A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

Hilda, Ebedia; Hidayah, Indriana; Kusumawardani, Sri Suning

doi:10.25126/jitecs.202162344

Cited by 4 publications

(3 citation statements)

References 33 publications

(46 reference statements)

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Section: Experiments and Resultsmentioning

confidence: 99%

“…In our experiment, we utilized three publicly available real-world datasets in the field of knowledge tracking: the ASSISTments2009 dataset [39], the ASSISTments 2012 dataset [40], and the EdNet dataset [41]. These datasets are widely recognized as popular temporal datasets that consist of interaction logs between students and real adaptive learning systems, such as Carnegie Learning's Cognitive Tutor [42] and the ASSISTment system [40].…”

Section: Datasetsmentioning

confidence: 99%

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GELT: A graph embeddings based lite-transformer for knowledge tracing

Liang,

Wu,

Liang

et al. 2024

PLoS ONE

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The development of intelligent education has led to the emergence of knowledge tracing as a fundamental task in the learning process. Traditionally, the knowledge state of each student has been determined by assessing their performance in previous learning activities. In recent years, Deep Learning approaches have shown promising results in capturing complex representations of human learning activities. However, the interpretability of these models is often compromised due to the end-to-end training strategy they employ. To address this challenge, we draw inspiration from advancements in graph neural networks and propose a novel model called GELT (Graph Embeddings based Lite-Transformer). The purpose of this model is to uncover and understand the relationships between skills and questions. Additionally, we introduce an energy-saving attention mechanism for predicting knowledge states that is both simple and effective. This approach maintains high prediction accuracy while significantly reducing computational costs compared to conventional attention mechanisms. Extensive experimental results demonstrate the superior performance of our proposed model compared to other state-of-the-art baselines on three publicly available real-world datasets for knowledge tracking.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

GELT: A graph embeddings based lite-transformer for knowledge tracing

Liang,

Wu,

Liang

et al. 2024

PLoS ONE

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“…There are multiple techniques available for modeling learners' understanding and learning progress. Among the widely recognized learner models, knowledge tracing particularly stands out based on its ability to trace learners' knowledge and predict their knowledge states [6][7][8]. Within adaptive learning systems, it is essential to construct a knowledge-tracing model for all learners interacting with the system, representing their proficiency levels in all skills taught in the course [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Introduction to Bayesian Knowledge Tracing with pyBKT

Bulut

Shin

Yildirim-Erbasli

et al. 2023

Psych

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This study aims to introduce Bayesian Knowledge Tracing (BKT), a probabilistic model used in educational data mining to estimate learners’ knowledge states over time. It also provides a practical guide to estimating BKT models using the pyBKT library available in Python. The first section presents an overview of BKT by explaining its theoretical foundations and advantages in modeling individual learning processes. In the second section, we describe different variants of the standard BKT model based on item response theory (IRT). Next, we demonstrate the estimation of BKT with the pyBKT library in Python, outlining data pre-processing steps, parameter estimation, and model evaluation. Different cases of knowledge tracing tasks illustrate how BKT estimates learners’ knowledge states and evaluates prediction accuracy. The results highlight the utility of BKT in capturing learners’ knowledge states dynamically. We also show that the model parameters of BKT resemble the parameters from logistic IRT models.

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Intelligent techniques in e-learning: a literature review

Ilić¹,

Mikić²,

Kopanja³

et al. 2023

Artif Intell Rev

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Online learning has become increasingly important, having in mind the latest events, imposed isolation measures and closed schools and campuses. Consequently, teachers and students need to embrace digital tools and platforms, bridge the newly established physical gap between them, and consume education in various new ways. Although literature indicates that the development of intelligent techniques must be incorporated in e-learning systems to make them more effective, the need exists for research on how these techniques impact the whole process of online learning, and how they affect learners’ performance. This paper aims to provide comprehensive research on innovations in e-learning, and present a literature review of used intelligent techniques and explore their potential benefits. This research presents a categorization of intelligent techniques, and explores their roles in e-learning environments. By summarizing the state of the art in the area, the authors outline past research, highlight its gaps, and indicate important implications for practice. The goal is to understand better available intelligent techniques, their implementation and application in e-learning context, and their impact on improving learning in online education. Finally, the review concludes that AI-supported solutions not only can support learner and teacher, by recommending resources and grading submissions, but they can offer fully personalized learning experience.

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A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

Cited by 4 publications

References 33 publications

GELT: A graph embeddings based lite-transformer for knowledge tracing

GELT: A graph embeddings based lite-transformer for knowledge tracing

An Introduction to Bayesian Knowledge Tracing with pyBKT

Intelligent techniques in e-learning: a literature review

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