Volume 12, Issue 1 (5-2025)
Human Information Interaction 2025, 12(1): 0-0 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Moradi M. A Model for Increasing Student Interaction with Professors and Course Content Based on Data Mining Techniques. Human Information Interaction 2025; 12 (1)
URL: http://hii.khu.ac.ir/article-1-3203-en.html
URL: http://hii.khu.ac.ir/article-1-3203-en.html
Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Bozorgmehr University of Qaenat, Qaenat, Iran
Abstract: (637 Views)
Background and Purpose: Every academic semester, universities evaluate the educational quality of professors based on evaluation criteria determined by the Ministry of Science in order to determine whether quality standards are being met. However, it has never been investigated which of the determined criteria had the greatest impact on increasing student interaction with the professor and course content, and consequently increasing student learning and productivity. Also, methods such as multiple attribute decision making techniques (MADM) only measure expert opinions for each evaluation criterion, which may be inconsistent with reality. Therefore, the purpose of this research is to present a model to increase student interaction with professors and course content based on data mining techniques.
Method: In order to examine the importance of each evaluation criterion in increasing student interaction with professors and course content, data mining techniques and regression models were used. A decision tree classification model has also been presented to predict the academic status of students based on the characteristics of a professor.
Results: Based on the results, the evaluation criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material" with a coefficient of 28.907 had the greatest impact on increasing student interaction with professors and course content, and consequently increasing student average scores. The evaluation criterion "social etiquette and behavior with students and mutual respect" with a coefficient of 12.069 was the second most influential factor. Also, the evaluation criterion "class discipline and time management" with a coefficient of 11.597 is the third influential factor.
Conclusion: The level of importance obtained for each evaluation criterion and the classification model created can help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, increase their efficiency and average grades.
Method: In order to examine the importance of each evaluation criterion in increasing student interaction with professors and course content, data mining techniques and regression models were used. A decision tree classification model has also been presented to predict the academic status of students based on the characteristics of a professor.
Results: Based on the results, the evaluation criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material" with a coefficient of 28.907 had the greatest impact on increasing student interaction with professors and course content, and consequently increasing student average scores. The evaluation criterion "social etiquette and behavior with students and mutual respect" with a coefficient of 12.069 was the second most influential factor. Also, the evaluation criterion "class discipline and time management" with a coefficient of 11.597 is the third influential factor.
Conclusion: The level of importance obtained for each evaluation criterion and the classification model created can help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, increase their efficiency and average grades.
Keywords: Professor-Student Interaction, Professors Evaluation Criteria, Data Mining, Regression Model, Decision Tree Model.
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
