IntelliFuzz: An Advanced Fuzzy Logic Framework for Dynamic Evaluation of Student Performance in Open-Ended Learning Tasks
DOI:
https://doi.org/10.22399/ijcesen.911Keywords:
Fuzzy Logic, Open-Ended Tasks, Student Performance Evaluation, Dynamic Assessment, IntelliFuzz, Automated Assessment SystemAbstract
This study presents IntelliFuzz, an advanced fuzzy logic-based assessment system designed for the dynamic evaluation of student performance in open-ended tasks. The proposed system leverages fuzzy logic to address the inherent subjectivity and ambiguity in evaluating tasks such as essays, project work, and case studies. IntelliFuzz incorporates multiple evaluation criteria, including task relevance, critical thinking, creativity, and presentation quality, to generate a comprehensive performance score. Experimental results on a dataset of 500 student submissions demonstrate the effectiveness of IntelliFuzz. The system achieved a 95% accuracy in aligning with expert assessments and reduced evaluation time by 30% compared to traditional manual grading methods. The fuzzy inference system was calibrated using 150 expert feedback samples, yielding an average correlation coefficient of 0.92 between system-generated scores and expert evaluations. Furthermore, IntelliFuzz was rated 85% satisfactory by instructors for its ability to provide consistent and fair evaluations.The study highlights the potential of fuzzy logic in educational assessment, offering a scalable and efficient solution for evaluating subjective student tasks. Future research will focus on integrating machine learning to further enhance the adaptability and precision of the system.
References
Cagan, J., Grossmann, I.E. and Hooker, J., (1997). A conceptual framework for combining artificial intelligence and optimization in engineering design. Research in Engineering Design, 9;20-34. DOI: https://doi.org/10.1007/BF01607055
Jyothi, A.P., Shankar, A., Narayan, A., Monisha, T.R., Gaur, P. and Kumar, S.S., (2024), April. Computational Intelligence and Its Transformative Influence. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/I2CT61223.2024.10543715
Keller, J.M., Liu, D. and Fogel, D.B., (2016). Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. John Wiley & Sons.
Rahman, I. and Mohamad-Saleh, J., (2018). Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey. Applied Soft Computing, 69;72-130. DOI: https://doi.org/10.1016/j.asoc.2018.04.051
Khaleel, M., Jebrel, A. and Shwehdy, D.M., (2024). Artificial Intelligence in Computer Science: Int. J. Electr. Eng. and Sustain., pp.01-21. https://doi. org/10.5281/zenodo. 10937515.
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.W. and Qiu, J., (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4). DOI: https://doi.org/10.1016/j.xinn.2021.100179
Glover, F., (1986). Future paths for integer programming and links to artificial intelligence. Computers & operations research, 13(5);533-549. DOI: https://doi.org/10.1016/0305-0548(86)90048-1
Armaghani, D.J., Mohammed, A.S., Bhatawdekar, R.M., Fakharian, P., Kainthola, A. and Mahmood, W.I., (2024). Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 138(3);2023-2027. DOI: https://doi.org/10.32604/cmes.2023.031701
Robertson, J., Fossaceca, J.M. and Bennett, K.W., (2021). A cloud-based computing framework for artificial intelligence innovation in support of multidomain operations. IEEE Transactions on Engineering Management, 69(6);3913-3922. DOI: https://doi.org/10.1109/TEM.2021.3088382
Abioye, S.O., Oyedele, L.O., Akanbi, L., Ajayi, A., Delgado, J.M.D., Bilal, M., Akinade, O.O. and Ahmed, A., (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44;103299. DOI: https://doi.org/10.1016/j.jobe.2021.103299
Del Ser, J., Osaba, E., Sanchez-Medina, J.J. and Fister, I., (2019). Bioinspired computational intelligence and transportation systems: a long road ahead. IEEE Transactions on Intelligent Transportation Systems, 21(2);466-495. DOI: https://doi.org/10.1109/TITS.2019.2897377
Zahraee, S.M., Assadi, M.K. and Saidur, R., (2016). Application of artificial intelligence methods for hybrid energy system optimization. Renewable and sustainable energy reviews, 66, pp.617-630. DOI: https://doi.org/10.1016/j.rser.2016.08.028
Jackson, I., Ivanov, D., Dolgui, A. and Namdar, J., (2024). Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research, pp.1-26. DOI: https://doi.org/10.1080/00207543.2024.2309309
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P. and Fischl, M., (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122;502-517. DOI: https://doi.org/10.1016/j.jbusres.2020.09.009
S. Han and X. Sun, (2024). Optimizing Product Design Using Genetic Algorithms and Artificial Intelligence Techniques, in IEEE Access, 12;151460-151475, doi: 10.1109/ACCESS.2024.3456081 DOI: https://doi.org/10.1109/ACCESS.2024.3456081
Huang, M.H. and Rust, R.T., (2022). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98(2);209-223. DOI: https://doi.org/10.1016/j.jretai.2021.03.001
Khan, M., Chuenchart, W., Surendra, K.C. and Khanal, S.K., (2023). Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects. Bioresource technology, 370;128501. DOI: https://doi.org/10.1016/j.biortech.2022.128501
Naseer, I., (2021). The efficacy of Deep Learning and Artificial Intelligence Framework in Enhancing Cybersecurity, Challenges and Future Prospects. Innovative Computer Sciences Journal, 7(1).
Bennett, C.C. and Hauser, K., (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial intelligence in medicine, 57(1);9-19. DOI: https://doi.org/10.1016/j.artmed.2012.12.003
Rane, Nitin and Choudhary, Saurabh and Rane, Jayesh, Integrating ChatGPT, Bard, and Leading-edge Generative Artificial Intelligence in Building and Construction Industry: Applications, Framework, Challenges, and Future Scope, http://dx.doi.org/10.2139/ssrn.4645597 DOI: https://doi.org/10.2139/ssrn.4645597
ARSLANKAYA, S., & ÇELİK, M. T. (2021). Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks. International Journal of Computational and Experimental Science and Engineering, 7(1), 2021–03. Retrieved from https://ijcesen.com/index.php/ijcesen/article/view/139 DOI: https://doi.org/10.22399/ijcesen.837731
M. Venkateswarlu, K. Thilagam, R. Pushpavalli, B. Buvaneswari, Sachin Harne, & Tatiraju.V.Rajani Kanth. (2024). Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.676 DOI: https://doi.org/10.22399/ijcesen.676
Nasti, S. M., Najar, Z. A., & Chishti, M. A. (2024). A Comprehensive Review of Path Planning Techniques for Mobile Robot Navigation in Known and Unknown Environments. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.797 DOI: https://doi.org/10.22399/ijcesen.797
R. Logesh Babu, K. Tamilselvan, N. Purandhar, Tatiraju V. Rajani Kanth, R. Prathipa, & Ponmurugan Panneer Selvam. (2025). Adaptive Computational Intelligence Algorithms for Efficient Resource Management in Smart Systems. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.836 DOI: https://doi.org/10.22399/ijcesen.836
S.D.Govardhan, Pushpavalli, R., Tatiraju.V.Rajani Kanth, & Ponmurugan Panneer Selvam. (2024). Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.591 DOI: https://doi.org/10.22399/ijcesen.591
Ozer, E., SEVİNÇKAN, N., DEMİROĞLU, E., & AYDOS, H. (2025). Enhancing Trading Strategies: Mandani Fuzzy Logic Forecasting for Borsa Istanbul Stocks Using Important Indicators. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.695 DOI: https://doi.org/10.22399/ijcesen.695
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
