<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a knowledge difficulty clustering algorithm (MIBKPC) based on multidimensional time-series data and learning path networks is proposed. The algorithm integrates the cyclic learning sequence, forgetting behavior and system interaction degree formed by learners learning some of the more difficult knowledge points, and constructs a computational model for the difficulty similarity of knowledge points based on system interaction behavior so as to better portray the difficulty similarity of knowledge points based on system interaction behavior. Then a knowledge point difficulty clustering algorithm (MFSKPC) based on multidimensional time-series data and maximum frequent subgraphs is proposed. The algorithm extracts the maximum frequent subgraphs of the atlas based on the learner’s directed learning path atlas using the gSpan-based maximum frequent subgraph mining algorithm and portrays the difficulty similarity of knowledge points based on system interaction behaviors by combining the system interaction degree and the maximum frequent subgraphs. In the accuracy comparison, the clustering accuracy of the MFSKPC algorithm for advanced learners was higher than that of the MIBKPC algorithm for both K=3 and K =5 conditions by 7.89% and 8.29%, respectively. In the comparative analysis of double-loop psychological instruction, the experimental class improved its pre and post-test scores by an average of 2.38 points, while the control group improved by an average of only 1.88 points. The experiment showed that the double-loop teaching based on the MFSKPC algorithm was more effective.</p>
</abstract>

In this paper, a knowledge difficulty clustering algorithm (MIBKPC) based on multidimensional time-series data and learning path networks is proposed. The algorithm integrates the cyclic learning sequence, forgetting behavior and system interaction degree formed by learners learning some of the more difficult knowledge points, and constructs a computational model for the difficulty similarity of knowledge points based on system interaction behavior so as to better portray the difficulty similarity of knowledge points based on system interaction behavior. Then a knowledge point difficulty clustering algorithm (MFSKPC) based on multidimensional time-series data and maximum frequent subgraphs is proposed. The algorithm extracts the maximum frequent subgraphs of the atlas based on the learner’s directed learning path atlas using the gSpan-based maximum frequent subgraph mining algorithm and portrays the difficulty similarity of knowledge points based on system interaction behaviors by combining the system interaction degree and the maximum frequent subgraphs. In the accuracy comparison, the clustering accuracy of the MFSKPC algorithm for advanced learners was higher than that of the MIBKPC algorithm for both K=3 and K =5 conditions by 7.89% and 8.29%, respectively. In the comparative analysis of double-loop psychological instruction, the experimental class improved its pre and post-test scores by an average of 2.38 points, while the control group improved by an average of only 1.88 points. The experiment showed that the double-loop teaching based on the MFSKPC algorithm was more effective.

Research on online and offline double-loop teaching of college students’ mental health education based on cluster analysis algorithm

Aviation Maintenance NCO School, Air Force Engineering University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a knowledge difficulty clustering algorithm (MIBKPC) based on multidimensional time-series data and learning path networks is proposed. The...

Research on online and offline double-loop teaching of college students’ mental health education based on cluster analysis algorithm

Data publikacji: 04 paź 2023

Zakres stron: -

Otrzymano: 11 lis 2022

Przyjęty: 14 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00479

Słowa kluczowe
Mental health education, Multidimensional interaction behavior, Maximum frequent subgraph, Knowledge difficulty clustering, gSpan

© 2023 Yang Bai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on online and offline double-loop teaching of college students’ mental health education based on cluster analysis algorithm

Data publikacji: 04 paź 2023

Zakres stron: -

Otrzymano: 11 lis 2022

Przyjęty: 14 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00479

Słowa kluczoweMental health education, Multidimensional interaction behavior, Maximum frequent subgraph, Knowledge difficulty clustering, gSpan

© 2023 Yang Bai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Mental health education, Multidimensional interaction behavior, Maximum frequent subgraph, Knowledge difficulty clustering, gSpan