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مقایسه مدل های سنجش کیفیت خدمات در ارزیابی دانشجویان از کیفیت فرایند تدریس و یادگیری با استفاده از شبکۀ عصبی مصنوعی | ||
| تحقیقات اقتصاد و توسعه کشاورزی ایران | ||
| مقاله 8، دوره 45، شماره 4، دی 1393، صفحه 663-672 اصل مقاله (652.41 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijaedr.2014.53840 | ||
| نویسندگان | ||
| سید یوسف حجازی* 1؛ فاطمه رجبیان غریب2؛ محمود امید3 | ||
| 1استاد دانشکدة اقتصاد و توسعة کشاورزی، دانشگاه تهران | ||
| 2دانشجوی کارشناسی ارشد آموزش کشاورزی، دانشگاه تهران | ||
| 3استاد دانشکدة مهندسی فناوری کشاورزی، دانشگاه تهران | ||
| چکیده | ||
| هدف از این مطالعه تعیین و ارزیابی کمی موقعیت کیفی فرایند تدریس و یادگیری در مراکز آموزش عالی کشاورزی است. به این منظور، از توانمندی شبکههای عصبی مصنوعی در مدل‌سازی روابط غیر خطی، برای بررسی و ارزیابی مدل‌های مختلف سنجش کیفیت خدمات استفاده شد. جامعة آماری، دانشجویان تحصیلات تکمیلی رشته‌های کشاورزی دانشگاه فردوسی مشهد (1070نفر) است که با استفاده از جدول مورگان 280 پرسشنامه جمع‌آوری شد و درنهایت 202 پرسشنامه تجزیه و تحلیل شد. به‌منظور بررسی و ارزیابی کیفیت فرایند تدریس و یادگیری از چهار مدل سروپروف غیر وزنی، سروکوآل غیر وزنی، سروپرف وزنی و سروکوآل وزنی به کمک شبکههای عصبی مصنوعی استفاده شد. نتایج به‌کارگیری رویکرد شبکههای عصبی مصنوعی نشان داد مدل سروکوآل وزنی با دقت بیشتری قادر به ارزیابی کیفیت تدریس و پیش‌بینی رضایت است. این مدل با معماری 7-29-14-1 یعنی 7 نرون در لایة ورودی، 29 و 14 نرون در لایه‌های مخفی اول و دوم و یک نرون در لایة خروجی، به‌عنوان بهترین راه حل برای تخمین ارزیابی کیفیت انتخاب شد. این معماری دارای ضریب همبستگی 96/0 بود و مقایر MAE، MSE و MAPE آن به‌ترتیب 18/0، 06/0 و 41/4 درصد داشتند. | ||
| کلیدواژه‌ها | ||
| رضایتمندی دانشجویان؛ شبکة عصبی مصنوعی؛ کیفیت آموزش؛ مدل سروکوآل وزنی | ||
| عنوان مقاله [English] | ||
| Comparison of services quality assessment models between students from teaching and learning process quality by using artificial neural network | ||
| نویسندگان [English] | ||
| Seyed Yousef Hejazi1؛ Fatemeh Rajabiyan Gharib2؛ Mahmood Omid3 | ||
| 1Professor, MSc. Student Education, Professor, Faculty of Agricultural Engineering and Technonogy, University of Tehran, Iran | ||
| 2Professor, MSc. Student Education, Professor, Faculty of Agricultural Engineering and Technonogy, University of Tehran, Iran | ||
| 3Professor, MSc. Student Education, Professor, Faculty of Agricultural Engineering and Technonogy, University of Tehran, Iran | ||
| چکیده [English] | ||
| The aim of this study is quantity determining and evaluating of the quality position of teaching and learningprocess. So, the artificial neural networks were used for modeling nonlinear relationships to inspect and evaluate different models of service quality evaluation. The statistical population include 1070 peoples, which were master and PhD students of Faculty of Agriculture of FerdowsiUniversity (Mashhad). 280 questionnaires were collected by using Morgan table; from which 202 questionnaires were finally analyzed. In order to inspect and evaluate the quality of teaching and learning process, four models were used with artificial neural networks, including non-weighted Servprof, non-weighted Servqual, weighted Servprof and weighted Servqual. The results of the artificial neural network method showed that weighted Servqual model is more accurate to evaluate the quality of teaching and to predict satisfactory. 7-29-14-1 architecture with 29 and 14 neurons respectively in the first and second hidden layers and one neuron (weighted Servqual) in the output layer was chosen as the best model for determining the quality evaluation. This architecture has the best results for R (0.96), MAE (0.18), MSE (0.06) and MAPE (4.41%) between the actual and modeled values. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Neural Networks, quality of teaching, Student satisfaction, weighted Servqual model | ||
| مراجع | ||
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Abzari, M., Mansouri, H. & Vahedi, S. (2011). Studying and assessing of electrical power industry services quality evaluate models. Proceedings of Twenty-Sixth International Conference on Electricity. October 31 & Novenber 1-2, 2011, Tehran, Iran,(In Farsi).
Aghamolaei T., Zare S., & Abedini S. (2006). The quality gap of educational services from the point of view of students in Hormozgan university of medical sciences. Strides in Development of Medical Education,3, (2), 78-85(In Farsi).
Ahmad, Shaukat, .M.Z., Usman, A., Rehman, W., & Ahmed, N. (2010). Does service quality affect students’ performance? Evidence from institute of higher learning. African Journal of Business Management. 4, (12), 2527-2533.
Aldridge, S., & Rowley, J. (1998). Measuring customer satisfaction in higher education, Quality Assurance in Education, 6, ( 4), 197-204.
Asubonteng, P., & McCleary K. J. (1996). Servequal revisited: a critical review Of service quality, The Journal of ServicesMarketing, 10, ( 6),62-81.
Bahraini, K., Shahalizadeh Kalkhoran, M.,& Nouraie, F.. (2009). Services Quality Study in Islamic Azad University based on SERVQUAL model and QFD (A Case Study, Islamic Azad University of Aliabad Katol). Journal of Management, 6, (11), 62-79.
Behara, R., Fisher, W.W., & Lemmink, G. A. M. (2002). Modeling and evaluating quality measurement using neural networks. International Journal of Operations & Production management, 22 (10), 1162 -1185.
Chang-long, W., Yan-ming, Q.(2009) Quality evaluation of universities undergraduate practice teaching work based on artificial neural network. Computational Intelligence and Natural Computing. International Conference on, 6-7 June,393-396.
Cronin, J. & Taylor, S. A. (1994). Servperf versus Servqual: Reconciling performance- minus- expectations measurement of service quality, Journal of Marketing, 58, (1) , 125 – 131.
Hayes, B.E. (1997). Measuring customer satisfaction ,Development and Use of Questionnaires Publisher, ASQ Quality, 2 Sub Edition.
Jaw deng, W., Chin Chen. W., & Pei, W. (2008). Back-Propagation Neural Network based Importance Performance Analysis for Determining Critical Service Attributes. Expert System with Applications. 34, 1115- 1125.
Jun-qiao, Q., Chang-long, W., Yan-ming, Q.(2009) The application on the evaluation of quality of universities undergraduate theory teaching work based on artificial neural network. Information Assurance and Security, Fifth International Conference on, 18-20 Aug,387-390.
Kebriaei A., & Roudbari M.( 2005). The quality gap in educational services at Zahedan university of medical sciences: Based on student’ perceptions and expectations. Iranian Journal of Medical Education, 5, (1), 53-61(In Farsi).
Levinthal, C.F., Lansky, L.M.,Andrews, S.(2001). Student evaluations of teacher behaviours as estimations of real ideal discrepancies: a critique of teacher rating methods, Journal of educational psychology,.62, ( 2), 104-109.
Lihua, L., Fuming, L., Changlong, W.(2009). Study on undergraduate teaching job quality assessment based on artificial Fish-BP neural network. Services Science, Management and Engineering, International Conference on. 11-12 July, 246-249.
Maroofi, Y., Kiamanesh, A., Mehr Mohammadi, M., & Ali Askari, M. (2007). Teaching Quality Assessment in Higher Education: Examine some Views. Journal of Curriculum Studies, 5 (In Farsi).
Marsh, H.W., Muthen, B.,Asparouhow, T.,Ludtke, O., Robitzsch, A., Morin, A., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA; application to student. Evaluations of University TeachingStructural Equation Modeling, 16, 439-476.
Mir Fakhroddini, H., Taheri Demne, M., & Mansouri, H. (2010). Artificial neural network a new method in measure service quality academic libraries, Journal of Librarianship and Information Science, 1, (13)(In Farsi).
Mir Ghafouri, H., Taheri, M., & Zare Ahmad Abadi, H. (2009). Services quality measuring methods evaluation by artificial neural networks. Management view, 31, 63-79 (In Farsi).
Munawarkhan, M., Ahmed, I., & Musarrat Nawaz, M.(2011). Student’s Perspective of Service Quality in Higher Learning Institutions; An evidence Based Approach, International Journal of Business and Social Science. 2, ( 11), 159-164.
Okumufi, A. & Duygun, A. (2008). Services quality measurement on education services marketing and relationship between perceived service quality and student satisfaction, Anadulu University Journal Of Social Sciences. 8 ,(2), 17–38.
Oliveira, o., & Ferrera ,e. (2009). Adaptation and application of the Servequal scale in higher education , POMS 20th Annual Conference,(May 1-4). Relationship Management Approach, 2nd ed., Wiley, Chichester.
Raoufi, Sh., Sheykhian, A., Ebrahimzadeh, F., Tarahi, M. J., & Ahmadi, P. (2010). Designing a New form of Theoretical Teaching Quality Evaluation based on Stakholder Perspectives and Six Principles of Classical Knowledge Research. Hormozgan Medical Journal, 3 (In Farsi).
Safari, S. (2011). Characteristics of the teaching – learning process in higher education, Journal of Engineering Education in Iran, 50, 90-73(In Farsi).
Shabani Varaki, B., Hosseingholizdeh, R.(2006). Investigation of the Teaching Quality in University, Journal of Research and Planning in Higher Education , 1, (39), 1-22(In Farsi).
Shabani Varaki, B., Javidi, T., & Farrokhzad, H. (2008). Evaluation of Teaching Quality in Higher Education Institutions Applied Science Agricultural Jihad”;Journal of Education and Development. 6 (In Farsi).
Stodnick, M., Rogers, P. (2008). Using Servqual to measure the quality of the classroom experience, Decision Sciences Journal of Innovative Education.1, (6), 115-133.
Wang, X., Xu, J.(2009). The model of teaching quality evaluation based on BP neural networks and Its application. Education Technology and Computer Science. First International Workshop on. 7-8 March,916-919.
Yan-ming, Q., Chang-long, W., & Kai,Y.(2009). Evaluation of classroom teaching quality in universities based on artificial neural network, Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on, 11-12 July,513-516. Yı lmaz, V., Filiz, Z., & Yaprak, B. (2007). Service quality measurement in the turkish higher education system with Servequal method, 7, )1), 299-316.
Zolfaghar, M., & Mehr Mohammadi, M.(2004). Student Evaluation of Teaching Quality of Human Sciences Faculty of Tehran Universities. Journal of Shahed University, 6 (In Farsi).
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