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Human Information Interaction 2025, 12(1): 61-78 |
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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: (864 Views)
In order to know whether the quality standards are being met, universities evaluate the educational quality of professors every semester using professor evaluation by students based on evaluation criteria determined by the Ministry of Science. However, it has never been investigated which of the criteria has had the greatest impact on increasing student interaction with professors and course content, and consequently increasing student learning and productivity. Also, methods such as Multiple Attribute Decision Making (MADM) techniques only measure the opinions of experts for each of the evaluation criteria, which may be in contradiction with reality. Therefore, the purpose of this study is to investigate the importance of each of the professor evaluation criteria related to student-professor interaction and course content based on students' performance and their average scores, as well as the results of professor evaluations by students. For this purpose, data mining techniques and regression models have been used. Also, a decision tree classification model has been presented to predict the academic status of students based on the characteristics of a professor.
Methods and Materials
The research method consists of 4 phases. In the first phase, the evaluation criteria for university professors related to student interaction with professors and course content were reviewed based on the items announced by the Ministry of Science. Then, in the second phase, data and information on the evaluation of professors by students and the average efficiency and grades of students were collected. In the third phase, the collected data were analyzed using data mining techniques and regression models, and the importance of each evaluation criteria was examined. In the fourth phase, a decision tree classification model was presented to predict the academic status of students according to the characteristics of the professor. The presented model will help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, achieve the desired academic status of students.
Resultss and Discussion
Based on the results obtained, 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, as a result, increasing student productivity and grades. This emphasizes the need to use organization in teaching and learning, and the teacher should pay special attention to setting the lesson plan as planning and organizing the set of activities in relation to educational goals, lesson content, and students' abilities for the duration of the semester. The evaluation criterion "social manners and behavior with students and mutual respect" with a coefficient of 12.069 is the second factor affecting student efficiency. The evaluation criterion "classroom order and time management" with a coefficient of 11.597 is the third factor affecting student efficiency and scores. "Teacher's mastery of the subject matter" with a coefficient of 8.316 has been identified as the fourth factor affecting student efficiency and scores. The evaluation criterion "appropriateness of teaching strategies and methods to the course objectives" with a coefficient of 7.775 has been identified as the fifth factor affecting students' scores. The evaluation criterion "using appropriate student evaluation methods according to the course objectives" with a coefficient of 7.769 has been the sixth factor affecting students' average scores. "Possibility of communication (face-to-face and offline) with the professor outside the classroom" with a coefficient of 1.571 is the seventh factor affecting students' efficiency. Also, solutions were presented to strengthen the evaluation criterion with high weight and importance, namely the criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material".
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.
Methods and Materials
The research method consists of 4 phases. In the first phase, the evaluation criteria for university professors related to student interaction with professors and course content were reviewed based on the items announced by the Ministry of Science. Then, in the second phase, data and information on the evaluation of professors by students and the average efficiency and grades of students were collected. In the third phase, the collected data were analyzed using data mining techniques and regression models, and the importance of each evaluation criteria was examined. In the fourth phase, a decision tree classification model was presented to predict the academic status of students according to the characteristics of the professor. The presented model will help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, achieve the desired academic status of students.
Resultss and Discussion
Based on the results obtained, 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, as a result, increasing student productivity and grades. This emphasizes the need to use organization in teaching and learning, and the teacher should pay special attention to setting the lesson plan as planning and organizing the set of activities in relation to educational goals, lesson content, and students' abilities for the duration of the semester. The evaluation criterion "social manners and behavior with students and mutual respect" with a coefficient of 12.069 is the second factor affecting student efficiency. The evaluation criterion "classroom order and time management" with a coefficient of 11.597 is the third factor affecting student efficiency and scores. "Teacher's mastery of the subject matter" with a coefficient of 8.316 has been identified as the fourth factor affecting student efficiency and scores. The evaluation criterion "appropriateness of teaching strategies and methods to the course objectives" with a coefficient of 7.775 has been identified as the fifth factor affecting students' scores. The evaluation criterion "using appropriate student evaluation methods according to the course objectives" with a coefficient of 7.769 has been the sixth factor affecting students' average scores. "Possibility of communication (face-to-face and offline) with the professor outside the classroom" with a coefficient of 1.571 is the seventh factor affecting students' efficiency. Also, solutions were presented to strengthen the evaluation criterion with high weight and importance, namely the criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material".
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.
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