کاربرد فنون داده‌کاوی برای پیش‌بینی وضعیت سلامت روان دانشجویان با هدف بهبود وضعیت آموزش

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران

2 دانشکده مهندسی صنایع، دانشگاه صنعتی سجاد، مشهد، ایران

چکیده

بهبود عملکرد دانشجویان همواره یکی از مهم‌ترین اهداف مسئولان و مدیران دانشگاه‌ها و مراکز آموزشی به شمار می‌رود. عوامل متعددی بر عملکرد مناسب دانشجویان تأثیرگذار است. علاوه بر عواملی که در حوزه آموزش و یادگیری دانشجویان است، موضوع سلامت جسمانی و روانی نیز بر نحوه عملکرد آن‌ها تأثیر می‌گذارد. به منظور تصمیم‌گیری به‌موقع و متناسب با وضعیت روانی هر دانشجو نیاز است الگوهایی در دسترس باشد تا بتوان بر اساس آن‌ها وضعیت بهداشت روان هر دانشجو پیش‌بینی شود. در این پژوهش تلاش شده با به‌کارگیری فن داده‌کاوی، وضعیت دانشجویان ورودی جدید دانشگاه، از لحاظ نیاز به مراجعه به مشاوره مورد بررسی قرار گیرد و الگوهای پنهان نهفته در مجموعه داده پایش سلامت روان دانشجویان با به‌کارگیری فنون رویکرد طبقه‌بندی استخراج گردد. فنون استفاده‌شده در این پژوهش، شامل درخت تصمیم‌، طبقه‌بندی بر اساس قانون، شبکه‌های عصبی، رگرسیون لجستیک و ماشین بردار پشتیبان می‌باشد. برای تمامی پارامترهای فنون مذکور، تنظیم انجام شده و نشان‌دهنده علائم نیاز به مشاوره با نرخ صحت 99% می‌باشد. نتایج پژوهش نشان داد: می‌توان بر اساس مدل تدوین شده، وضعیت سلامت روانی دانشجویان را پیش بینی نمود. یکی از خروجی‌های کاربرد روش درخت تصمیم، این است که اگر فردی از یک ماه گذشته تا به امروز شدیداً، احساس ناامیدی ‌کند، یا به نظر اطرافیانش فردی وسواسی باشد یا احساس کند زندگی برایش بی‌ارزش است به مشاوره احتیاج دارد.

چکیده تصویری

کاربرد فنون داده‌کاوی برای پیش‌بینی وضعیت سلامت روان دانشجویان با هدف بهبود وضعیت آموزش

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of data mining techniques to predict of students' mental health status to improve educational performance

نویسندگان [English]

  • Hamidreza Koosha 1
  • Sana Dangkoub 2
  • Amirabbas Barzanooni 2
1 Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2 Department of Industrial Engineering, Sadjad university of Technology, Mashhad, Iran
چکیده [English]

One of the main objectives of every managers of educational centers is making improvements in students’ educational performance. Besides the educational factors, physical and mental health is considerable which has a significant effect on students’ behavior. Therefore, some rules and patterns are required to make the best decisions, based on the prediction of students’ mental health state. This paper proposes a data mining approach for analyzing and extracting patterns in terms of new students’ mental health, which means whether they need to visit a psychologist. Our effort was on extracting hidden rules in new students’ mental health examination by employing classification approach. Techniques used in this study are decision tree, rule based classifier, neural network, logistic regression and support vector machine. Moreover, a parameter tuning process is done for all the techniques mentioned and the results presents the list of symptoms of individuals who need detailed examination. The results of the research represent that one can predict the status of students’ mental helath based on propsed model. One of the outcomes of decision tree is that if a person severely feels disappointed or seems to be obsessive by others, or feels that life is worthless, definitely a consultaion is needed. 

کلیدواژه‌ها [English]

  • Data mining
  • Classification approach
  • Prediction
  • Mental health
  • Detailed examination requirement

[1] Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 73, 247-256.

[2] Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution. Computers & Education, 113, 226-242.

[3] Bali, R. K. (Ed.). (2005). Clinical knowledge management: opportunities and challenges. IGI Global. Published in the United State of America by Idea Group Publishing.

[4] Moghadasi, H. & Hosseini, A. Asadi, F. & Jahanbakhsh, M. (2010). Data mining and health care. Health Information Management Journal, 9(2), 297-304. (In Persian).

[5] Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177-194. ‏

[6] Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv: 1201.3418.‏

[7] Alfiani, A. P., & Wulandari, F. A. (2015). Mapping Student's Performance Based on Data Mining Approach (A Case Study). Agriculture and Agricultural Science Procedia, 3, 173-177.‏

[8] Ilayaraja, M., & Meyyappan, T. (2015). Efficient Data Mining Method to Predict the Risk of Heart Diseases through Frequent Itemsets. Procedia Computer Science, 70, 586-592.‏

[9] Chang, C. L. (2007). A study of applying data mining to early intervention for developmentally-delayed children. Expert Systems with Applications, 33(2), 407-412.

[10] Choo, C., Diederich, J., Song, I., & Ho, R. (2014). Cluster analysis reveals risk factors for repeated suicide attempts in a multi-ethnic Asian population. Asian Journal of Psychiatry, 8, 38-42.‏

[11] Paramasivam, V., Yee, T. S., Dhillon, S. K., & Sidhu, A. S. (2014). A methodological review of data mining techniques in predictive medicine: An application in hemodynamic prediction for abdominal aortic aneurysm disease. Biocybernetics and Biomedical Engineering, 34(3), 139-145.‏

[12] Torkestani, MS. & Dehpanah, A. & Taghavifard, MT. & Shafee, SH. (2015). A framework for modifying the insurance rate in automobile industry by Neural Network (case: Asia insurance company). Journal of Information Technology Management. 8(4).711-732. [In Persian].

[13] Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

[14] Burgos, C., Campanario, M. L., de la Pena, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 66, 541-556.

[15] Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43-47.

[16] Natek, S., & Zwilling, M. (2014). Student data mining solution–knowledge management system related to higher education institutions. Expert systems with applications, 41(14), 6400-6407.

[17] Peral, J., Maté, A., & Marco, M. (2017). Application of Data Mining techniques to identify relevant Key Performance Indicators. Computer Standards & Interfaces, 50, 55-64.

[18] Ahmed, A. M., Rizaner, A., & Ulusoy, A. H. (2016). Using data mining to predict instructor performance. Procedia Computer Science, 102, 137-142.

[19] Gobert, J. D., Kim, Y. J., Sao Pedro, M. A., Kennedy, M., & Betts, C. G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18, 81-90.

[20] Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector. Procedia Computer Science, 57, 500-508.

[21] Diederich, J., Al-Ajmi, A., & Yellowlees, P. (2007). E x-ray: data mining and mental health. Applied Soft Computing, 7(3), 923-928.‏

[22] Fernández-Arteaga, V., Tovilla-Zárate, C. A., Fresán, A., González-Castro, T. B., Juárez-Rojop, I. E., López-Narváez, L., & Hernández-Díaz, Y. (2016). Association between completed suicide and environmental temperature in a Mexican population, using the Knowledge Discovery in Database approach. Computer Methods and Programs in Biomedicine, 135, 219-224.

[23] Aljumah, A. A., Ahamad, M. G., & Siddiqui, M. K. (2013). Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University-Computer and Information Sciences, 25(2), 127-136.

[24] Ramanan, S., de Souza, L. C., Moreau, N., Sarazin, M., Teixeira, A. L., Allen, Z. & Bertoux, M. (2017). Determinants of theory of mind performance in Alzheimer's disease: A data-mining study. Cortex, 88, 8-18.

[25] Nguyên, X. L., Chaskalovic, J., Rakotonanahary, D., & Fleury, B. (2010). Insomnia symptoms and CPAP compliance in OSAS patients: A descriptive study using Data Mining methods. Sleep medicine, 11(8), 777-784.‏

[26] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 15, 104-116.‏

[27] Ni, H., Yang, X., Fang, C., Guo, Y., Xu, M., & He, Y. (2014). Data mining-based study on sub-mentally healthy state among residents in eight provinces and cities in China. Journal of Traditional Chinese Medicine, 34(4), 511-517.‏

[28] Rostami, M. & Ayat, SS. & Saghari, F. & Yaghoobi, F. (2014). Prediction of educational progress with fuzzy clustering in educational centers. Journal of Educational Technology, 10(1), 23-36. (In Persian).

[29] Maghsoodi, B. & Soleilmani, S. & Amiri, A. & Afsharchi, M. (2012). Improvement in the quality of electronic educational systems using educational data maining. Journal of Educational Technology, 6(4), 277-286. (In Persian).

[30] Kohavi, R., & Quinlan, J. R. (2002, January). Data mining tasks and methods: Classification: decision-tree discovery. Willi Klösgen, Willi Klosgen, Jan M. Żytkow, (Eds), In Handbook of data mining and knowledge discovery (pp. 267-276). Oxford University Press, Inc.