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

<p>In this study, an intelligent decision support system for education was successfully constructed by integrating data mining techniques, evaluation similarity matrix and novel braided net clustering method. Based on the evaluation similarity matrix, this system brings together decision-making members with similar views through clustering analysis. It calculates these categorized groups’ preferences, consistency indexes, and decision values to derive the optimal solution. The research in this paper empirically validates the effectiveness of the intelligent decision support system against the background of actual teaching scenarios and with all students in their classes as the research subjects. The validation results show that in the Powerpoint Practical and PS Practical tests, the average scores of the students in the experimental class reached 87.1 and 86.2, respectively, and this score is significantly higher than that of the control class. In addition, in the Analysis of the four dimensions of pre-course learning behavior (including interest in learning, attitude toward learning, etc.), the mean values of the experimental class (4.46, 3.99, 3.89, 4.18) were also significantly higher than those of the control class (3.2, 2.73, 2.94, 2.89). The application of the Intelligent Decision Support System increased students’ learning interest and effectively improved their performance in both machine and written tests. The main effect of its teaching method is significant with an F-value of 18.029 and a P-value of less than 0.001. The results of this study confirm the practical application of intelligent decision support systems in teaching and learning, and provide a new methodology and perspective for computer-intelligent teaching.</p>
</abstract>

In this study, an intelligent decision support system for education was successfully constructed by integrating data mining techniques, evaluation similarity matrix and novel braided net clustering method. Based on the evaluation similarity matrix, this system brings together decision-making members with similar views through clustering analysis. It calculates these categorized groups’ preferences, consistency indexes, and decision values to derive the optimal solution. The research in this paper empirically validates the effectiveness of the intelligent decision support system against the background of actual teaching scenarios and with all students in their classes as the research subjects. The validation results show that in the Powerpoint Practical and PS Practical tests, the average scores of the students in the experimental class reached 87.1 and 86.2, respectively, and this score is significantly higher than that of the control class. In addition, in the Analysis of the four dimensions of pre-course learning behavior (including interest in learning, attitude toward learning, etc.), the mean values of the experimental class (4.46, 3.99, 3.89, 4.18) were also significantly higher than those of the control class (3.2, 2.73, 2.94, 2.89). The application of the Intelligent Decision Support System increased students’ learning interest and effectively improved their performance in both machine and written tests. The main effect of its teaching method is significant with an F-value of 18.029 and a P-value of less than 0.001. The results of this study confirm the practical application of intelligent decision support systems in teaching and learning, and provide a new methodology and perspective for computer-intelligent teaching.

Design and Implementation of Smart Teaching for Computer Courses in Colleges and Universities Enabled by Artificial Intelligence Technology

Applied Mathematics and Nonlinear Sciences

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

In this study, an intelligent decision support system for education was successfully constructed by integrating data mining techniques, evaluation similarity...

Design and Implementation of Smart Teaching for Computer Courses in Colleges and Universities Enabled by Artificial Intelligence Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 20. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0503

Schlüsselwörter
Similarity matrix, Coded net clustering, Decision support system, Data mining, Brilliant teaching

© 2024 Kan Wang, published by Sciendo

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

Design and Implementation of Smart Teaching for Computer Courses in Colleges and Universities Enabled by Artificial Intelligence Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 20. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0503

SchlüsselwörterSimilarity matrix, Coded net clustering, Decision support system, Data mining, Brilliant teaching

© 2024 Kan Wang, published by Sciendo

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

Schlüsselwörter
Similarity matrix, Coded net clustering, Decision support system, Data mining, Brilliant teaching