Fuzzy analytical network process logic for performance measurement system of e-learning centers of universities

Document Type: Research Paper

Authors

School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

This paper proposes an efficient performance measurement system to evaluate the excellence of e-learning centers of universities. The proposed system uses the analytic network process (ANP) as an effective multi-criteria decision making (MCDM) method and its fuzzy mode to respond to uncertainties in judgements. This system also needs a targeted and systematic criteria set which is collected through comprehensive literature studies and experiences of faculty members.The performance of e-learning centers can then be systemically measured and managed by finding the relationship between these criteria, comparing the pairwise of criteria together and gaining their importance under uncertainty. In this paper, eight main criteria and twenty-five sub criteria is identified by a comprehensive survey on a statistical community consist of faculty members, staff and students of e-learning centers. Based on the results, the criteria for measuring university performance are mainly "student, teacher, educational content, communication, research, scheduling, continuous improvement and infrastructure." From the results of the final weights obtained, the "master's attitude toward the course" is most important in measuring performance. The sub-criterion of "attracting student participation by the master" has the next important place as well. The subcategory of the need for learning, the interest of  the interfere in education, and the future prospects of the student future are in the subsequent degree of importance.

Keywords

Main Subjects


Abdollahian, Z., Abdollahian, V., & Abodollahian, M. (2012). Prioritization of entrepreneurial entrepreneurial skills by FANP method. National Conference on Entrepreneurship and Knowledge Based Business.

Ahmad, K., & Mohamed Zabri, S. (2016). The application of non-financial performance measyrement in Malaysian manufacturing firm. Procedia, 35, 476-484.

Arman, M., Mozhdeyi, S., Hossein Beigi, A., & Rabiye, M. (2010). Extending Approximate Approaches to Determine the Relative and Final Weights of alternatives in fuzzy ahp. 7th conference of management. Tehran.

Asghari Zadeh, E., & Mohamed poor, M. (2007). Performance evaluation of the Telecommunication Research Center improved with balanced scorecard method with multi-index utility theory. Third Performance Management Conference.

Balabonienca, I., & Vecerskienah, G. (2014). The peculiarities of performance measurement in universities. 19th international scientific conference, economic and management (ICEM), (pp. 23-25).

Balaboniene, I., & Vecerskiene, G. (2015, december 1). The Aspects of Performance Measurement in Public Sector Organization. Procedia - social and behavioral sciences, 213, 314-320.

Basir Abyaneh, M., & Jafari, D. (2014). Performance evaluation of the production unit using a fuzzy multi-index decision-fusion integrated approach and a balanced scorecard model. Industrial Engineering Research Conferenc.

Bozóki,, S., & Rapcsák,, T. (2008). On Saaty’s and Koczkodaj ’s inconsistencies of pairwise comparison matrices1. Journal of global operation, 42, 157-175.

Brug. (2013). refrence of key performance measurement indicators. (M. Ghara Daghi, & H. Sanei, Trans.) Ariana Ghalam.

Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39, 3000-3011.

Cavalluzzoa, K., & Ittne, C. (2004, april-may). Implementing performance measurement innovations: evidence from government. acounting, organization and society, 29(3-4), 243-267.

Dagiliene, L., & Mykolaitiene, V. (2015). Disclosure of social responsibility in annual performance reports of universities. 20th international scientific coference economics and management (icem).

Degdeviren, M., Yuksel, I., & Kurt, M. (2008). A fuzzy network process (AHP) model to identify faulty behavior (FBR) in work system. safety science, 771-783.

Dewangan, V., & Godse, M. (2014, september). Towards a holistic enterprise innovation performance measurement system. Technovation, 34(9), 536-545.

Esmaeili, M., Seyedi, S., & ranban, S. (2013). Determining maintenance strategy by using Fuzzy Group MADM approach. Natural and Social Sciences, 2, 2639-2647.

Habibi, A., Izadyar, S., & Sarafrazi, A. (2014). fuzzy multi criteria decision making. Katibh Gil.

Heydari, M., Ghorbani Doolat Abadi, M., & Hashemi, S. A. (2016). University performance evaluation using Balanced Scorecard Model. First International Conference on Intelligent Business and Organizations - New Paradigms of Management.

Hosein pour, R. (2016). A Comparative Study on the Evaluation and Ranking of Universities and Research Centers in Different Countries, Guidelines for Assessing the Performance of Iranian Universities. The first international conference on modern research in the field of education and psychology and social welfare of Iran. doi:ESCONF01_263

Jam Barazmi, M., & Hossein zadeh, M. (2011). An Approach to Evaluating the Function of Electronic Learning Systems: A Fuzzy Analytical Hierarchy Process Analytical Approach and Important Factors of Success. 6th National Conference and 3rd International Conference on Electronic Learning and Learning.

Kapetaniou, C., & Hee Lee, S. (2016). A framework for assessing the performance of universities: The case of Cyprus. technological forecasting and social change, 1-12.

Mehregan, M., & Dabaghi, A. (2013). Development of a comprehensive method for decision making of uncertain multipliers based on gray-link analysis. Public Management Research, 24, 5-25.

Micheli, P., & Mari, L. (2014, june). The theory and practice of performance measuremen. management acounting research, 25(2), 147-156.

Mohammadi, A., & Molayi, N. (2011). Application of gray multi-criteria decision making in evaluating the performance of companies. Industrial management, 4, 125-142.

Motaki, N., & Kamach, O. (2017). ERP selection: A step-by-step application of AHP Method. International Journal of Computer Applications (0975 – 8887), 176, 15-21.

Pilevari, N., Hasanzadeh, M., & Shahriari, M. (2016). A hybrid fuzzy multiple attribute decision making approach for identificaton and ranking in supply chain: real case of Steel industry. Int. J. Industrial Mathematics, 8, 49-63.

Rahimi, G. (2006). performance evaluation and organization continous improvement. Tadbir, 173.

Ramezani, M. (2010). Decisions under uncertainty and fuzzy management. Payam Noor Sarakhs University of Technology.

Richetta, P., & Thurachon, W. (2012, april). Applying Fuzzy Analytic Hierarchy Process to Evaluate and Select Product of Notebook Computers. International Journal of Modeling and Optimization, 2.

Saaty, T. (2001). Decision Making with Dependence and Feedback: The Analytic Network Process (Second ed.). Pittsburgh: RWS Publications.

Saaty, T. L., & Tran, L. T. (2007). On the Invalidity of Fuzzifying Numerical Judgments in the Analytic Hierarchy Process. ISAHP 2007, Viña del Mar, Chile, 1-17.

Sehat, S., & Parizadi, I. (2009). Applying the Technique of Network Analysis Process to Analyzing Strengths, Weaknesses, Opportunities and Threats (Case Study of Iran Insurance Company). Industrial Management First Aid, 2, 105-120.

Selim, H. (2007). Critical success factor for e-learning acceptance: confirmatory factor models. computers & Education, 49, 396-413.

Soon, C. (2012). Presentation of performance evaluation model by integration of AHP fuzzy methods, TOPSIS. Scientific Journal of Promotionists, 29.

Sun, P.-C., Tsai, R., Finger, G., & Chen, Y.-Y. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183-1202.

Tesfamariam, S., & Sadiq, R. (2006, November). Risk-based environmental decision-making using fuzzy analytic hierarchy process. Stochastic Environmental Research and Risk Assessment, 21, 35-50.

Urdan, T. A., & Weggen, C. C. (2000). Corporate e-learning: exploring a new frontier.

Vahidnia, M., lesheikh, A., Alimohammadi, A., & Bassiri, A. (2008). FUZZY ANALYTICAL HIERARCHY PROCESS IN GIS APPLICATION. Remote Sensing and Spatial Information Sciences, XXXVII PART B2, 593-596.

Wang, X., Liu, Z., & Cai, Y. (2015). A rating based fuzzy analytic network process (F-ANP) model for evaluation of ship maneuverability. Ocean Engineering, 106, 39-46.

Yousif, M., & Shaout, A. (2016). Fuzzy logic computational model for performance evaluation of Sudanese Universities and academic staff. Journal of King Saud University Computer and Information Sciences, 1-39.

Yüksel, I., & Dag˘deviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37, 1270-1278.

Zebardast, E. (2010). Application of Network Analysis Process in Urban and Regional Planning. Fine Arts and Architecture, 41, 79-90.

 

 

 
 

 

 

 

Abdollahian, Z., Abdollahian, V., & Abodollahian, M. (2012). Prioritization of entrepreneurial entrepreneurial skills by FANP method. National Conference on Entrepreneurship and Knowledge Based Business.

Ahmad, K., & Mohamed Zabri, S. (2016). The application of non-financial performance measyrement in Malaysian manufacturing firm. Procedia, 35, 476-484.

Arman, M., Mozhdeyi, S., Hossein Beigi, A., & Rabiye, M. (2010). Extending Approximate Approaches to Determine the Relative and Final Weights of alternatives in fuzzy ahp. 7th conference of management. Tehran.

Asghari Zadeh, E., & Mohamed poor, M. (2007). Performance evaluation of the Telecommunication Research Center improved with balanced scorecard method with multi-index utility theory. Third Performance Management Conference.

Balabonienca, I., & Vecerskienah, G. (2014). The peculiarities of performance measurement in universities. 19th international scientific conference, economic and management (ICEM), (pp. 23-25).

Balaboniene, I., & Vecerskiene, G. (2015, december 1). The Aspects of Performance Measurement in Public Sector Organization. Procedia - social and behavioral sciences, 213, 314-320.

Basir Abyaneh, M., & Jafari, D. (2014). Performance evaluation of the production unit using a fuzzy multi-index decision-fusion integrated approach and a balanced scorecard model. Industrial Engineering Research Conferenc.

Bozóki,, S., & Rapcsák,, T. (2008). On Saaty’s and Koczkodaj ’s inconsistencies of pairwise comparison matrices1. Journal of global operation, 42, 157-175.

Brug. (2013). refrence of key performance measurement indicators. (M. Ghara Daghi, & H. Sanei, Trans.) Ariana Ghalam.

Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39, 3000-3011.

Cavalluzzoa, K., & Ittne, C. (2004, april-may). Implementing performance measurement innovations: evidence from government. acounting, organization and society, 29(3-4), 243-267.

Dagiliene, L., & Mykolaitiene, V. (2015). Disclosure of social responsibility in annual performance reports of universities. 20th international scientific coference economics and management (icem).

Degdeviren, M., Yuksel, I., & Kurt, M. (2008). A fuzzy network process (AHP) model to identify faulty behavior (FBR) in work system. safety science, 771-783.

Dewangan, V., & Godse, M. (2014, september). Towards a holistic enterprise innovation performance measurement system. Technovation, 34(9), 536-545.

Esmaeili, M., Seyedi, S., & ranban, S. (2013). Determining maintenance strategy by using Fuzzy Group MADM approach. Natural and Social Sciences, 2, 2639-2647.

Habibi, A., Izadyar, S., & Sarafrazi, A. (2014). fuzzy multi criteria decision making. Katibh Gil.

Heydari, M., Ghorbani Doolat Abadi, M., & Hashemi, S. A. (2016). University performance evaluation using Balanced Scorecard Model. First International Conference on Intelligent Business and Organizations - New Paradigms of Management.

Hosein pour, R. (2016). A Comparative Study on the Evaluation and Ranking of Universities and Research Centers in Different Countries, Guidelines for Assessing the Performance of Iranian Universities. The first international conference on modern research in the field of education and psychology and social welfare of Iran. doi:ESCONF01_263

Jam Barazmi, M., & Hossein zadeh, M. (2011). An Approach to Evaluating the Function of Electronic Learning Systems: A Fuzzy Analytical Hierarchy Process Analytical Approach and Important Factors of Success. 6th National Conference and 3rd International Conference on Electronic Learning and Learning.

Kapetaniou, C., & Hee Lee, S. (2016). A framework for assessing the performance of universities: The case of Cyprus. technological forecasting and social change, 1-12.

Mehregan, M., & Dabaghi, A. (20013). Development of a comprehensive method for decision making of uncertain multipliers based on gray-link analysis. Public Management Research, 24, 5-25.

Micheli, P., & Mari, L. (2014, june). The theory and practice of performance measuremen. management acounting research, 25(2), 147-156.

Mohammadi, A., & Molayi, N. (2011). Application of gray multi-criteria decision making in evaluating the performance of companies. Industrial management, 4, 125-142.

Motaki, N., & Kamach, O. (2017). ERP selection: A step-by-step application of AHP Method. International Journal of Computer Applications (0975 – 8887), 176, 15-21.

Pilevari, N., Hasanzadeh, M., & Shahriari, M. (2016). A hybrid fuzzy multiple attribute decision making approach for identificaton and ranking in supply chain: real case of Steel industry. Int. J. Industrial Mathematics, 8, 49-63.

Rahimi, G. (2006). performance evaluation and organization continous improvement. Tadbir, 173.

Ramezani, M. (2010). Decisions under uncertainty and fuzzy management. Payam Noor Sarakhs University of Technology.

Richetta, P., & Thurachon, W. (2012, april). Applying Fuzzy Analytic Hierarchy Process to Evaluate and Select Product of Notebook Computers. International Journal of Modeling and Optimization, 2.

Saaty, T. (2001). Decision Making with Dependence and Feedback: The Analytic Network Process (Second ed.). Pittsburgh: RWS Publications.

Saaty, T. L., & Tran, L. T. (2007). On the Invalidity of Fuzzifying Numerical Judgments in the Analytic Hierarchy Process. ISAHP 2007, Viña del Mar, Chile, 1-17.

Sehat, S., & Parizadi, I. (2009). Applying the Technique of Network Analysis Process to Analyzing Strengths, Weaknesses, Opportunities and Threats (Case Study of Iran Insurance Company). Industrial Management First Aid, 2, 105-120.

Selim, H. (2007). Critical success factor for e-learning acceptance: confirmatory factor models. computers & Education, 49, 396-413.

Soon, C. (2012). Presentation of performance evaluation model by integration of AHP fuzzy methods, TOPSIS. Scientific Journal of Promotionists, 29.

Sun, P.-C., Tsai, R., Finger, G., & Chen, Y.-Y. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183-1202.

Tesfamariam, S., & Sadiq, R. (2006, November). Risk-based environmental decision-making using fuzzy analytic hierarchy process. Stochastic Environmental Research and Risk Assessment, 21, 35-50.

Urdan, T. A., & Weggen, C. C. (2000). Corporate e-learning: exploring a new frontier.

Vahidnia, M., lesheikh, A., Alimohammadi, A., & Bassiri, A. (2008). FUZZY ANALYTICAL HIERARCHY PROCESS IN GIS APPLICATION. Remote Sensing and Spatial Information Sciences, XXXVII PART B2, 593-596.

Wang, X., Liu, Z., & Cai, Y. (2015). A rating based fuzzy analytic network process (F-ANP) model for evaluation of ship maneuverability. Ocean Engineering, 106, 39-46.

Yousif, M., & Shaout, A. (2016). Fuzzy logic computational model for performance evaluation of Sudanese Universities and academic staff. Journal of King Saud University Computer and Information Sciences, 1-39.

Yüksel, I., & Dag˘deviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37, 1270-1278.

Zebardast, E. (2010). Application of Network Analysis Process in Urban and Regional Planning. Fine Arts and Architecture, 41, 79-90.

 

 

 

 

 

 

 

Abdollahian, Z., Abdollahian, V., & Abodollahian, M. (2012). Prioritization of entrepreneurial entrepreneurial skills by FANP method. National Conference on Entrepreneurship and Knowledge Based Business.

Ahmad, K., & Mohamed Zabri, S. (2016). The application of non-financial performance measyrement in Malaysian manufacturing firm. Procedia, 35, 476-484.

Arman, M., Mozhdeyi, S., Hossein Beigi, A., & Rabiye, M. (2010). Extending Approximate Approaches to Determine the Relative and Final Weights of alternatives in fuzzy ahp. 7th conference of management. Tehran.

Asghari Zadeh, E., & Mohamed poor, M. (2007). Performance evaluation of the Telecommunication Research Center improved with balanced scorecard method with multi-index utility theory. Third Performance Management Conference.

Balabonienca, I., & Vecerskienah, G. (2014). The peculiarities of performance measurement in universities. 19th international scientific conference, economic and management (ICEM), (pp. 23-25).

Balaboniene, I., & Vecerskiene, G. (2015, december 1). The Aspects of Performance Measurement in Public Sector Organization. Procedia - social and behavioral sciences, 213, 314-320.

Basir Abyaneh, M., & Jafari, D. (2014). Performance evaluation of the production unit using a fuzzy multi-index decision-fusion integrated approach and a balanced scorecard model. Industrial Engineering Research Conferenc.

Bozóki,, S., & Rapcsák,, T. (2008). On Saaty’s and Koczkodaj ’s inconsistencies of pairwise comparison matrices1. Journal of global operation, 42, 157-175.

Brug. (2013). refrence of key performance measurement indicators. (M. Ghara Daghi, & H. Sanei, Trans.) Ariana Ghalam.

Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39, 3000-3011.

Cavalluzzoa, K., & Ittne, C. (2004, april-may). Implementing performance measurement innovations: evidence from government. acounting, organization and society, 29(3-4), 243-267.

Dagiliene, L., & Mykolaitiene, V. (2015). Disclosure of social responsibility in annual performance reports of universities. 20th international scientific coference economics and management (icem).

Degdeviren, M., Yuksel, I., & Kurt, M. (2008). A fuzzy network process (AHP) model to identify faulty behavior (FBR) in work system. safety science, 771-783.

Dewangan, V., & Godse, M. (2014, september). Towards a holistic enterprise innovation performance measurement system. Technovation, 34(9), 536-545.

Esmaeili, M., Seyedi, S., & ranban, S. (2013). Determining maintenance strategy by using Fuzzy Group MADM approach. Natural and Social Sciences, 2, 2639-2647.

Habibi, A., Izadyar, S., & Sarafrazi, A. (2014). fuzzy multi criteria decision making. Katibh Gil.

Heydari, M., Ghorbani Doolat Abadi, M., & Hashemi, S. A. (2016). University performance evaluation using Balanced Scorecard Model. First International Conference on Intelligent Business and Organizations - New Paradigms of Management.

Hosein pour, R. (2016). A Comparative Study on the Evaluation and Ranking of Universities and Research Centers in Different Countries, Guidelines for Assessing the Performance of Iranian Universities. The first international conference on modern research in the field of education and psychology and social welfare of Iran. doi:ESCONF01_263

Jam Barazmi, M., & Hossein zadeh, M. (2011). An Approach to Evaluating the Function of Electronic Learning Systems: A Fuzzy Analytical Hierarchy Process Analytical Approach and Important Factors of Success. 6th National Conference and 3rd International Conference on Electronic Learning and Learning.

Kapetaniou, C., & Hee Lee, S. (2016). A framework for assessing the performance of universities: The case of Cyprus. technological forecasting and social change, 1-12.

Mehregan, M., & Dabaghi, A. (20013). Development of a comprehensive method for decision making of uncertain multipliers based on gray-link analysis. Public Management Research, 24, 5-25.

Micheli, P., & Mari, L. (2014, june). The theory and practice of performance measuremen. management acounting research, 25(2), 147-156.

Mohammadi, A., & Molayi, N. (2011). Application of gray multi-criteria decision making in evaluating the performance of companies. Industrial management, 4, 125-142.

Motaki, N., & Kamach, O. (2017). ERP selection: A step-by-step application of AHP Method. International Journal of Computer Applications (0975 – 8887), 176, 15-21.

Pilevari, N., Hasanzadeh, M., & Shahriari, M. (2016). A hybrid fuzzy multiple attribute decision making approach for identificaton and ranking in supply chain: real case of Steel industry. Int. J. Industrial Mathematics, 8, 49-63.

Rahimi, G. (2006). performance evaluation and organization continous improvement. Tadbir, 173.

Ramezani, M. (2010). Decisions under uncertainty and fuzzy management. Payam Noor Sarakhs University of Technology.

Richetta, P., & Thurachon, W. (2012, april). Applying Fuzzy Analytic Hierarchy Process to Evaluate and Select Product of Notebook Computers. International Journal of Modeling and Optimization, 2.

Saaty, T. (2001). Decision Making with Dependence and Feedback: The Analytic Network Process (Second ed.). Pittsburgh: RWS Publications.

Saaty, T. L., & Tran, L. T. (2007). On the Invalidity of Fuzzifying Numerical Judgments in the Analytic Hierarchy Process. ISAHP 2007, Viña del Mar, Chile, 1-17.

Sehat, S., & Parizadi, I. (2009). Applying the Technique of Network Analysis Process to Analyzing Strengths, Weaknesses, Opportunities and Threats (Case Study of Iran Insurance Company). Industrial Management First Aid, 2, 105-120.

Selim, H. (2007). Critical success factor for e-learning acceptance: confirmatory factor models. computers & Education, 49, 396-413.

Soon, C. (2012). Presentation of performance evaluation model by integration of AHP fuzzy methods, TOPSIS. Scientific Journal of Promotionists, 29.

Sun, P.-C., Tsai, R., Finger, G., & Chen, Y.-Y. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183-1202.

Tesfamariam, S., & Sadiq, R. (2006, November). Risk-based environmental decision-making using fuzzy analytic hierarchy process. Stochastic Environmental Research and Risk Assessment, 21, 35-50.

Urdan, T. A., & Weggen, C. C. (2000). Corporate e-learning: exploring a new frontier.

Vahidnia, M., lesheikh, A., Alimohammadi, A., & Bassiri, A. (2008). FUZZY ANALYTICAL HIERARCHY PROCESS IN GIS APPLICATION. Remote Sensing and Spatial Information Sciences, XXXVII PART B2, 593-596.

Wang, X., Liu, Z., & Cai, Y. (2015). A rating based fuzzy analytic network process (F-ANP) model for evaluation of ship maneuverability. Ocean Engineering, 106, 39-46.

Yousif, M., & Shaout, A. (2016). Fuzzy logic computational model for performance evaluation of Sudanese Universities and academic staff. Journal of King Saud University Computer and Information Sciences, 1-39.

Yüksel, I., & Dag˘deviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37, 1270-1278.

Zebardast, E. (2010). Application of Network Analysis Process in Urban and Regional Planning. Fine Arts and Architecture, 41, 79-90.