Document Type : Scientific - Research


1 Industrial Engineering Department, Engineering Faculty, sisatan and Baluchestan University, Zahedan, Iran

2 Industrial Engineering department, Engineering faculty, University of Sistan and Baluchestan, zahedan, Iran


Considering the role of universities in engineering education, it is necessary to study the status of the educational system and its strengths and weaknesses in order to improve the process of engineering education. In this research, the factors affecting the increase of academic years of engineering students and the status of students who do not complete their studies in the authorized years have been investigated. At first, the effective indicators on increasing the academic years of engineering students were identified and prioritized using the AHP technique. The ranking results show the average of basic courses, the average of main courses, the average of general courses, the number of conditional semesters, the average of optional courses and the number of units dropped have the most impact on increasing the academic years of engineering students. Then, a model was provided to predict the increase in academic years according to the educational status of engineering students using an artificial neural network. According to the results of the neural network, the factors of the number of units dropped, the average of the main courses, the average of the basic courses, the number of conditional terms, the duration of marriage and the average of the high school math and physics courses have the greatest impact on increasing academic years. Finally, by comparing the results of AHP method and neural network, the average factors of basic and main courses, the number of conditional terms and the number of units dropped in both methods were identified as the most effective factors that should be paid more attention while studying engineering students. 


Ahmadi, A., Khairatikazerooni. T. (2019). Data mining withdrawal of the students of Payame Noor University in Tehran state to increase student retention rate (Preventing customer rejection). Quarterly Journal of Public Organizations Management, 7 (2), 47-58 [in Persian].
Alfiani A., Wulandari F. (2015). Mapping student’s performance based on data mining approach (A case study), The 2014 International Conference on Agro-industry (ICoA): Competitive and sustainable Agro-industry for Human Welfare, 3. 173-177.
Asgharizadeh, A., Mohammadi Balani, A. (2020). Multi-characteristic decision-making techniques. Iranian Journal of University of Tehran Publishing Institute [in Persian].
Asif B., Merceron A., Abbas Ali S., Haide N. (2017). Analyzing undergraduate students’ performance using educational data mining, Iranian Journal of Computers & Education, 113, 177-194.
Burgos C., Campanario M., Peña D., Lara J., Lizcano D., Martínez M. (2018). Data mining for modeling students’performance; A tutoring action plan to prevent academic dropout, Computers and Electrical Engineering, 66, 541-556.
Changizy Ashtyani, S., Shamsi, M., & Mohammadbeygi, A. (2010). Frequency of educational decline and some effective factors of student’s opinion in Arak University of Medical Sciences. Arak Medical University Journal, 12 (4), 24-33 [in Persian].
Costa E., Fonseca B., Santana M., Araújo F., Rego J.  (2017). Evaluating the effectiveness of educational data mining techniques for early prediction sf students’ academic failure in introductory programming courses, Computers in Human Behavior, 73, 247-256.
Emamghorashi, F., Heydari, ST., & Najafipour, S. (2010). Evaluation of effecting factors on educational status of medical students in Jahrom medical university during 1994- 2003. Iranian Journal of Babol University of Medical Sciences, 12 (5), 40-45 [in Persian].
Er E. (2012). Identifying at-risk students using machine learning techniques: A case study with is 100. In International Journal of Machine Learning and Computing, 476-48.
Fernandes E., Holanda M., Victorino M., Borges V., Carvalho R., Van Erven G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil, Iranian Journal of Business Research, 94, 335-343.
Garkaz, M., Esmaili, H. (2012). Investigating the determinants of academic performance of undergraduate students accounting using neural network. Quarterly Journal of Educational Leadership and Administration, 6(1), 107-125 [in Persian].
Ghazanfari, M., Alizade, S., & Teimori, B. (2014). Data mining and knowledge discovery. Tehran: Iran University of Science and Technology Publications [in Persian].
Hasani, A., Bazrafshan, M. (2018). 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). Iranian Journal of Management and Planning in Educational Systems, 11(2), 187-208 [in Persian].
Heydari, S., Yaghini, M. (2011). Classification and prediction of students’ educational status using data mining techniques. Iranian Journal of Higher Education Letter, 12, 107-124 [in Persian].
Jawad J., Hawarib A., Zaidi S. (2020). Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux, Desalination, 484. 114427
Kaur P., Singh M., Singh Josan G. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector, 3rd International Conference on Recent Trends in Computing 2015(ICRTC-2015), 57, 500-508.
Koosha, H., Dangkoub, S., & Barzanooni, A. (2018). Application of data mining techniques to predict students’ mental health status to improve educational performance. Iranian Journal of Technology of Education, 13(2), 157-171 [in Persian].
Memarian, H., Memarian, A., & Mohasel Afshar, E. (2019). Investigating the reasons behind unmotivated engineering students. Iranian Engineering Education, Quarterly, 22 (86), 21-37 [in Persian].
Nameni, A., Fathian Boroojeni, M., & Ashrafi, L. (2018). A model for predicting academic vulnerability in neural network-based undergraduate, Iranian Journal of Educational planning studies, 7(13), 8-27 [in Persian].
Noorossana, R., Saghaei, A., Shadalouie, F., & Samimi, Y. (2008). Customer satisfaction measurement to identify areas for improvement in higher education research services.  Iranian Journal of Research and Planning in Higher Education, 14 (3), 97-119 [in Persian].
Rivas A., González-Briones A., Hernández G., Prieto J., Chamoso P. (2020). Artificial neural network analysis of the academic performance of students in virtual learning environments: Neurocomputing, In press, corrected proof Available online 8 May 2020
Rutkowski L., Jaworski M., Duda P. (2020). Basic concepts of data stream mining. In: Stream data mining: Algorithms and their probabilistic properties. Studies in Big Data, 56. Springer, Cham
Saati T., (1980). The analytic hierarchy process: Planning, priority setting, resource allocation, McGraw-Hill, New York.
Shahraki, M., Narouei, M. (2019). Evaluating the quality of educational services and satisfaction of engineering students based on SERVQUAL model and artificial neural network (Case study: Faculty of Engineering, Sistan and Baluchestan University). Iranian Engineering Education,Quarterly, 21(11), 73-91 [in Persian].
Yosefi, R., Gholami, A. (2012). An overview of data mining concepts. Iranian Journal of Brought light. 39, 10-15 [in Persian].