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p-Index From 2021 - 2026
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All Journal Studies in Learning and Teaching The Indonesian Journal of Computer Science
Elegbeleye, Femi
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Other

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Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks Mbodila, Munienge; Esan, Omobayo Ayokunle; Elegbeleye, Femi
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4417

Abstract

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.    
A Systematic Review of Challenges in Teaching and Learning Computer Programming Modules Elegbeleye, Femi; Isong, Bassey
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4592

Abstract

Computer programming has become an essential skill that is needed across many disciplines as it helps foster innovations like machine learning and artificial intelligence. Regardless of its significance, many students studying computer science and other disciplines often grapple with grasping basic programming language concepts, such as understanding logic, syntax, data structure, and data types. These challenges usually lead to very high rates of failure and loss of motivation among the students, therefore producing poor academic outcomes. This study investigates the unique programming challenges the students face, identifying some contributing factors and examining which challenges have more impact on the student. Moreover, it explores whether computing or non-computing students are more affected by these obstacles and reviews interventions to improve learning outcomes. The findings suggest best practices to enhance motivation and engagement in programming education, including introducing adaptive learning tools into the learning management systems, game-based applications, and AI-driven support systems personalized to meet each student's needs.
Evaluating Student Activity and Learning on Moodle: A Data-Driven Analysis and Insights of Usage Reports Mbodila, Muneinge; Elegbeleye, Femi
Studies in Learning and Teaching Vol. 6 No. 2 (2025): August
Publisher : CV Sinergi Ilmu dan Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46627/silet.v6i2.623

Abstract

Learning Management Systems (LMS) are extensively utilized to enhance teaching and learning in higher education institutions. These platforms provide invaluable insights into users' usage data and behavior within the online environment. Therefore, evaluating users' activity on these platforms is essential to maximize their effectiveness. This study aims to evaluate usage reports and engagement patterns and analyze online student learning activity using Moodle's tracking features. To achieve this, statistical and visualization techniques were employed to analyze student data from a year-long module delivered in a blended mode during the first semester at a South African university. The study utilized LMS log data to evaluate students' and instructors' usage patterns and engagement levels on the online platform, focusing on module-related activities. The data mining analysis revealed that LMS usage was significantly higher when students were on campus during the first semester and relatively lower when off-campus or in residence. In addition, no significant differences were observed in the type of LMS tools used or module activities across the eight months of the first semester. In conclusion, this data-driven approach and its findings underscore the importance of monitoring LMS activity.
Evaluating the Impact of Artificial Intelligence Enhanced Augmented Reality Tools on Social Interaction in Learners with Autism Spectrum Disorder Elegbeleye, Femi; Olusegun Oguntona; Ife Elegbeleye; Jose Lukose
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4969

Abstract

Autism Spectrum Disorder (ASD) is a cognitive developmental condition characterized by persistent deficits in social communication and interaction, alongside restricted and repetitive patterns of behavior. The global prevalence of ASD is estimated at approximately 1% in the general population, with higher rates observed in specific demographic groups. Individuals with ASD often experience challenges in interpreting social cues, initiating interactions, and participating in group settings, which can impede their academic and social development. This study examines how Augmented Reality (AR) and Artificial Intelligence (AI)-based interventions can complement or improve the social communication skills and behavioral patterns of individuals with ASD. A systematic literature review (SLR) was conducted, focusing on peer-reviewed studies published between 2019 and 2024, to assess the efficacy and practicality of these technologies in educational environments. The analysis covers engagement of visual boards, smartphones, tablets, and AR glasses, which are increasingly integrated into pedagogical strategies to enhance the learning experiences of students with ASD. The results demonstrate that AI-enhanced AR-based interventions significantly outperformed traditional teaching methods, with notable improvements in social interaction (70% vs. 50%), emotional recognition (60% vs. 40%), engagement (80% vs. 55%), communication skills (75% vs. 45%), and behavioral outcomes (65% vs. 50%). These technologies appear to support the development of social skills by providing interactive, personalized, and visually enriched learning environments. The outcomes of this research highlight the potential of AI-enhanced AR to complement traditional teaching methods, offering valuable insights for educators, therapists, and policymakers seeking practical approaches to support learners with ASD. Further empirical research is recommended to validate these findings across diverse educational settings
Comparative Evaluation of Adaptive Learning Models for Trustworthy Personalised Education Elegbeleye, Femi; Isong, Bassey
Studies in Learning and Teaching Vol. 6 No. 3 (2025): December
Publisher : CV Sinergi Ilmu dan Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46627/silet.v6i3.698

Abstract

Adaptive learning systems (ALSs) aim to personalize education by adjusting content and learning pathways in response to learner performance and behavior. This study conducts a comparative evaluation of four widely adopted adaptive learning models: Item Response Theory (IRT), Bayesian Networks (BN), Collaborative Filtering (CF), and Reinforcement Learning (RL). The evaluation integrates conceptual analysis and empirical simulation, using the large-scale EdNet dataset comprising over 131 million learner interactions. Each model was implemented in Python and assessed with standard metrics, including accuracy, precision, recall, and F1-score, with class imbalance addressed through SMOTE. Results show that RL consistently achieves the strongest performance across personalization accuracy, adaptability, and responsiveness to learner feedback, particularly under balanced conditions. BN closely follows, offering robust predictive accuracy alongside interpretability and cognitive modelling. CF shows moderate effectiveness, with improvements under SMOTE but limited adaptability in sparse or dynamic environments. IRT consistently performs lowest, maintaining value primarily in assessment contexts. Based on these findings, the study proposes a hybrid RL–BN framework, combining RL’s dynamic personalization with BN’s interpretability to create transparent, scalable, and pedagogically grounded ALSs. The results contribute evidence-based guidance for educators and developers in selecting and integrating adaptive learning models to meet diverse learner and institutional needs.
Co-Authors Esan, Omobayo Ayokunle Ife Elegbeleye Isong, Bassey Jose Lukose Mbodila, Muneinge Mbodila, Munienge Olusegun Oguntona