ارائه الگویی برای افزایش تعامل دانشجویان با استادان و محتوای دروس بر اساس تکنیک‌های داده‌کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان
محمد مرادی 1
سمیرا دانیالی 2
1 گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه بزرگمهر قائنات، قائنات، ایران.
2 گروه، علم اطلاعات و دانش‌شناسی، دانشکده روانشناسی و علوم تربیتی، دانشگاه خوارزمی، تهران، ایران
چکیده
زمینه و هدف: دانشگاه‌ها هر نیم‌سال تحصیلی به‌منظور آگاهی از تحقق استانداردهای کیفیت، به ارزیابی کیفیت آموزشی استادان بر اساس شاخص‌های ارزشیابی تعیین‌شده توسط وزارت علوم می‌پردازند. اما هیچ‌گاه بررسی نشده است که کدام‌یک از شاخص‌های تعیین‌شده، تأثیر بیشتری برافزایش تعامل دانشجو با استاد و محتوای دروس و درنتیجه افزایش یادگیری و بازدهی دانشجویان داشته است. همچنین، روش‌هایی مانند تکنیک‌های تصمیم‌گیری چندشاخصه (MADM) تنها نظرات خبرگان را برای هر یک از شاخص‌های ارزشیابی می‌سنجند که ممکن است با واقعیت در تناقض باشد. لذا هدف این پژوهش ارائه الگویی به‌منظور افزایش تعامل دانشجویان با استادان و محتوای دروس بر اساس تکنیک‌های داده‌کاوی است.

روش پژوهش: به‌منظور بررسی اهمیت هر شاخص ارزشیابی در افزایش تعامل دانشجویان با استادان و محتوای دروس، از تکنیک‌های داده‌کاوی و مدل رگرسیون استفاده‌شده است. همچنین به ارائه مدل طبقه‌بندی درخت تصمیم به‌منظور پیش‌بینی وضعیت تحصیلی دانشجویان بر اساس ویژگی‌های یک استاد پرداخته‌شده است.

یافته‌ها: بر اساس نتایج بدست آمده، شاخص ارزشیابی «داشتن طرح درس مناسب و جامعیت و پیوستگی در ارائه مطالب» با ضریب 28.907 بیشترین تأثیر را در افزایش تعامل دانشجویان با استادان و محتوای دروس و درنتیجه افزایش میانگین نمرات دانشجویان داشته است. شاخص ارزشیابی «آداب و رفتار اجتماعی با دانشجویان و احترام متقابل» با ضریب 12.069 دومین عامل تأثیرگذار بوده است. همچنین شاخص ارزشیابی «مدیریت نظم و زمان کلاس» با ضریب 11.597 سومین عامل تأثیرگذار است.

نتیجه‌گیری: میزان اهمیت بدست آمده برای هر شاخص ارزشیابی و مدل طبقه‌بندی ایجادشده می‌تواند به استادان و مدیران آموزشی به‌منظور تعیین شیوه تدریس و مدیریت کلاس در جهت افزایش تعامل دانشجویان با استادان و محتوای دروس و درنتیجه افزایش بازدهی و میانگین نمرات آن‌ها کمک کند.
کلیدواژه‌ها
تعامل استاد و دانشجو
شاخص‌های ارزشیابی استادان
تکنیک‌های داده‌کاوی
مدل رگرسیون
مدل درخت تصمیم

عنوان مقاله English

A Model for Increasing Student Interaction with Professors and Course Content Based on Data Mining Techniques

نویسندگان English

Mohammad Moradi 1
Samira Danialy 2
1 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Bozorgmehr University of Qaenat, Qaenat, Iran
2 Ph.D. in Knowledge and Information Science Kharazmi University, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran
چکیده English

Introduction: 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.

کلیدواژه‌ها English

Professor-Student Interaction
Professors Evaluation Criteria
Data mining
regression model
Decision Tree Model
منابع
هرندی، عطاءالله؛ میرزائیان خمسه، پیوند. (1402). تبیین نقش بازاریابی رسانه‌های اجتماعی شرکتی بر درگیرسازی مشتریان: بررسی تجربه برند و نسل سنی مشتریان. بررسی‌های مدیریت رسانه، 2(3)، 337-365.
https://doi.org/10.22059/mmr.2024.369197.1071
لبافی، سمیه؛ روشندل اربطانی، طاهر؛ محمدی، داوود. (1396). بررسی نقش شبکه‌های اجتماعی در بازاریابی با استفاده از چارچوب تیلور و اوکازاکی (مطالعه موردی). نشریه مطالعات رسانه‌ای، 12(3)، 103-114.
https://www.magiran.com/p1973516

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Anand, K., Urolagin, S., & Mishra, R. K. (2021). How does hand gestures in videos impact social media engagement-Insights based on deep learning. International Journal of Information Management Data Insights, 1(2), 100036. https://doi.org/10.1016/j.jjimei.2021.100036
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منابع
هرندی، عطاءالله؛ میرزائیان خمسه، پیوند. (1402). تبیین نقش بازاریابی رسانه‌های اجتماعی شرکتی بر درگیرسازی مشتریان: بررسی تجربه برند و نسل سنی مشتریان. بررسی‌های مدیریت رسانه، 2(3)، 337-365.
https://doi.org/10.22059/mmr.2024.369197.1071
لبافی، سمیه؛ روشندل اربطانی، طاهر؛ محمدی، داوود. (1396). بررسی نقش شبکه‌های اجتماعی در بازاریابی با استفاده از چارچوب تیلور و اوکازاکی (مطالعه موردی). نشریه مطالعات رسانه‌ای، 12(3)، 103-114.
https://www.magiran.com/p1973516

References
Abu-Shanab, E., Al-Sharafi, M. A., & Al-Emran, M. (2024). The influence of network externality and fear of missing out on the continuous use of social networks: a cross-country comparison. International Journal of Human–Computer Interaction, 40(15), 4058-4070. https://doi.org/10.1080/10447318.2023.2208990
Aldous, K. K., An, J., & Jansen, B. J. (2019, July). View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, pp. 47-57). https://doi.org/10.1609/icwsm.v13i01.3208
AlRawashdeh, H., Shwedeh, F., & Abdallah, S. (2017, July). How post time and type affect user engagement on public profiles in the Arab World. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 554-561). https://doi.org/10.1145/3110025.3110108
Anand, K., Urolagin, S., & Mishra, R. K. (2021). How does hand gestures in videos impact social media engagement-Insights based on deep learning. International Journal of Information Management Data Insights, 1(2), 100036. https://doi.org/10.1016/j.jjimei.2021.100036
Antonakopoulou, S., & Veglis, A. (2021). How post time and post type affect the engagement on Facebook: The case of a national media organization. Observatorio (OBS*), 15(4). https://doi.org/10.15847/obsOBS15420211856
Dhanesh, G., Duthler, G., & Li, K. (2022). Social media engagement with organization-generated content: Role of visuals in enhancing public engagement with organizations on Facebook and Instagram. Public Relations Review, 48(2), 102174. https://doi.org/10.1016/j.pubrev.2022.102174
Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., & Goodman, S. (2019). Social media engagement behavior: A framework for engaging customers through social media content. European journal of marketing, 53(10), 2213-2243. https://doi.org/10.1108/EJM-03-2017-0182
Fu, G. (2005). Modeling water availability and its response to climatic change for the Spokane River Watershed. Washington State University. https://hdl.handle.net/2376/413
Gkikas, D. C., Tzafilkou, K., Theodoridis, P. K., Garmpis, A., & Gkikas, M. C. (2022). How do text characteristics impact user engagement in social media posts: Modeling content readability, length, and hashtags number in Facebook. International Journal of Information Management Data Insights, 2(1), 100067. https://doi.org/10.1016/j.jjimei.2022.100067
Harandi, A. O., & MirzaeianKhamseh, P. (2023). Explaining the Role of Company's Social Media Marketing on Customer Engagement: Examining Brand Experience and Customer Age Generation. , 2(3), 337-365. (in Persian) https://doi.org/10.22059/mmr.2024.369197.1071
Huang, J., Wang, C., Su, M., Dai, Q., & Bhuiyan, M. Z. A. (2018, October). Inspecting influences on likes and comments of photos in instagram. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 938-945). IEEE. https://doi.org/10.1109/SmartWorld.2018.00168
Jaakonmäki, R., Müller, O., & Vom Brocke, J. (2017, January). The impact of content, context, and creator on user engagement in social media marketing. In Proceedings of the Annual Hawaii International Conference on System Sciences (Vol. 50, pp. 1152-1160). IEEE Computer Society Press. https://doi.org/10125/41289
Joo, S., Lu, K., & Lee, T. (2020). Analysis of content topics, user engagement and library factors in public library social media based on text mining. Online information review, 44(1), 258-277. https://doi.org/10.1108/OIR-11-2018-0345
Kontsevaia, D. B., & Berger, P. D. (2017). Analyzing factors affecting the success of social media posts for B2B networks: a fractional-factorial design approach. International Journal of Business, Economics and Management, 4(6), 112-123. https://doi.org/10.18488/journal.62.2017.46.112.123
Labbafi, S., Roshandel, T., & Mohammadi, D. (2018). Role of Social Network in marketing; use of the Taylor and Okazaki framework (Case Study). Media Studies, 12(3), 103-114. (in Persian) https://www.magiran.com/p1973516
Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management science, 64(11), 5105-5131. https://doi.org/10.1287/mnsc.2017.2902
León-Alberca, T., Renés-Arellano, P., & Aguaded, I. (2024). Digital Marketing and Technology Trends: Systematic Literature Review on Instagram. In International Conference On Communication And Applied Technologies (pp. 309-318). Springer, Singapore. https://doi.org/10.1007/978-981-99-7210-4_29
Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of marketing research, 57(1), 1-19. https://doi.org/10.1177/0022243719881113
Misra, A., Dinh, T. D., & Ewe, S. Y. (2024). The more followers the better? The impact of food influencers on consumer behaviour in the social media context. British Food Journal. https://doi.org/10.1108/BFJ-01-2024-0096
Monacho, B. C., & Slamet, Y. U. L. I. U. S. (2023). The Effect of Influencer Engagement Rate in Increasing Followers of Instagram Official Account. Jurnal Komunikasi: Malaysian Journal of Communication, 39(2), 373-388. https://ejournal.ukm.my/mjc/issue/view/1605
Moon, S., & Yoo, S. (2022). Are More Followers Always Better? The Non-Linear Relationship between the Number of Followers and User Engagement on Seeded Marketing Campaigns in Instagram. Asia Marketing Journal, 24(2), 62-77. https://doi.org/10.53728/2765-6500.1589
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تعامل انسان و اطلاعات
دوره 12، شماره 1
بهار 1404
صفحه 61-78