Evaluating the Undergraduate Course based on a Fuzzy AHP-FIS Model

Evaluating the Undergraduate Course based on a Fuzzy AHP-FIS Model

Автор: Yan Liu, Xin Zhang

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 6 vol.12, 2020 года.

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Course evaluation is a critical part of undergraduate curriculum in computer science. Most existing evaluation methods are based on questionnaire by analyzing the satisfaction rate of the respondents. However, there are many indicators such as attendance rate, activity level and average score that can reflect the overall effectiveness of the course. Limited research has taken all those indicators into account during course evaluation. This research chooses an innovative perspective that considers course evaluation as a multiple criteria decision-making problem. A hybrid model is proposed to measure the course effectiveness regarding various indicators. The indicators are first prioritized by a fuzzy Analytic Hierarchical Process (AHP) model which applies fuzzy numbers to deal with the uncertainty brought by subjective judgement. A hierarchical fuzzy inference system (FIS) is then designed to evaluate the course effectiveness, which reduces the number of the fuzzy IF-THEN rules and increases the efficiency compared to the traditional FIS. A numerical example is presented to demonstrate the application. The proposed model helps not only judge an individual course based on a comprehensive view but also rank multiple courses.

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Course evaluation, fuzzy AHP, hierarchical FIS, decision-making, MCDM

Короткий адрес: https://sciup.org/15017611

IDR: 15017611   |   DOI: 10.5815/ijmecs.2020.06.05

Список литературы Evaluating the Undergraduate Course based on a Fuzzy AHP-FIS Model

  • I. A. Khan et al., "Redesign and validation of a computer programming course using Inductive Teaching Method," PloS one, vol. 15, no. 6, p. e0233716, 2020, doi: 10.1371/journal.pone.0233716.
  • M. Goos and A. Salomons, "Measuring teaching quality in higher education: assessing selection bias in course evaluations," Research in Higher Education, vol. 58, no. 4, pp. 341-364, 2016, doi: 10.1007/s11162-016-9429-8.
  • M. Nicolaou and M. Atkinson, "Do student and survey characteristics affect the quality of UK undergraduate medical education course evaluation? A systematic review of the literature," Studies in Educational Evaluation, vol. 62, pp. 92-103, 2019, doi: 10.1016/j.stueduc.2019.04.011.
  • J. Dai and J. Blackhurst, "A four-phase AHP–QFD approach for supplier assessment: a sustainability perspective," International Journal of Production Research, vol. 50, no. 19, pp. 5474-5490, 2012, doi: 10.1080/00207543.2011.639396.
  • G. R. Faramarzi, M. Khodakarami, A. Shabani, R. Farzipoor Saen, and F. Azad, "New network data envelopment analysis approaches: an application in measuring sustainable operation of combined cycle power plants," Journal of Cleaner Production, vol. 108, pp. 232-246, 2015, doi: 10.1016/j.jclepro.2015.06.065.
  • M.-Y. Liao, C.-W. Wu, and J.-W. Wu, "Fuzzy inference to supplier evaluation and selection based on quality index: a flexible approach," Neural Computing and Applications, vol. 23, no. S1, pp. S117-S127, 2013, doi: 10.1007/s00521-012-1266-x.
  • I. J. Orji and S. Wei, "An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry," Computers & Industrial Engineering, vol. 88, pp. 1-12, 2015, doi: 10.1016/j.cie.2015.06.019.
  • R. K. Singh, N. Chaudhary, and Nikhil Saxena "Selection of warehouse location for a global supply chain: A case study," IIMB Management Review, 2018, doi: 10.1016/j.iimb.2018.08.009.
  • B. C. Balusa and A. K. Gorai, "Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method," Journal of Sustainable Mining, vol. In press, 2018, doi: https://doi.org/10.1016/j.jsm.2018.10.003.
  • E. Antonanzas-Baztan et al., "Design, implementation and evaluation of an education course to promote professional self-efficacy for breastfeeding care," Nurse education in practice, vol. 45, p. 102799, May 2020, doi: 10.1016/j.nepr.2020.102799.
  • G. Na, S. Shankarb, and S. G. Bb, "Structured Evaluation of Laboratory Course Content for Better Attainment of Program Outcomes," Procedia Computer Science, vol. 172, pp. 869-874, 2020.
  • B. G. Emiroglu and S. Sahin, "Analysis of Students’ Performances during Lab Sessions of Computer Networks Course," Educational Technology & Society, vol. 16, no. 3, 2013.
  • P.-T. Oon, B. Spencer, and C. C. S. Kam, "Psychometric quality of a student evaluation of teaching survey in higher education," Assessment & Evaluation in Higher Education, vol. 42, no. 5, pp. 788-800, 2016, doi: 10.1080/02602938.2016.1193119.
  • K. Young, J. Joines, T. Standish, and V. Gallagher, "Student evaluations of teaching: the impact of faculty procedures on response rates," Assessment & Evaluation in Higher Education, vol. 44, no. 1, pp. 37-49, 2018, doi: 10.1080/02602938.2018.1467878.
  • M. Kaushik, "Evaluating a First-Year Engineering Course for Project Based Learning (PBL) Essentials," Procedia Computer ence, vol. 172, pp. 364-369, 2020.
  • N. Ramli, D. Mohamad, and N. H. Sulaiman, "Evaluation of Teaching Performance with Outliers Data using Fuzzy Approach," Procedia - Social and Behavioral Sciences, vol. 8, pp. 190-197, 2010, doi: 10.1016/j.sbspro.2010.12.026.
  • J.-F. Chen, H.-N. Hsieh, and Q. H. Do, "Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach," Applied Soft Computing, vol. 28, pp. 100-108, 2015, doi: 10.1016/j.asoc.2014.11.050.
  • Y. Liu, C. M. Eckert, and C. Earl, "A review of fuzzy AHP methods for decision-making with subjective judgements," Expert Systems with Applications, vol. 161, p. 113738, 2020/12/15/ 2020, doi: https://doi.org/10.1016/j.eswa.2020.113738.
  • W. Zhu, M. Wan, Y. Zhou, and W. Pan, "Fuzzy computation of teaching performance based on data envelopment analysis method," Cognitive Systems Research, vol. 52, pp. 351-358, 2018, doi: 10.1016/j.cogsys.2018.07.018.
  • R. Fuentes, B. Fuster, and A. Lillo-Bañuls, "A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables," Expert Systems with Applications, vol. 48, pp. 89-99, 2016, doi: 10.1016/j.eswa.2015.11.022.
  • T. L. Saaty, "The analytic hierarchy process: planning, priority setting, resources allocation," New York: McGraw, 1980.
  • N. Subramanian and R. Ramanathan, "A review of applications of Analytic Hierarchy Process in operations management," International Journal of Production Economics, vol. 138, no. 2, pp. 215-241, 2012, doi: 10.1016/j.ijpe.2012.03.036.
  • A. Emrouznejad and M. Marra, "The state of the art development of AHP (1979–2017): a literature review with a social network analysis," International Journal of Production Research, vol. 55, no. 22, pp. 6653-6675, 2017, doi: 10.1080/00207543.2017.1334976.
  • "1-s2.0-S1877050920313855-main.pdf."
  • M. Ebrahimi and M. Taheri, "Selection of Database Management System with Fuzzy-AHP for Electronic Medical Record," I.J. Information Engineering and Electronic Business, vol. 5, pp. 1-6, 2015.
  • R. Nagpal, D. Mehrotra, Pradeep, K. Bhatia, and A. Sharma, "Rank University Websites Using Fuzzy AHP and Fuzzy TOPSIS Approach on Usability," International Journal of Information Engineering & Electronic Business, vol. 7, no. 1, pp. 29-36, 2015.
  • S. Mustapha, K. M. Fayçal, and S. Mohammed, "Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators," International Journal of Intelligent Systems & Applications, vol. 6, no. 11, pp. 49–78, 2014.
  • F. Boufera, F. Debbat, N. Monmarché, M. Slimane, and M. F. Khelfi, "Fuzzy Inference System Optimization by Evolutionary Approach for Mobile Robot Navigation," International Journal of Intelligent Systems and Applications, vol. 10, no. 2, pp. 85-93, 2018.
  • L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, no. 3, pp. 338-353, 1965.
  • A. Kaufmann and M. Gupta, Introduction to Fuzzy Arithmetic. Theory and Applications. New York: Van Nostrand Reinhold, 1991.
  • G. Klir and B. Yuan, Fuzzy sets and fuzzy logic. Prentice Hall New Jersey, 1995.
  • T. J. Ross, Fuzzy Logic with Engineering Applications, 2nd Edition ed. John Wiley & Sons Ltd, 2004, p. 78.
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