تحلیل داده‌های آموزشی دانشجویان با هدف ارزیابی موفقیت تحصیلی با استفاده از رویکرد داده‌کاوی (نمونه موردی: دانشکده مدیریت و مهندسی صنایع دانشگاه شاهرود)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار دانشکده مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود.

2 استادیار، دانشکده مهندسی صنایع، دانشگاه آزاد لاهیجان

چکیده

کسب دانش پیرامون الگوهای رفتاری وضعیت تحصیلی دانشجویان نقش بسزایی در مدیریت موفق مراکز آموزشی و ارائه خدمات مطلوب و متناسب با وضعیت‌های فعلی و آتی تحصیلی دانشجویان دارد. این دانش، در اطلاعات آموزشی نهفته بوده و می‌تواند با استفاده از ابزار داده‌کاوی استخراج شود. در این مقاله یک مدل داده‌کاوی مبتنی بر تکنیک خوشه‌بندی سلسله‌مراتبی توسعه‌یافته برای تحلیل اطلاعات مربوط به وضعیت تحصیلی دانشجویان شامل اطلاعات پیش از ورود به دانشگاه و وضعیت تحصیلی آنان ارائه شده است. علاوه بر آن، یک مدل پیش‌بینی مبتنی بر قرارگیری هر دانشجو با مشخصات خاص در یک خوشه و برآورد عملکرد تحصیلی وی ارائه شده است. مدل داده‌کاوی پیشنهاد شده در دانشکده مدیریت دانشگاه صنعتی شاهرود به‌عنوان نمونه موردی پیاده‌سازی شده است. نتایج تحقیق توسط خبرگان و مدیران آموزشی دانشگاه اعتبارسنجی شده است. پس از تحلیل داده‌ها و ارائه مدل پیش‌بینـی، خوشه‌های دانشجویان مبتنی بر شاخص نشان‌دهنده وضعیت تحصیلی ارائه شده است. نتایج نشان می‌دهند که برخی از ویژگی‌های دانشجویان مربوط به دوران قبل و بعد از ورود آنان به دانشگاه بر تعلق آنان به خوشه‌های وضعیت تحصیلی تأثیر معنادار دارند. با استفاده از ابزار داده‌کاوی، مجموعه‌ای از قوانین برای ارتقای کیفیت تصمیمات مدیران آموزشی و انتقال دانشجویان از خوشه‌های با وضعیت تحصیلی نامناسب به وضعیت بهتر پیشنهاد شده است. هم‌چنین با توجه به شناسایی وضعیت تحصیلی دانشجویان، مدل پیش‌بینی‌کننده وضعیت آتی تحصیلی آنان نظیر رشد معدل و یا مشروطی تحصیلی ارائه شده است. تمامی نتایج با توجه به نظرات دریافت‌شده از خبرگان ارزیابی و اعتبار آنان تأیید شده است.

کلیدواژه‌ها

عنوان مقاله [English]

Analyzing Students’ Educational Information to Evaluate Their Success via Using Data Mining Method (Case Study: Faculty of Management and Industrial Engineering, Shahrood University of Technology)

نویسندگان [English]

  • Ali Akbar Hasani 1
  • Morteza Bazrafshan 2
1 Assistant Professor, Faculty of Industrial Engineering and Management, Shahroud University of Technology.
2 Assistant Professor, Faculty of Industrial Engineering, Lahijan Azad University
چکیده [English]

Knowledge acquisition about the behavior patterns of the educational status of students can have a significant impact on the successful management of educational institutes and provides appropriate services in accordance with current and future status of students. This knowledge is hidden in educational data and could be extracted via using data mining method. In this paper, a data mining model to analyze students’ academic achievement based on the data related to before and after the students’ entrance to university was proposed. To tackle this problem, an advanced hierarchical clustering method was developed. In addition, a forecasting model to determine the belonging of each student to the each considered cluster was provided. The proposed model was evaluated on a case study of the Faculty of Management in Shahrood University of Technology. The obtained results were validated by experts and university administrators. The students were classified in different clusters via considering theirs specific features. The results indicated that some of the considered features related to before and after entering university have significant impacts on determining the students’ educational achievements and also their assigned cluster. Some decision rules are proposed for educational administrators to improve the overall students’ educational achievements and transferring students from clusters with poor academic situations to better ones such as applied rules for transferring students with special status. In addition, an efficient forecasting model for assessment of students' achievement states was proposed. The obtained results were verified by experts.
 

کلیدواژه‌ها [English]

  • educational achievement
  • educational data mining
  • forecasting model
  • advanced hierarchical clustering

مراجع

Ahmadi, S., Karimzadgan, D., Khairati Kazerooni, T. (2015). Data Mining Of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (To Prevent Customer Churn). Journal of Information Technology Management, 7(2), 217-238.
Baker, R. & Yacef, K. (2009). The state of educational data mining in 2009: A
review and future visions. Journal of Educational Data Mining, 1(1): 3-17.
Baradwaj, B.K. and Pal, S. (2011). Mining Educational Data to Analyze Students Performance. International Journal of Advanced Computer Science and Applications, 2, 6, 63-71.
Barker, K., Trafalis, T. & Rhoads, T. (2004). IEEE Systems and Information
Engineering Design Symposium, University of Oklahoma.
Beck, J. & Mostow, J. (2008). A Case Study Empirical Comparison of Three
Methods. 9th international conference on Intelligent Tutoring Systems, June
23-27, Montreal Canada.
Bienkowski, M., Feng M. & Means B. (2012). Enhancing Teaching and Learning
Through Educational Data Mining and Learning Analytics: An Issue Brief,
Available in: http://www.cra.org/ccc/files/docs/learning-analytics-ed.pdf.
Bogard, M. (2013). A Data Driven Analytic Strategy for Increasing Yield and
Retention at Western Kentucky University Using SAS Enterprise BI and SAS
Enterprise Miner. Available in: http://support.sas.com/resources/papers/
proceedings13/044-2013.pdf.
Bogard, M., James, C., Helbig, T. & Huff, G. (2012). Using SAS® Enterprise BI
and SAS®Enterprise Miner TM to Reduce Student Attrition. SAS Conference
Proceedings: SAS Global Forum 2012, April 22-25, Orlando, Florida.
Burez, J. & Van den Poel, D. (2007). Using analytical models to reduce customer
attrition by targeted marketing for subscription services. Expert Systems with
Applications, 32(2): 277–288.
Buttle, F. (2008). Customer Relationship Management, UK, Routledge.
Campbell, J.P., Oblinger, D.G. (2007). Academic Analytics. Available in:
http://net.educase.edu/ir/library/pdf/PUB6101.pdf.
Dekker, G.W., Pechenizkiy, M. & Vleeshouwers J.M. (2009). Predicting student
drop out: A case study. 2nd International Educational Data Mining
Conference, July 1-3, Cordoba Spain.
Delavari, N., Phon-Amnuaisuk, S., Beikzadeh, M. R. (2008). Data Mining Application in Higher Learning. Informatics in Education, 7(1): 31–54.
Delen, D. (2010). A comparative analysis of machine learning techniques for student
retention management. Decision Support Systems, 49 (4): 498-506.
Eesaee Cheshme Mahi., M., Bagherian., F., & Hasanzadeh Tavakkoli., M.R. (2011). Relation between personality dimensions with students' academic performance. Biennial Journal of Management and Planning in Educational Systems, 4, 6, 115-130.
 Gagolewski, M., Bartoszuk, M. and Cena, A. (2016). Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm. Information Sciences.
Gagolewski, M., Bartoszuk, M., & Cena, A. (2016). Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm. Information Sciences, 363, 8–2.
García, E., Romero, C., Ventura, S. & Castro, C. (2011). A collaborative educational
association rules mining tool. The Internet and Higher Education, 14(2): 77-88.
Goyal, M., and Vohra, R. (2012). Applications of Data Mining in Higher Education. International Journal of Computer Science Issues, 9, 2(1): 113-21.
Hadden, J., Tiwari, A., Roy, R. & Ruta, D. (2007). Computer assisted customer
churn management: State-of-the-art and future trends. Computers and
Operations Research, 34(10): 2902–2917.
Herzog, S. (2005). Measuring Determinants of Student Return VS. Dropout/ Stopout
VS. Transfer: A First-to-Second Year Analysis of New Freshmen. Research
in Higher Education, 46(8): 883–928.
Honigman, B. (2013). 5 Secrets to Increasing Customer Retention -- and Profits,
Available in: http://www.entrepreneur.com/article/227946
Kabakchieva, D. (2013). Predicting Student Performance by Using Data Mining
Methods for Classification. Cybernetics and Information Technologies, 13(1): 61–72.
Kabakchieva, D., Stefanova, K. & Kisimov, V. (2011). Analyzing University Datafor Determining Student Profiles and Predicting Performance, 4th International Conference on Educational Data Mining, Eindhoven, July 6-8, The Netherlands.
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G. & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3): 950-965.
Murphy, E.C. & Murphy, M.A. (2013). Leading on the Edge of Chaos, John Wiley
& Sons, Canada. Available in: http://www.impactlearning.com/resources
/metrics /customer-retention.
Namni, A. & Talepasand, S. (2016). Academic Achievement Predicting Model for Admission in Master Levels Case Study: E-Learning Center for Iran University of Science and Technology. Journal of Management and Planning in Educational System, 8, 15, 71-82.
Nandeshwar, A. & Chaudhari, S. (2009). Enrollment prediction models using data
mining. Available in: http://nandeshwar.info/wp-content/uploads/2008/11/
DM WVU_Project.pdf.
Nandeshwar, A., Menzies, T. & Nelson, A. (2011). Learning patterns of university
student retention. Expert Systems with Applications, 38(12): 14984–14996.
Náplava, P. & Šnorek, M. (2003). Modeling of Student's Quality by Means of GMDH Algorithms. System Analysis Modeling Simulation, 43(10): 1415- 1426.
Pechenizkiy, M., Calders, T., Vasilyeva, E. & Bra, P. D. (2008). Mining the student assessment data: Lessons drawn from a small scale case study. in 1st International Educational Data Mining Conference (EDM2008). Montreal Canad, June 20-21 2008.
Pittman, K. (2008). Comparison of data mining techniques used to predict student retention. Ph.D. thesis, Nova Southeastern University, USA.
Rokach, Lior & Oded Maimon (2005). "Clustering methods." Data mining and knowledge discovery handbook. Springer US, 2005. 321-352.
Romero, C. & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1): 135–146.
Romero, C. & Ventura, S. (2007). Educational data mining: A survey from 1995 to
Romero, C. (2008). Data mining algorithms to classify students. in 1st International Educational Data Mining Conference (EDM2008), June 20-21 Canada.
Romero, C. and Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135–146.
Romero, C., Ventura, S., Barnes, T. & Desmarais, M. (2009). Recommendation in higher education using data mining techniques, 2nd International Conference on Educational Data Mining, EDM 2009, July 1-3, Cordoba Spain.
Sabaghian, Z. & Pourkazemi., M. (2009). The Comparison of University Students Who Had Consecution In Their Field of Study At M.S. Level, With Those Who Had Not In Terms of Their Educational Success. Journal of Management and Planning in Educational Systems, 1 2, 7-21.
Safapour, P., (2014). Application of Data Mining In Higher Education, National Conference of Computer Engineering and Information Technology, Islamic Azad University, Shoshtar Branch.
Scott, G., Shah, M., Grebennikov, L. & Singh, H. (2008). Improving student retention: A University of Western Sydney case study. Journal of Institutional Research, 14(1): 9–23.
Smith, W. (2005). Applying Data Mining to Scheduling Courses at a University. Communications of the Association for Information Systems, 16, 463-479.
Sujitparapitaya, S. (2006). Considering student mobility in retention outcomes. New Direction for Institutional Research, 131, 35-51.
Superby, J., Vandamme, J. & Meskens, N. (2006). Determination of factors influencing the achievement of the first-year university students using data mining methods. Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), Hong kong, 37-44.
Talebi, M. (2014). Decision Support System for Faculty Managers for Lessen Presentation via using Data mining (Case study: Bachelor Degree of Payam Noor University, Qom Branch), Master Thesis, Payam Noor University, Tehran Branch, Computer and Information Technology.
Tantuco N. & Uy R. (2014). Creating Long-term Loyalty Relationships. Available in:http://catalogue.pearsoned.co.uk/assets/hip/gb/hip_gb_pearsonhighered/samplechapter/0273755021.pdf.
Tari, M. (2012). Effectiveness Factors in Higher Education Management, Payam Noor University- Tehran Branch- Department of Computer and Information Technology, Master Thesis.
Toyserkani, M. & Nasiri, M. (2012). Period of Student Graduation Prediction via Using Data Mining, First National conference of Scholars of Computer and Information Technology, Tabriz university, Tabriz.

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