A Fuzzy Logic Approach to Assess Web Learner’s Joint Skills

Full Text (PDF, 586KB), PP.14-21

Views: 0 Downloads: 0


Mousumi Mitra 1,* Atanu Das 1

1. Dept. of CSE, Netaji Subhash Engineering College, Garia, Kolkata-700152, INDIA

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2015.09.02

Received: 24 Jun. 2015 / Revised: 15 Jul. 2015 / Accepted: 24 Jul. 2015 / Published: 8 Sep. 2015

Index Terms

E-learning, fuzzy logic, joint-skill, skill assessment, competencies


Skill assessment is an important but complicated task in the entire web based teaching and learning process. The learner’s performance assessment has a strong influence on learners’ approaches to learn and their learning outcomes like professional acceptability on desired skills. Most educators focus either on assessing a learner’s technical skill set or non-technical skill set, individually, rather than focusing on both the aspects. This paper bridges the gap by applying fuzzy logic approach to analyze a learner’s joint skills incorporating both skills-set.
An already proven e-commerce website’s evaluation technique has been chosen and applied in two situations of learner’s skill assessment through case studies namely: technical skills evaluation, and non-technical skills evaluation. Experiments show that the learner’s success depends on both sets of skill attributes. This work then proposed a novel method for skill assessment considering two (instead of one) sets of skill attributes invoking parallel or joint application of the technique. This new technique has also been analysed through a case study.

Cite This Paper

Mousumi Mitra, Atanu Das, "A Fuzzy Logic Approach to Assess Web Learner's Joint Skills", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.9, pp.14-21, 2015. DOI:10.5815/ijmecs.2015.09.02


[1]B. Ranjit, “An application of fuzzy set in students’ evaluation,” Fuzzy Sets and Systems, vol. 74, pp. 187–194, 1995.
[2]D. Zhou and W. Huang, “Fuzzy Set Approach to Assessing E-commerce Websites,” Proc. of the 10th Americas Conf. on Inf. Sys., NY, pp. 2376-2383, August, 2004.
[3]H. Li and W. Wang, “Intelligent Tutoring System Based on Credal Networks and Learning Emotions,” 2nd Int. Workshop on Educ. Tech. and Comp. Sci., etcs, vol. 1, pp. 321-324, 2010.
[4]J. R. Echauz and G. J. Vachtsevanos, “Fuzzy Grading System,” IEEE Trans. Educ., vol. 38(2), pp. 158–164, 1995.
[5]M. Jian and D. Zhou, “Fuzzy set approach to the assessment of student-centered learning,” IEEE Trans. on Educ., vol 43(2), pp. 237-241, 2000.
[6]M. Mitra and A. Das, “Applying A Fuzzy Technique For Web-Based Learner’s Performance Evaluation,” Proc. of N. Conf. on Control, Comm. & Device Electronics, JIS Engg. College, WB, India, pp. 125-131, 2013.
[7]P. Brna, “Artificial intelligence in educational software: has its time come?” British J. of Educ. Tech., vol. 30, pp. 79–81, 1999.
[8]R. Bag and A. Das, “Developing an Intelligent Tutoring System following Bayesian Approach,” Int. J. of Adv. Engg. & App., vol. 2, pp. 114-119, 2010.
[9]T. D. Cochrane, “Critical success factors for transforming pedagogy with mobile Web 2.0,” British J. of Educ. Tech., vol. 45(1), pp. 65–82, 2014.
[10]Y. Hsu, H. N. J. Ho, C. Tsai, G. Hwang, H. C. Chu, C. Wang and N. Chen, “Research Trends in Technology-based Learning from 2000 to 2009: A content Analysis of Publications in Selected Journals,” J. of Educ. Tech. & Society, vol. 15(2), pp. 354-370, 2012.
[11]D. Bose, A. Das, “Using Fuzzy Trapezoidal Rule for Web Learner’s Competence Assessment,” Int. J. of Electronics and Communication Tech., (IJECT), Vol. 6 (1), spl-1, pp. 169-173, 2015.
[12]N. Cavus, “The application of a multi-attribute decision-making algorithm to learning management systems evaluation,” British Journal of Educational Technology, vol.42, pp. 19–30, 2011.