Exercise Recommendation Based on Cognitive Diagnosis and Neutrosophic Set

Ma, Hua; Huang, Zhuoxuan; Tang, Wensheng; Zhang, Xuxiang

doi:10.1109/cscwd54268.2022.9776197

Cited by 9 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that existing research fails to deal with the possible complexity of knowledge concepts and the uncertainty of learners' cognitive status, Ma et al [12] present an exercise recommendation approach via a new fuzzy cognitive diagnosis method. This approach utilizes Neutrosophic set theory to diagnose learners' knowledge status comprehensively, and suitable exercises are recommended to learners by combining the user-based CF method and the probability matrix factorization method.…”

Section: Traditional Learning Resource Recommendationmentioning

confidence: 99%

Learning resource recommendation via knowledge graphs and learning style clustering

Tang

Zhang

et al. 2023

IFS

View full text Add to dashboard Cite

An e-learning system should recommend learners appropriate learning resources according to their actual needs and cognitive status for improving their learning performance. To overcome the deficiencies of existing approaches (e.g., poor interpretability, limited efficiency and accuracy of recommendation), we propose a new recommendation approach to learning resources via knowledge graphs and learning style clustering. In this approach, the knowledge graphs of an online learning environment are constructed based on a generic ontology model, and the graph embedding algorithm and graph matching process are applied to optimize the efficiency of graph computation for identifying similar learning resources. By introducing learning style theory, learners are clustered based on their learning styles. Based on the clustering results, the learners’ degrees of interest in similar learning resources are measured, and the recommendation results are obtained according to the degrees of interest. Finally, the experiments demonstrate that the proposed approach significantly enhances the computational efficiency and the quality of learning resource recommendation compared with the existing approaches in large-scale graph data scenarios.

show abstract

Section: Traditional Learning Resource Recommendationmentioning

confidence: 99%

Learning resource recommendation via knowledge graphs and learning style clustering

Tang

Zhang

et al. 2023

IFS

View full text Add to dashboard Cite

show abstract

“…Yet, the former strategy might lead to students selecting learning resources that are either overly basic or excessively advanced, consequently impeding optimal learning efficiency. Conversely, the latter method could potentially create higher barriers to access [36,96].…”

Section: Applications In Educationmentioning

confidence: 99%

Modern Science Education in China

Wang

2021

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

View full text Add to dashboard Cite

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.

show abstract

“…The fusion of education with information science is an inexorable trend, catalyzing an expansive realm of research in online education technologies. Notably, the application of artificial intelligence and related technologies to analyze substantial educational data yields invaluable insights and enhances the educational landscape [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Online educational platforms, like edX, Coursera, and Udacity, have gained widespread adoption for tracking, reporting, and delivering online courses [3,5,6]. These platforms offer students a spectrum of advantages, including diverse online courses and personalized learning resources [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing

Li,

Wang

2023

PLoS ONE

View full text Add to dashboard Cite

Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing. Our model mitigates the impact of subjective labeling by fine-tuning the skill relation matrix and Q-matrix. Additionally, we employ Graph Convolutional Networks (GCNs) to capture intricate interactions between students, exercises, and skills. Specifically, the Knowledge Relation Importance Rank Calibration method is employed to generate the skill relation matrix and Q-matrix. These calibrated matrices, alongside heterogeneous interactions, serve as input for the GCN to compute exercise and skill embeddings. Subsequently, exercise embeddings, skill embeddings, item difficulty, and contingency tables collectively contribute to an exercise relation matrix, which is then fed into an attention mechanism for predictions. Experimental evaluations on two publicly available educational datasets demonstrate the superiority of our proposed model over baseline models, evidenced by enhanced performance across three evaluation metrics.

show abstract

Exercise Recommendation Based on Cognitive Diagnosis and Neutrosophic Set

Cited by 9 publications

References 13 publications

Learning resource recommendation via knowledge graphs and learning style clustering

Learning resource recommendation via knowledge graphs and learning style clustering

Modern Science Education in China

Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing

Contact Info

Product

Resources

About