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All Journal Journal of Digital Learning and Distance Education Radinka Journal of Science and Systematic Literature Review
Ejenarhome Otega Prosper
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Evaluating the Impact of Blended Learning Models on Higher Education Outcomes: A Multidimensional Analysis Oise, Godfrey; Ejenarhome Otega Prosper; Oyedotun Samuel ABIODUN; Onwuzo Chioma JULIA
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 2 (2025): Journal of Digital Learning and Distance Education (JDLDE)
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/jdlde.v4i2.535

Abstract

Blended learning (BL), which combines online digital tools with traditional face-to-face instruction, has gained increasing prominence in higher education, particularly since the COVID-19 pandemic. This study conducts a systematic review of 50 peer-reviewed empirical studies (2020–2024) to evaluate the impact of BL on academic performance, student engagement, and learner satisfaction. The results reveal that BL enhances learning outcomes when supported by responsive instruction, flexible access, and structured digital platforms, particularly in STEM disciplines. However, the effectiveness of BL is highly context-dependent. Disciplines relying on interpretive and dialogic learning, as well as under-resourced institutions, often experience minimal or negative effects, especially in asynchronous-heavy models. The review also identifies a decline in student engagement beyond the fourth week in flex-only formats, suggesting that synchronous interaction is critical for sustained motivation and retention. Key barriers to effective implementation include faculty workload, digital inequality, and institutional inertia. Addressing these challenges requires structured faculty development, investment in accessible technology, and alignment with discipline-specific pedagogy. This review affirms the pedagogical value of BL but emphasizes the need for inclusive, adaptive, and strategically supported approaches to ensure its sustainable integration across diverse educational settings.  
Intelligent Waste Management Systems: A Review of IoT, Deep Learning, and Optimization Techniques for Sustainable E-Waste and Solid Waste Handling Oise, Godfrey; Oyedotun Samuel ABIODUN; Onwuzo Chioma JULIA; Ejenarhome Otega Prosper
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 2 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i2.508

Abstract

This review addresses the urgent environmental and health issues posed by the rapid growth of electronic waste (e-waste) and municipal solid waste (MSW), highlighting the role of emerging technologies in crafting sustainable waste management solutions. It explores the integration of the Internet of Things (IoT), deep learning, and optimization algorithms in enhancing waste classification, recycling, and disposal. Key innovations include IoT-enabled smart bins for real-time monitoring, deep learning models like CNNs achieving up to 97% sorting accuracy, and optimization techniques that improve energy efficiency and scalability. The paper synthesizes findings from over 50 studies and emphasizes both technical advances and implementation challenges, such as data limitations, model interpretability, and the lack of robust policy frameworks. Future research directions include explainable AI, edge computing, and global standardization of e-waste regulations. The review is intended for researchers, developers, and policymakers working toward circular economy principles and sustainable smart city solutions.
Student Success Prediction in Digital Learning Environments Oise, Godfrey perfectson; Ejenarhome Otega PROSPER; Augustine Osazee AIRHIAVBERE; Agwam Gladys IFEOMA
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 6 (2025): Journal of Digital Learning and Distance Education (JDLDE)
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/jdlde.v4i6.592

Abstract

Acknowledging the risk of perpetuating bias in AI-driven student success prediction, this study introduces a fairness-conscious machine learning framework that aims to balance predictive accuracy with ethical responsibility in digital learning settings. Using a dataset of 5,000 anonymized student records, three models, Random Forest (RF), Gradient Boosting (GBM), and Support Vector Machine (SVM), were developed to forecast academic outcomes. Model evaluation combined standard metrics (accuracy, precision, recall, and F1-score) with fairness measures such as demographic parity, equal opportunity, and disparate impact ratio to explore trade-offs between accuracy and fairness. Results indicated that while RF and GBM had slightly higher accuracy, SVM demonstrated more consistent fairness across demographic groups, emphasizing its stronger balance between predictive power and equity. A fairness-centered optimization method was applied to embed fairness constraints directly into model training, showing that both accuracy and fairness can be improved simultaneously rather than being in opposition. The framework integrates fairness throughout data preprocessing, model development, and post-prediction review, promoting transparent and responsible decision-making. By aligning with international ethical AI standards from UNESCO and the OECD, this research provides a practical pathway for creating educational prediction systems that enhance inclusion, minimize bias, and build trust in digital learning environments.  
Co-Authors Agwam Gladys IFEOMA Augustine Osazee AIRHIAVBERE Oise, Godfrey Oise, Godfrey perfectson Onwuzo Chioma JULIA Oyedotun Samuel ABIODUN