ICD: A new interpretable cognitive diagnosis model for intelligent tutor systems

Qi, Tianlong; Ren, Meirui; Guo, Longjiang; Li, Xiaokun; Li, Jin; Zhang, Lichen

doi:10.1016/j.eswa.2022.119309

Cited by 18 publications

(3 citation statements)

References 21 publications

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“…It also uncovers potential higher-order relations between these entities. The ICD, a cognitive diagnostic model proposed by Qi et al uses three layers of neural networks to model the influence of exercises on concepts, the interaction between concepts, and the influence of concepts on exercises, aiming to address the interaction among knowledge concepts and the quantitative relation between exercises and concepts [50]. These models have shown the comparable capacity of graph neural networks in modeling the complex learning interaction among students.…”

Section: Graph Neural Network In Educational Applicationsmentioning

confidence: 99%

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Huang,

Chen

2024

J Big Data

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Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.

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Section: Graph Neural Network In Educational Applicationsmentioning

confidence: 99%

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Huang,

Chen

2024

J Big Data

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“…The application of cognitive diagnosis in the education industry has led to a shift towards personalized education in traditional online classrooms [15,16]. Cognitive diagnosis models can be differentiated from two perspectives.…”

Section: Related Workmentioning

confidence: 99%

BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning

Chen,

Liang

2023

BDCC

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In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students’ cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students’ mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models.

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“…Chen et al proposed DCD [19], which maps cognitive parameters and test item traits into distributional forms, utilizing a variational autoencoder as the interaction function to enhance the model's generalization capability. The ICD model proposed by Qi et al [108] basically follows the encoder-decoder architecture. A data enhancement approach was proposed so that each learner's responses were divided differently multiple times.…”

Section: Deep Neural Network (Dnn)mentioning

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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ICD: A new interpretable cognitive diagnosis model for intelligent tutor systems

Cited by 18 publications

References 21 publications

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Enhancing academic performance prediction with temporal graph networks for massive open online courses

BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning

Modern Science Education in China

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