Research Article

Fuzzy Logic for Refining the Evaluation of Learners’ Performance in Online Engineering Education

Akrivi Krouska
Department of Informatics and Computer Engineering, University of West Attica, Greece
Christos Troussas
Department of Informatics and Computer Engineering, University of West Attica, Greece
* Corresponding author
Cleo Sgouropoulou
Department of Informatics and Computer Engineering, University of West Attica, Greece
DOI icon 10.24018/ejeng.2019.4.6.1369

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Submitted 2019-06-03
Published 2019-06-15

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Article Main Content

Intelligent tutoring systems have been widely used for optimizing the educational process by creating a student-centered learning environment. As a matter of fact, an integral part of intelligent tutoring systems is the evaluation of the learners’ performance. In traditional learning, the instructors calculate the grade of the students derived from the assessment units and other factors, such as the difficulty of the exercises or their effort, in order to produce the final students’ score in the course. However, in most cases, the evaluation of learners’ performance in intelligent tutoring systems takes place by calculating an average grade of students without taking into account the aforementioned factors. In view of the above, this paper presents a novel way for refining the evaluation of students’ performance using fuzzy logic. As a testbed for our research, we have designed and implemented an intelligent tutoring system holding social networking characteristic for teaching the engineering course of “Compilers”. More specifically, the system is responsible for acquiring information about students such as their grades, the kinds of misconceptions, the level of tests’ difficulty as well as their effort including their social interaction, i.e. participation in forums, making comments in posts and posting regarding the educational process. Taking these into consideration, fuzzy logic model diagnoses the accuracy of students’ grade and the system suggests that the instructor redefine students’ grade properly. Our system was evaluated using t-test and the results show high accuracy and objectivity in the evaluation of students’ performance.

Keywords: Evaluation of Student Performance Fuzzy Logic Intelligent Tutoring Systems E-learning Assessment units

References

  1. L. Cagliero, L. Farinetti and E. Baralis, "Recommending Personalized Summaries of Teaching Materials," IEEE Access, vol. 7, pp. 22729-22739, 2019.
     Google Scholar
  2. D. E. Benchoff, M. P. González and C. R. Huapaya, "Personalization of Tests for Formative Self-Assessment", IEEE Latin-American Learning Technologies Journal, vol. 13, no. 2, pp. 70-74, May 2018.
     Google Scholar
  3. C. Cunha-Pérez, M. Arevalillo-Herráez, L. Marco-Giménez and D. Arnau, "On Incorporating Affective Support to an Intelligent Tutoring System: an Empirical Study", IEEE Latin-American Learning Technologies Journal, vol. 13, no. 2, pp. 63-69, May 2018.
     Google Scholar
  4. J. Dong, W. Hwang, R. Shadiev and G. Chen, "Implementing On-Call-Tutor System for Facilitating Peer-Help Activities", in IEEE Transactions on Learning Technologies, vol. 12, no. 1, pp. 73-86, 1 Jan.-March 2019.
     Google Scholar
  5. B. Latimer, D. A. Bergin, V. Guntu, D. J. Schulz and S. S. Nair, "Integrating Model-Based Approaches into a Neuroscience Curriculum—An Interdisciplinary Neuroscience Course in Engineering", IEEE Transactions on Education, vol. 62, no. 1, pp. 48-56, Feb. 2019.
     Google Scholar
  6. J. D. Ortega-Alvarez, W. Sanchez and A. J. Magana, "Exploring Undergraduate Students’ Computational Modeling Abilities and Conceptual Understanding of Electric Circuits", in IEEE Transactions on Education, vol. 61, no. 3, pp. 204-213, Aug. 2018.
     Google Scholar
  7. Z. Wang, S.-Y. Gong, S. Xua, X.-E. Hu, “Elaborated feedback and learning: Examining cognitive and motivational influences”, Computers and Education, vol. 136, pp. 130-140, 2019.
     Google Scholar
  8. A. A. Surya, M. K. Kurian and S. M. Varghese, “Overall performance evaluation of engineering students using fuzzy logic”, International Journal on Cybernetics & Informatics, vol. 5, no. 2, pp. 71-78, 2016.
     Google Scholar
  9. Meenakshi and P. Nagar, “Application of Fuzzy Logic for Evaluation of Academic Performance of Students of Computer Application Course”, International Journal for Research in Applied Science & Engineering Technology, vol. 3, no. 5, pp. 260-267, 2015.
     Google Scholar
  10. R. S. Yadav, A. K. Soni and S. Pal, “A study of academic performance evaluation using Fuzzy Logic techniques”, presented at the International Conference on Computing for Sustainable Global Development, USA, pp. 48-53, 2014.
     Google Scholar
  11. G. Gokmen, T. Ç. Akinci, M. Tektaş, N. Onat, G. Kocyigit and N. Tektaş, “Evaluation of student performance in laboratory applications using fuzzy logic”, Procedia-Social and Behavioral Sciences, vol. 2, no. 2, pp. 902-909, 2010.
     Google Scholar
  12. E. Sakthivel, K. S. Kannan and S. Arumugam, “Optimized evaluation of students’ performances using fuzzy logic”, International Journal of Scientific & Engineering Research, vol. 4, no. 9, pp. 1128-1133, 2013.
     Google Scholar
  13. S. Patil, A. Mulla and R. R. Mudholkar, “A fuzzy Evaluation Approach”, International Journal of Computer Science and Communication, vol. 3, no. 1, pp. 9-12, 2012.
     Google Scholar
  14. C. R. Huapaya, “Proposal of fuzzy logic-based students' learning assessment model”, presented at the XIX Argentine Congress on Computer Sciences, Argentina, 2012.
     Google Scholar
  15. S. N. Ingoley και J. W. Bakal, “Evaluating students’ performance using fuzzy logic”, International Journal of Computer Applications, pp. 15-20, 2012.
     Google Scholar
  16. J. A. Montero, R. M. Alsina, J. A. Morán and M. Cid, “Fuzzy logic system for students’ evaluation”, presented at the International Work-Conference on Artificial Neural Networks, Spain, pp. 1246-1253, 2005.
     Google Scholar
  17. D. Putra and A. Sasmita, “Fuzzy Logic Method for Evaluation of Difficulty Level of Exam and Student Graduation”, International Journal of Computer Science Issues, vol. 10, no. 2, pp. 223-229, 2013.
     Google Scholar
  18. A. Krouska, C. Troussas, and M. Virvou, “A literature review of Social Networking- based Learning Systems using a novel ISO-based framework”, Intelligent Decision Technologies, vol. 13, no. 1, pp. 23-39, 2019.
     Google Scholar
  19. A. Krouska, C. Troussas, and M. Virvou, “SN-Learning: An exploratory study beyond e-learning and evaluation of its applications using EV-SNL framework”, Journal of Computer Assisted Learning, vol. 35, no. 2, pp. 168-177, 2019.
     Google Scholar
  20. N. Dabbagh, “The online learner: Characteristics and pedagogical implications”, Contemporary Issues in Technology and Teacher Education, vol. 7, no. 3, pp. 217- 226, 2007.
     Google Scholar