A novel quantitative relationship neural network for explainable cognitive diagnosis model

Yang, Haowen; Qi, Tianlong; Li, Jin; Guo, Longjiang; Ren, Meirui; Zhang, Lichen; Wang, Xiaoming

doi:10.1016/j.knosys.2022.109156

Cited by 18 publications

(3 citation statements)

References 13 publications

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“…Better prediction performance than that of the DKT model was obtained. To address the problem of weak modeling of knowledge point relationships, Yang et al [19] proposed a novel quantitative relationship neural network for the explainable cognitive diagnosis model (QRCDM). It used explicit and implicit correlations between exercises and corresponding knowledge concepts to calculate student errors and guesses.…”

Section: Related Workmentioning

confidence: 99%

Research on Knowledge Tracing-Based Classroom Network Characteristic Learning Engagement and Temporal-Spatial Feature Fusion

Shou,

Li,

et al. 2024

Electronics

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To accurately assess students’ cognitive state of knowledge points in the learning process within the smart classroom, a knowledge tracing (KT) model based on classroom network characteristic learning engagement and temporal-spatial feature fusion (CL-TSKT) is proposed. First, a classroom network is constructed based on the information of the student ID, seating relationship, student–student interaction, head-up or head-down state, and classroom network characteristics obtained from a smart classroom video. Second, a learning engagement model is established by utilizing the student–student interactions, head-up or head-down state, and classroom network characteristics. Finally, according to the learning engagement model and the knowledge point test data, a parallel temporal attention GRU network is proposed. It is utilized to extract the temporal features of the knowledge points and learning engagement. They are fused to obtain the knowledge point-learning engagement temporal characteristics and their associated attributes. Meanwhile, a CNN is used to extract the knowledge point-knowledge point spatial features. We consider the associative properties of knowledge point-knowledge points from a spatial perspective and fuse the knowledge point-knowledge point spatial features with the knowledge point-learning engagement temporal features. To accurately characterize the cognitive state of the knowledge points and provide effective support for teachers’ accurate and sustainable interventions for learners in the teaching and learning process, this paper conducts extensive experiments on four real datasets. The CL-TSKT model in this paper shows superior performance in all four evaluation metrics, compared with the state-of-the-art KT models.

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Section: Related Workmentioning

confidence: 99%

Research on Knowledge Tracing-Based Classroom Network Characteristic Learning Engagement and Temporal-Spatial Feature Fusion

Shou,

Li,

et al. 2024

Electronics

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show abstract

“…With the advancement of computational power and the exponential growth of data in the 21st century, Bayesian networks have found widespread application in diverse domains such as medicine [26], finance [27], and natural language processing [28]. Research in the field has also made significant progress in reasoning, learning, and the application of Bayesian networks [29].…”

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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“…Cross entropy added by some regularization terms is most adopted as the objective function [46,79,97,145,160,175]. Tong et al and Yao et al proposed pairwise objective functions to enhance the monotonicity and leverage non-interactive items [133,164].…”

Section: Gradient Descentmentioning

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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A novel quantitative relationship neural network for explainable cognitive diagnosis model

Cited by 18 publications

References 13 publications

Research on Knowledge Tracing-Based Classroom Network Characteristic Learning Engagement and Temporal-Spatial Feature Fusion

Research on Knowledge Tracing-Based Classroom Network Characteristic Learning Engagement and Temporal-Spatial Feature Fusion

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

Modern Science Education in China

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