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

Li, Guangquan; Shuai, Junkai; Hu, Yuqing; Zhang, Yonghong; Wang, Yinglong; Yang, Tonghua; Xiong, Naixue

doi:10.3390/electronics11203364

Cited by 5 publications

(4 citation statements)

References 41 publications

(44 reference statements)

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“…-The DKT [17] model is a RNN or LSTM based neural networks to model the interactive process of student question answering for prediction.…”

Section: Methodsmentioning

confidence: 99%

“…This framework combines neural networks to learn the complex interaction between students and test question vectors and uses the monotonicity hypothesis and Q matrix of educational psychology for reference to ensure the interpretability of students' ability model. DKT-LCIRT [17] emphasized on reflect intrinsic difference between students by kinds of capability vectoring, therefore the model can articulately present interpretability.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Pei,

Raga Jr.,

Abisado

2023

Int. J. Adv. Intell. Informatics

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Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences.

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“…-The DKT [17] model is a RNN or LSTM based neural networks to model the interactive process of student question answering for prediction.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Pei,

Raga Jr.,

Abisado

2023

Int. J. Adv. Intell. Informatics

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“…DKT-LCIRT [40]: DKT-LCIRT not only models student learning ability but also introduces IRT to improve the interpretability of the model improving the DKVMN model.…”

Section: Baselines and Evaluation Metricsmentioning

confidence: 99%

DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

Zhang,

Gong,

Luo

et al. 2024

IEEE Access

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Knowledge tracing aims to predict students' future question-answering performance based on their historical question-answering records, but the current mainstream knowledge tracing model ignores the individual differences in different students' knowledge-absorption and problem-solving abilities, which leads to a poor prediction of students' question-answering performance by the model. To solve this, Dynamic Key-Value Memory Networks Knowledge Tracing with Students' Knowledge-Absorption Ability and Problem-Solving Ability (DKVMN-KAPS) is proposed in this paper. Firstly, a hierarchical convolutional neural network is used to consider students' knowledge mastery at multiple time steps, and then quantify students' knowledge-absorption ability, aiming to more accurately portray students' knowledge states; secondly, an autoencoder is used to dynamically update students' problem-solving ability at each time step; and finally, students' question answering performance is predicted by considering the students' knowledge state, problemsolving ability, and question features. Extensive experiments on three datasets show that the prediction performance of DKVMN-KAPS outperforms existing models and improves the prediction accuracy of deep knowledge tracing models.INDEX TERMS knowledge tracing, knowledge-absorption ability, problem-solving ability, individual differences.

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“…For example, Zhang et al [171] developed Gated-GNN to trace the student-knowledge response records and to extract students' latent traits, and used IRT to predict the probability of students answering exercises correctly. Gan et al [44] and Li et al [77] combined the key-value memory network with IRT. Wang et al [144] proposed a Dynamic Cognitive Diagnosis (DynamicCD) approach.…”

Section: Combination Of Cognitive Diagnosis and Knowledge Tracingmentioning

confidence: 99%

Modern Science Education in China

Wang

2021

Western Influences in the History of Science and Technology in Modern China

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Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate the massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information to the receiver.In both of the schemes, the receiver jointly decodes the information from the BS and the IoT devices.To evaluate the above two schemes, in this paper, we have derived the closed-form expressions of the ergodic rates and the outage probabilities for the cellular and IoT transmissions. Finally, numerical results are provided to verify the theoretical analysis and compare the two proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

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

Cited by 5 publications

References 41 publications

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

Modern Science Education in China

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