2,762
Views
1
CrossRef citations to date
0
Altmetric
Articles

Applying a fuzzy questionnaire in a peer review process

, &

References

  • Alfonso, G., López, R., de Hierro, A. F., & Roldán, C. (2016). A fuzzy regression model based on finite fuzzy numbers and its application to real-world financial data. Journal of Computational and Applied Mathematics, 24(2), 344–359.
  • Amini, S., & Jochem, R. (2011). A conceptual model based on the fuzzy set theory to measure and evaluate the performance of service processes. Paper presented at enterprise distributed object computing conference workshops (EDOCW), 29 Aug–2 Sept 2011, Helsinki, Finland, pp. 122–131.
  • Andayani, S., Hartati, S., Wardoyo, R., & Mardapi, D. (2017). Decision-making model for student assessment by unifying numerical and linguistic data. International Journal of Electrical and Computer Engineering, 7(1), 363–373.
  • Başaran, M. A., Kalaycı, N., & Atay, M. T. (2011). A novel hybrid method for better evaluation: Evaluating university instructors teaching performance by combining conventional content analysis with fuzzy rule based systems. Expert Systems with Applications, 38(10), 12565–12568. doi: 10.1016/j.eswa.2011.04.043
  • Battisti, F., Nicolini, G., & Salini, S. (2005). The Rasch model to measure service quality. The ICFAI Journal of Services Marketing, 3(3), 58–80.
  • Battisti, F., Nicolini, G., & Salini, S. (2010). The Rasch model in customer satisfaction survey data. Quality Technology & Quantitative Management, 7(1), 15–34. doi: 10.1080/16843703.2010.11673216
  • Berk, R. A. (2005). Survey of 12 strategies to measure teaching effectiveness. International Journal of Teaching and Learning in Higher Education, 17(1), 48–62.
  • Blackmore, J. A. (2005). A critical evaluation of peer review via teaching observation within higher education. International Journal of Educational Management, 19(3), 218–232.
  • Brent, R., & Felder, R. M. (2004). A protocol for peer review of teaching. Education designs, North Carolina State University, Session 3530.
  • Brochado, A. (2009). Comparing alternative instruments to measure service quality in higher education. Quality Assurance in Education, 17(2), 174–190. doi: 10.1108/09684880910951381
  • Büyüközkan, G., Ruan, D., & Feyzioğlu, O. (2007). Evaluating e-learning web site quality in a fuzzy environment. International Journal of Intelligent Systems, 22(5), 567–586. doi: 10.1002/int.20214
  • Cabrerizo, F. J., López-Gijón, J., Martínez, M. A., Morente-Molinera, J. A., & Herrera-Viedma, E. (2017). A fuzzy linguistic extended LibQUAL+ model to assess service quality in academic libraries. International Journal of Information Technology & Decision Making, 16(1), 225–244. doi: 10.1142/S0219622015500406
  • Calcagnì, A., & Lombardi, L. (2014). Dynamic fuzzy rating tracker (DYFRAT): A novel methodology for modeling real-time dynamic cognitive processes in rating scales. Applied Soft Computing, 24, 948–961. doi: 10.1016/j.asoc.2014.08.049
  • Carrasco, R. A., Villar, P., Hornos, M. J., & Herrera-Viedma, E. (2011). A linguistic multi-criteria decision making model applied to the integration of education questionnaires. International Journal of Computational Intelligence Systems, 4(5), 946–959. doi: 10.1080/18756891.2011.9727844
  • Chang, T. C., & Wang, H. (2016). A multi criteria group decision-making model for teacher evaluation in higher education based on cloud model and decision tree. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1243–1262.
  • Chen, T. C. (2001). Applying linguistic decision-making method to deal with service evaluation problems. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, 9(1), 103–114. doi: 10.1142/S0218488501001022
  • Courneya, C. A., Pratt, D. D., & Collins, J. (2008). Through what perspective do we judge the teaching of peers? Teaching and Teacher Education, 24, 69–79. doi: 10.1016/j.tate.2007.01.009
  • Deng, W. J. (2008). Fuzzy importance-performance analysis for determining critical service attributes. International Journal of Service Industry Management, 19(2), 252–270. doi: 10.1108/09564230810869766
  • de Sáa, S. D. L. R., Gil, MÁ, González-Rodríguez, G., López, M. T., & Lubiano, M. A. (2015). Fuzzy rating scale-based questionnaires and their statistical analysis. IEEE Transactions on Fuzzy Systems, 23(1), 111–126. doi: 10.1109/TFUZZ.2014.2307895
  • De Witte, K., & Rogge, N. (2011). Accounting for exogenous influences in performance evaluations of teachers. Economics of Education Review, 30(4), 641–653. doi: 10.1016/j.econedurev.2011.02.002
  • Dombi, J. (2008). Towards a general class of operators for fuzzy systems. IEEE Transactions on Fuzzy Systems, 16(2), 477–484. doi: 10.1109/TFUZZ.2007.905910
  • Dombi, J. (2009). Pliant arithmetics and pliant arithmetic operations. Acta Polytech Hun, 6(5), 19–49.
  • Ellis, L., Burke, D. M., Lomire, P., & McCormack, D. R. (2003). Student grades and average ratings of instructional quality: The need for adjustment. The Journal of Educational Research, 97(1), 35–40. doi: 10.1080/00220670309596626
  • Frühwirth-Schnatter, S. (1992). On statistical inference for fuzzy data with applications to descriptive statistics. Fuzzy Sets and Systems, 50, 143–165. doi: 10.1016/0165-0114(92)90213-N
  • Gil, M. Á., & González-Rodríguez, G. (2012). Fuzzy vs. Likert scale in statistics. In E. Trillas, P. P. Bonissone, L. Magdalena, & J. Kacprzyk (Eds.), Combining experimentation and theory (pp. 407–420). Berlin, Heidelberg: Springer.
  • Gil, M. Á., Lubiano, M. A., De Sáa, S. D. L. R., & Sinova, B. (2015). Analyzing data from a fuzzy rating scale-based questionnaire. A case study. Psicothema, 27(2), 182–191.
  • Hammed, I. A. (2011). Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems. Expert Systems with Applications, 38, 7135–7142. doi: 10.1016/j.eswa.2010.12.048
  • Hartley, J. (2014). Some thoughts on Likert-type scales. International Journal of Clinical and Health Psychology, 14(1), 83–86. doi: 10.1016/S1697-2600(14)70040-7
  • Herrera, F., & Herrera-Viedma, E. (2000). Choice functions and mechanisms for linguistic preference relations. European Journal of Operational Research, 120(1), 144–161. doi: 10.1016/S0377-2217(98)00383-X
  • Herrera, F., López, E., Mendana, C., & Rodríguez, M. A. (1999). Solving an assignment–selection problem with verbal information and using genetic algorithms. European Journal of Operational Research, 119(2), 326–337. doi: 10.1016/S0377-2217(99)00134-4
  • Hesketh, B., Pryor, R., Gleitzman, M., & Hesketh, T. (1988). Practical applications and psychometric evaluation of a computerized fuzzy graphic rating scale. Advances in Psychology, 56, 425–454. doi: 10.1016/S0166-4115(08)60493-8
  • Ihsan, A. K. A. M., Taib, K. A., Talib, M. Z. M., Abdullah, S., Husain, H., Wahab, D. A., … Abdul, N. A. (2012). Measurement of course evaluation for lecturers at the faculty of engineering and built environment. Procedia – Social and Behavioral Sciences, 60, 358–364. doi: 10.1016/j.sbspro.2012.09.391
  • Jónás, T., & Árva, G. (2016). Application of fuzzy inference systems build from data for quality and service management purposes. In S. M. Dahlgaard-Park & J. J. Dahlgaard (Eds.), 19th QMOD-ICQSS conference international conference on quality and service sciences. Roma, Italy, 21.09.2016–23.09.2016. Lund University Library Press, Lund, pp. 519–534.
  • Kacprzyk, J. (1986). Towards a ‘human-consistent’ multistage decision making and control models using fuzzy sets and fuzzy logic. Fuzzy Sets and Systems, 18(3), 299–314. doi: 10.1016/0165-0114(86)90008-4
  • Kuzmanovic, M., Savic, G., Popovic, M., & Martic, M. (2013). A new approach to evaluation of university teaching considering heterogeneity of students’ preferences. Higher Education, 66(2), 153–171. doi: 10.1007/s10734-012-9596-2
  • Lalla, M., Facchinetti, G., & Mastroleo, G. (2005). Ordinal scales and fuzzy set systems to measure agreement: An application to the evaluation of teaching activity. Quality & Quantity, 38(5), 577–601. doi: 10.1007/s11135-005-8103-6
  • Li, Q. (2013). A novel Likert scale based on fuzzy sets theory. Expert Systems with Applications, 40(5), 1609–1618. doi: 10.1016/j.eswa.2012.09.015
  • Liaw, S. H., & Goh, K. L. (2003). Evidence and control of biases in student evaluations of teaching. International Journal of Educational Management, 17(1), 37–43.
  • Lin, H. F. (2010a). An application of fuzzy AHP for evaluating course website quality. Computers & Education, 54(4), 877–888. doi: 10.1016/j.compedu.2009.09.017
  • Lin, H. T. (2010b). Fuzzy application in service quality analysis: An empirical study. Expert Systems with Applications, 37(1), 517–526. doi: 10.1016/j.eswa.2009.05.030
  • Liou, T. S., & Chen, C. W. (2006). Subjective appraisal of service quality using fuzzy linguistic assessment. International Journal of Quality & Reliability Management, 23(8), 928–943. doi: 10.1108/02656710610688149
  • Liu, R., Cui, L., Zeng, G., Wu, H., Wang, C., Yan, S., & Yan, B. (2015). Applying the fuzzy SERVQUAL method to measure the service quality in certification and inspection industry. Applied Soft Computing, 26, 508–512. doi: 10.1016/j.asoc.2014.10.014
  • Lozano, L. M., García-Cueto, E., & Muñiz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology, 4(2), 73–79. doi: 10.1027/1614-2241.4.2.73
  • Lubiano, M. A., de Sáa, S. D. L. R., Montenegro, M., Sinova, B., & Gil, M. Á. (2016). Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale? Information Sciences, 360, 131–148. doi: 10.1016/j.ins.2016.04.029
  • Lupo, T. (2013). A fuzzy ServQual based method for reliable measurements of education quality in Italian higher education area. Expert Systems with Applications, 40(17), 7096–7110. doi: 10.1016/j.eswa.2013.06.045
  • Lupo, T. (2016). A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. Applied Soft Computing, 40, 468–478. doi: 10.1016/j.asoc.2015.12.010
  • Mashhadiabdol, M., Sajadi, S. M., & Talebi, K. (2014). Analysis of the gap between customers’ perceptions and employees’ expectations of service quality based on fuzzy SERVQUAL logic (case study: Mofid children’s hospital in Tehran, Iran). International Journal of Services and Operations Management, 17(2), 119–141. doi: 10.1504/IJSOM.2014.058840
  • Murray, J. (2013). Likert data: What to use, parametric or non-parametric. International Journal of Business and Social Science, 4(11), 258–264.
  • Nadiri, H., Kandampully, J., & Hussain, K. (2009). Students’ perceptions of service quality in higher education. Total Quality Management, 20(5), 523–535. doi: 10.1080/14783360902863713
  • Quirós, P., Alonso, J. M., & Pancho, D. P. (2016). Descriptive and comparative analysis of human perceptions expressed through fuzzy rating scale-based questionnaires. International Journal of Computational Intelligence Systems, 9(3), 450–467. doi: 10.1080/18756891.2016.1175811
  • Rouyendegh, B. D., & Erkan, T. E. (2013). An application of the fuzzy ELECTRE method for academic staff selection. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(2), 107–115. doi: 10.1002/hfm.20301
  • Samson, S., & McCrea, D. E. (2008). Using peer review to foster good teaching. Reference Services Review, 36(1), 61–70. doi: 10.1108/00907320810852032
  • Sowa, J. F. (2013). What is the source of fuzziness? In R. Seiging, E. Trillas, C. Moraga, & S. Termini (Eds.), On fuzziness (pp. 645–652). Berlin, Heidelberg: Springer.
  • Teeroovengadum, V., Kamalanabhan, T. J., & Seebaluck, A. K. (2016). Measuring service quality in higher education: Development of a hierarchical model (HESQUAL). Quality Assurance in Education, 24(2), 244–258. doi: 10.1108/QAE-06-2014-0028
  • Tóth Zs, E., Andor, G., & Árva, G. (2017a). Peer review of teaching at Budapest University of technology and economics - faculty of economic and social sciences. International Journal of Quality and Service Sciences, 9(3/4), 402–424. doi: 10.1108/IJQSS-02-2017-0014
  • Tóth Zs, E., Surman, V., & Árva, G. (2017b). Challenges in course evaluations at Budapest University of Technology and Economics. In Z. Bekirogullari, M. Y. Minas, & R. X. Thambusamy (Eds.), 8th ICEEPSY - International conference on education and educational psychology. Porto, Portugal, 2017.10.11–2017.10.14. Future Academy, 2017, pp. 629–641.
  • Washer, P. (2006). Designing a system for observation of teaching. Quality Assurance in Education, 14(3), 243–250. doi: 10.1108/09684880610678559
  • Yu, C. M., Tsang, H. T., & Chen, K. S. (2016). Developing a performance evaluation matrix to enhance the learner satisfaction of an e-learning system. Total Quality Management & Business Excellence, 1–19. Published online 19 Sept 2016. http://doi.org/10.1080/14783363.2016.1233809.
  • Zhang, J., Lin, T., & Ren, L. (2010). Dynamic fuzzy evaluation for e-commerce service quality based on the SERVPERF. Paper presented at the International Conference on E-Business and E-Government 2010 (ICEE), pp. 576–579. Retrieved from http://ieeexplore.ieee.org/document/5590689/