yields a consistent improvement of up to 2.58%
in AUC over state-of-the-art KT models on four benchmark datasets.Comment: In Proceedings of the 32nd ACM International Conference on
Information and Knowledge Management (CIKM'23), 5 pages, 3 figures, 2 table
Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing
Recently, knowledge tracing models have been applied in educational data
mining such as the Self-attention knowledge tracing model(SAKT), which models
the relationship between exercises and Knowledge concepts(Kcs). However,
relation modeling in traditional Knowledge tracing models only considers the
static question-knowledge relationship and knowledge-knowledge relationship and
treats these relationships with equal importance. This kind of relation
modeling is difficult to avoid the influence of subjective labeling and
considers the relationship between exercises and KCs, or KCs and KCs
separately. In this work, a novel knowledge tracing model, named Knowledge
Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural
Graph Forgetting Knowledge Tracing(NGFKT), is proposed to reduce the impact of
the subjective labeling by calibrating the skill relation matrix and the
Q-matrix and apply the Graph Convolutional Network(GCN) to model the
heterogeneous interactions between students, exercises, and skills.
Specifically, the skill relation matrix and Q-matrix are generated by the
Knowledge Relation Importance Rank Calibration method(KRIRC). Then the
calibrated skill relation matrix, Q-matrix, and the heterogeneous interactions
are treated as the input of the GCN to generate the exercise embedding and
skill embedding. Next, the exercise embedding, skill embedding, item
difficulty, and contingency table are incorporated to generate an exercise
relation matrix as the inputs of the Position-Relation-Forgetting attention
mechanism. Finally, the Position-Relation-Forgetting attention mechanism is
applied to make the predictions. Experiments are conducted on the two public
educational datasets and results indicate that the NGFKT model outperforms all
baseline models in terms of AUC, ACC, and Performance Stability(PS).Comment: 11 pages, 3 figure
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction
This paper presents novel techniques for enhancing the performance of
knowledge tracing (KT) models by focusing on the crucial factor of question and
concept difficulty level. Despite the acknowledged significance of difficulty,
previous KT research has yet to exploit its potential for model optimization
and has struggled to predict difficulty from unseen data. To address these
problems, we propose a difficulty-centered contrastive learning method for KT
models and a Large Language Model (LLM)-based framework for difficulty
prediction. These innovative methods seek to improve the performance of KT
models and provide accurate difficulty estimates for unseen data. Our ablation
study demonstrates the efficacy of these techniques by demonstrating enhanced
KT model performance. Nonetheless, the complex relationship between language
and difficulty merits further investigation.Comment: 10 pages, 4 figures, 2 table
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