Deep Knowledge Tracking Based on Exercise Semantic Information

Xiao, Rong; Zheng, Ruili; Xiao, Yunhua; Zhang, Yinghui; Sun, Bo; He, Jun

doi:10.1007/978-3-030-92836-0_24

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…[59], [41], [ [20], [74], [34], [36], [75], [37], [38], [39], [76] [27], [43], [44], [52], [45], [68], [54], [37], [47], [46] [69], [55], [56], [57], [58], [59], [60], [61], [62], [50] [34], [63], [59] [20], [42], [77], [79] Hypothesis set [71], [33], [38], [78], [47], [49] [53], [72], [51] [71], [26], [48], [33], [49], [40] [48], [13], [70], [41], [64],…”

Section: Domain Knowledgementioning

confidence: 99%

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Zanellati,

Mitri,

Gabbrielli

et al. 2024

IEEE Trans. Learning Technol.

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Knowledge Tracing is a well-known problem in AI for Education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their satisfactory performances, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate the state of the art in this field. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.

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Section: Domain Knowledgementioning

confidence: 99%

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Zanellati,

Mitri,

Gabbrielli

et al. 2024

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

show abstract

Section: Domain Knowledgementioning

confidence: 99%

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

Zanellati¹,

Mitri²,

Gabbrielli³

et al. 2023

Preprint

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<p>Knowledge Tracing is a well-known problem often tackled through purely data-driven approaches. In recent years, many advances have been made in research with various machine learning and deep learning techniques. Despite their satisfactory performance, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate this field's state of the art. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.</p>

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“…The DKT-LCIRT model is an improvement on the Dynamic Key-Value Memory Network model (DKVMN) [8,9] by adding learning capability features to the input layer of the neural network [10,11] and introducing item response theory to the output layer of the neural network [12]. Feature engineering is a crucial part of deep learning models, and features determine the upper limit of deep learning models, and by constructing new features, more information contained in the data can be mined [13,14], making the data further expressive [15][16][17]. The DKT-LCIRT model extracts potential features from the sequences of students' historical interactions [18,19], and then uses deep learning neural networks to track the student's knowledge state, output student capability parameter, and exercise difficulty parameter.…”

Section: Introductionmentioning

confidence: 99%

DKT-LCIRT: A Deep Knowledge Tracking Model Integrating Learning Capability and Item Response Theory

Shuai²,

Hu³

et al. 2022

Electronics

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In the realm of intelligent education, knowledge tracking is a critical study topic. Deep learning-based knowledge tracking models have better predictive performance compared to traditional knowledge tracking models, but the models are less interpretable and also often ignore the intrinsic differences among students (e.g., learning capability, guessing capability, etc.), resulting in a lack of personalization of predictive results. To further reflect the personalized differences among students and enhance the interpretability of the model at the same time, a Deep Knowledge Tracking model integrating Learning Capability and Item Response Theory (DKT-LCIRT) is proposed. The model dynamically calculates students’ learning capability by each time interval and allocates each student to groups with similar learning capabilities to increase the predictive performance of the model. Furthermore, the model introduces item response theory to enhance the interpretability of the model. Substantial experiments on four real datasets were carried out, and the experimental results showed that the DKT-LCIRT model improved the AUC by 3% and the ACC by 2% compared to other models. The results confirmed that the DKT-LCIRT model outperformed other classical models in terms of predictive performance, fully reflecting students’ individualization and adding a more meaningful interpretation to the model.

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Deep Knowledge Tracking Based on Exercise Semantic Information

Cited by 5 publications

References 8 publications

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

DKT-LCIRT: A Deep Knowledge Tracking Model Integrating Learning Capability and Item Response Theory

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