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All Journal Journal of Digital Learning and Distance Education Radinka Journal of Science and Systematic Literature Review
Oyedotun Samuel ABIODUN
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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.
Spatiotemporal Water Quality Modeling Using Deep Learning Architectures Oise, Godfrey; Ejenarhome Otega Prosper; Oyedotun Samuel ABIODUN
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (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.v3i3.519

Abstract

Water quality monitoring is vital for ensuring public health, environmental sustainability, and economic resilience. Traditional monitoring techniques, while precise, often fall short due to high costs, labor intensity, and limited temporal and spatial resolution barriers that are increasingly critical amid accelerating urbanization, climate change, and pollution. This study explores the application of deep learning architectures to spatiotemporal water quality modeling, leveraging diverse datasets comprising historical records, sensor readings, and government sources. Supervised learning techniques were evaluated for predictive and classification tasks, including Support Vector Regression (SVR), Random Forests, XGBoost, and Decision Trees. SVR yielded strong regression performance for Water Quality Index (WQI) prediction with an R² of 0.9693 and low mean squared error, while XGBoost and Decision Trees demonstrated robust classification accuracy above 94%, with Decision Trees excelling in macro-averaged metrics. Unsupervised learning using DBSCAN revealed moderate clustering potential, but also emphasized the limitations of density-based approaches for noisy environmental data. Exploratory analyses offered insights into parameter distributions and interdependencies, including Kernel Density Estimation, correlation heatmaps, box plots, PCA, and t-SNE. While the study confirms the potential of AI in water quality monitoring, it also underscores challenges such as data imbalance, limited minority class precision, and the need for interpretable and scalable models. Future work should integrate explainable AI, edge computing, and hybrid domain-informed models to foster real-time, equitable, and sustainable water monitoring solutions aligned with SDG 6. This research demonstrates the promise of deep learning in transitioning water quality management from reactive to predictive paradigms.
Utilizing Deep Learning to Influence Design Decisions and Predict Future Scenarios Oise, Godfrey; Ejenarhome Otega Prospera; Oyedotun Samuel ABIODUN
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (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.v3i3.523

Abstract

Deep learning is increasingly transforming design practice by enabling data-driven decision-making, predictive analysis, and the automation of visually complex tasks. This study investigates the application of a Convolutional Neural Network (CNN) to the CIFAR-10 dataset to demonstrate how image-based deep learning models can support design analysis and future-scenario prediction across fields such as architecture, product development, and urban planning. The model was developed using a sequential CNN architecture with convolutional, pooling, batch normalization, and dropout layers and trained over 20 epochs using the Adam optimizer. Performance evaluation employed accuracy, precision, recall, F1-scores, confusion matrices, and ROC–AUC curves to provide a transparent and interpretable assessment of model behavior. The CNN had a training accuracy of 89% and a test accuracy of 77%. Its macro-averaged precision, recall, and F1-scores were 78.8%, 79.0%, and 77.5%, respectively. Results show strong discriminative capability but also highlight misclassification challenges among visually similar classes and signs of overfitting. These findings emphasize both the potential and limitations of deep learning when applied to design workflows. The study concludes that CNN-based visual analysis can meaningfully inform design decisions, identify hidden patterns, and support predictive scenario modeling, underscoring the need for interpretability and responsible AI integration in design disciplines.
Co-Authors Ejenarhome Otega Prosper Ejenarhome Otega Prospera Oise, Godfrey Onwuzo Chioma JULIA