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Fuzzy Logic for Refining the Evaluation of Learners’ Performance in Online Engineering Education
Open accessAbstract
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.
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Fuzzy logic systems accommodate uncertainty and subjectivity in student assessment better than conventional methods.
“In summary, these works demonstrate the potential of fuzzy logic systems for accommodating uncertainty and subjectivity in student assessment whereas conventional methods do not [13,14].”
From Fuzzy Logic-Driven Assessment Model for Mathematical Proof Grading in Education (2025) · cites this paper as [14]
The Experimental Results and Discussion (p. 5) says the fuzzy-logic system accounts for difficulty, misconceptions, and effort and outperforms an average-grade assessment tool in the reported comparison. The Fuzzifier section (p. 4) shows how values can have weighted membership in more than one category. These passages support a more nuanced assessment, but do not establish the broader claim about accommodating uncertainty and subjectivity better than conventional methods generally; the comparison is with one average-grade tool. (LLM, conf=0.78)
Experimental — accuracy not yet measuredKrouska et al. (2020) adopted fuzzy logic as the basis of an intelligent tutoring system that improved assessments of learners' performance by considering task complexity and student working effort.
“In the field of online engineering learning, Krouska et al. (2020) are worth mentioning, who adopted fuzzy logic as the basis of an intelligent tutoring system that improved assessments of learners' performance by taking into account task complexity and student working effort”
From Fuzzy Logic-Driven Assessment Model for Mathematical Proof Grading in Education (2025) · cites this paper as [27]
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Research works printed but not linked: 19
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- [19][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. -
- [20][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. <!-- image --> Akrivi Krouska is a Postdoctoral Researcher in the Department of Informatics and Computer Engineering, University of West Attica, Greece. She has received a Ph.D. Degree in Informatics from the Department of Informatics, University of Piraeus, Greece, a M.Sc. degree in 'Information Systems' from University of Athens, Greece and a B.Sc. degree in Informatics from University of Piraeus, Greece. She has published a significant number of articles in international conferences, books and journals. Her research interests are in the areas of social networking services, sentiment analysis, learning analytics, user modeling and artificial intelligence in education. <!-- image --> Christos Troussas is a Postdoctoral Researcher in the Department of Informatics and Computer Engineering, University of West Attica, Greece. He has received a Ph.D. Degree in Informatics, a M.Sc. degree in 'Advanced Informatics and Computing Systems' and a B.Sc. degree in Informatics from the Department of Informatics, University of Piraeus, Greece. He has published a significant number of articles in international conferences, books and journals. His current research interests are in the areas of knowledge-based software engineering, multi-agent systems, adaptive HCI, user modeling and artificial intelligence. <!-- image --> Cleo Sgouropoulou is Professor and Head of the Department of Informatics and Computer Engineering, University of West Attica, Greece. She has received a Ph.D. Degree in Electrical and Computer Engineering from the Department of Electrical and Computer Engineering, National Technical University of Athens, Greece and a B.Eng. from the same Department. She has published a significant number of articles in international conferences, books and journals. Her research interests are in the areas of software engineering, artificial intelligent in education, user modeling and quality assurance in education.
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Fuzzy Logic-Driven Assessment Model for Mathematical Proof Grading in Education
Alkandari, M., & Alajmi, A. H. (2025) · European Journal of Pure and Applied Mathematics · doi:10.29020/nybg.ejpam.v18i3.6257 · Cited by 1
Metadata
- Authors
- Krouska, A., Troussas, C., & Sgouropoulou, C.
- Affiliations
- University of West Attica (GR)
- Venue
- European Journal of Engineering Research and Science
- Publisher
- European Open Science Publishing
- Published
- 2019-06-15
- Volume
- 4
- Issue
- 6
- Pages
- 50-56
- ISSN
- 2506-8016 (ISSN-L)
- OpenAlex
- W4249134699
- Cited by
- 30
- References
- 0
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 8 · 2023: 5 · 2022: 3 · 2021: 5 · 2020: 2
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- European Journal of Engineering and Technology Research (publishedVersion)
- European Journal of Engineering and Technology Research (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- intelligent tutoring systems, fuzzy logic, online engineering education, test difficulty, student effort, social networking in education, misconceptions
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