International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 15, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 965KB)
Advancing a Type-1 Rule-Based Fuzzy Logic Learning Model for Measuring Learner Engagement and Content Adjustment
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Author(s)
Chiedozie John Onyianta
1
Deborah Uzoamaka Ebem
1,2
Anayo Chukwu Ikegwu
3,*
Chibueze Valentine Ikpo
3
Index Terms
Adaptive Learning, Fuzzy Logic, Rule-Based, Learning Style, Content Adjustment, Learners Engagement
Abstract
Over the years, learning has shifted from a conventional classroom environment to a digital space due to an increased interest in e-learning and swift innovations in information technology. This has brought the attention of many individuals and institutions to delve into building various approaches for adaptive e-learning technologies. Most existing e-learning systems are teacher-based, time-wasting and do not monitor learners’ progress levels. This paper presents a type-1 rule-based fuzzy logic model to implement an adaptive e-learning system by identifying students’ prior knowledge, learning style, and learning pace. The system was designed with Object-Oriented Analysis and Design Methodology and implemented using PHP, JavaScript, and MySQL technologies. A total of 31 first-year students of the University of Nigeria, Nsukka, participated in the evaluation of the software. The pre-test measured the students' prior knowledge, and the performance of each student was mapped. The system monitors students’ engagement levels and performance to improve learning outcomes. It also has an ‘Ask Teacher’ feature, which allows a student to ask the teacher questions outside the forum and the student’s feedback form. Each chapter has a pre-test to test the student’s existing knowledge, well-explained chapter content in text and audio-visual format, and a post-test to test their performance at the end of each chapter. After participating in the experiment, a questionnaire was used to collect the general students’ views on online-adaptive learning. The study implies that it assists students, teachers, and universities to have seamless learning and offers a quick feedback mechanism for the university’s decision-making.
Cite This Paper
Chiedozie John Onyianta, Deborah Uzoamaka Ebem, Anayo Chukwu Ikegwu, Chibueze Valentine Ikpo, "Advancing a Type-1 Rule-Based Fuzzy Logic Learning Model for Measuring Learner Engagement and Content Adjustment", International Journal of Education and Management Engineering (IJEME), Vol.15, No.6, pp. 27-36, 2025. DOI:10.5815/ijeme.2025.06.03
Reference
[1]P. A. Khodke, M. G. Tingane, A. P. Bhagat, S. P. Chaudhari, and M. S. Ali, “Neuro Fuzzy intelligent e-Learning systems,” in Proceedings of 2016 Online International Conference on Green Engineering and Technologies, IC-GET 2016, 2017. doi: 10.1109/GET.2016.7916766.
[2]R. Platt, “Grasp Learning : Models , Methods , and Performance,” Annu. Rev. ofControl, Robot. Auton. Syst., vol. 6, pp. 363–89, 2023.
[3]A. Singh, S. Sengupta, and V. Lakshminarayanan, “Explainable Deep Learning Models in Medical Image Analysis,” J. Imaging, vol. 6, no. 52, pp. 1–19, 2020, doi: 10.3390/jimaging6060052.
[4]M. Maheshwari, “Transformation in Higher Education through E- Learning: A Shifting Paradigm,” Pacific Bus. Rev. Int., vol. 13, no. 8, pp. 49–63, 2021.
[5]M. Liu and D. Yu, “Towards intelligent E-learning systems,” Educ. Inf. Technol., pp. 7845–7876, 2023.
[6]T. Vukić, M. Jovanović, and D. Todorović, “GOALS AND OBJECTIVES OF EDUCATION FOR SUSTAINABLE DEVELOPMENT AS MODERN CURRICULUM INNOVATION IN SERBIA , MONTENEGRO AND CROATIA,” Philos. Sociol. Psychol. Hist., vol. 20, no. 1, pp. 55–72, 2021.
[7]А. Наук, “APPLICATION OF BASIC PRINCIPLES AND RULES OF INNOVATIVE,” in In Международная конференция академических наук, 2024, pp. 46–55.
[8]M. Chankseliani and T. Mccowan, “Higher education and the Sustainable Development Goals,” High. Educ., no. 2020, pp. 1–8, 2021, doi: 10.1007/s10734-020-00652-w.
[9]A. C. Ikegwu, F. H. Nweke, and C. V. Anikwe, “Recent trends in computational intelligence for educational big data analysis,” Iran J. Comput. Sci., vol. 7, no. 1, pp. 103–129, 2024, doi: 10.1007/s42044-023-00158-5.
[10]A. C. Ikegwu, H. F. Nweke, C. V. Anikwe, U. R. Alo, and O. R. Okonkwo, “Big Data Analytics for Data-driven Industry: A Review of Data Sources, Tools, Challenges, Solutions and Research Directions,” Clust. Comput., vol. 25, no. 5, pp. 3343–3387, 2022, doi: https://doi.org/10.1007/s10586-022-03568-5.
[11]R. M. Ambele, S. F. Kaijage, M. A. Dida, and L. Trojer, “A review of the Development Trend of Personalized learning Technologies and its Applications,” Int. J. Adv. Sci. Res. Eng., vol. 8, no. 11, 2022, doi: 10.31695/IJASRE.2022.8.11.9.
[12]S. K. Mathivanan et al., “Adoption of E-Learning during Lockdown in India,” Int. J. Syst. Assur. Eng. Manag., no. February, 2021, doi: 10.1007/s13198-021-01072-4.
[13]S. Yang and D. C. Yue Hu, “Innovative approaches for enhancing English learning using fuzzy logic-based intelligence assistant in the cloud platform,” Internet Technol. Lett., vol. e444, 2023, doi: https://doi.org/10.1002/itl2.444.
[14]P. Sudhakar and K. S. Kumari, “Digitalisation, Online Learning and Virtual World,” J. Humanit. Soc. Sci. Res., vol. 2, pp. 45–52, 2020.
[15]M. Chkiwa, M. Chkiwa, and F. Achour, “Student Knowledge Evaluation System: a case of application of Fuzzy logic in Intelligent Education,” in 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA), 2023, pp. 1–6. doi: 10.1109/INISTA59065.2023.10310505.
[16]S. Oxman, and W. Wong, “White paper: Adaptive Learning Systems”. A white paper from the DVX/ DeVry Education Group and Integrated Education Solutions, 2014.
[17]G.J. Hwang, P.H. Wu, and C.C. Chen, “An online game approach for improving students' learning performance in web-based problem-solving activities”. Computers & Education, 59(4), 2012.
[18]N.D. Oye, S. Mazleena S. and N. A. Iahad, “E-Learning Methodologies and Tools”. (IJACSA) International Journal of Advanced Computer Science and Applications, 3(2), 2012.
[19]B.H. Michael and S. Heather, “The Rise of K–12 Blended Learning”. Innosight Institute, Inc, 2011.
[20]T.C. Hsieh, T.I. Wang, C.Y. Su, and M.C. Lee, “A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning”. Educational Technology & Society, 15 (1), 273–288, 2012.
[21]V. Syamasudha and N. Siddaiah, “Design and Structure of Fuzzy Logic Using Adaptive Online Learning Systems”. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(3), 2012.
[22]A. Khalid, “Type-2 Fuzzy Logic-based Systems for Adaptive Learning and Teaching within Intelligent E-Learning Environments”. Ph.D. Thesis, University of Essex, 2016.