Graph-based cognitive diagnosis for intelligent tutoring systems

Su, Yu; Cheng, Zeyu; Wu, Jinze; Dong, Yujuan; Huang, Zhenya; Wu, Le; Chen, Enhong; Wang, Shijin; Xie, Fei

doi:10.1016/j.knosys.2022.109547

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…This method employs GCN to distill features from exercises and videos, thereby enhancing the depiction of students' knowledge proficiency [48]. The graph-based Cognitive Diagnosis model (GCDM), proposed by Su et al facilitates the extraction of interactions between students, skills, and questions from heterogeneous cognitive graphs [49]. It also uncovers potential higher-order relations between these entities.…”

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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“…Some researchers have realized that the correlation between learners, items, and even knowledge concepts can form a graph structure, and therefore incorporated GNN into their cognitive diagnostic models. In these works, GNN was mainly used to either improve the embeddings of learners, items, and knowledge concepts [85,103] or model the propagation of the influence among the mastery of different knowledge concepts [46,122]. Meanwhile, the interaction among the learners, items, and knowledge concepts was still modeled with DNN.…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

“…11 (b)) and item-concept association graphs, to enhance the representations of both learners and items. Gao et al [46] and Su et al [122] modeled the heterogeneous graph structures of learner-item-knowledge to fully explored higher-order interaction relationships between the nodes and the dependency relationships between knowledge concepts within a concept map, thus enhancing the representation of learner cognitive states and item features. Li et al [79] proposed the HierCDF framework to model the influence of hierarchical knowledge structures on cognitive diagnosis.…”

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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