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All Journal Journal of Digital Learning and Distance Education Radinka Journal of Science and Systematic Literature Review Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Oise, Godfrey
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Journal of Digital Learning and Distance Education

 1 
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.  
The Integration of Internet of Things (IoT) in Smart Classrooms: Opportunities, Challenges, and Future Trajectories Oise, Godfrey; Cyprian C. KONYEHA; COMFORT, Olayinka Tosin; Konyeha, Susan; Emmanueld, Chukwuma Ozobialu
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 3 (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.v4i3.537

Abstract

The integration of the Internet of Things (IoT) into educational environments signifies a transformative shift towards smart classrooms, enabling real-time data-driven instruction, environmental optimization, and personalized learning experiences. This study explores the opportunities, challenges, and future directions of IoT deployment in academic settings through a mixed-methods approach that combines quantitative analysis, qualitative interviews, and IoT-edge data assessment. Survey responses from 150 educators and interviews with 20 key stakeholders revealed significant adoption rates and pedagogical benefits, including enhanced engagement and individualized feedback. However, critical challenges such as data privacy, cybersecurity risks, limited teacher training, and infrastructure disparities hinder widespread implementation. A machine learning framework utilizing Random Forest classification was applied to a custom IoT-edge dataset, uncovering correlations between environmental variables and student behavior. High temperatures negatively affected classroom occupancy, while increased light intensity correlated with heightened engagement. Model evaluation yielded strong performance metrics, including an accuracy of 95% and an AUC of 0.99, highlighting the predictive power of features like learning outcomes and engagement scores. The findings emphasize the dual importance of technical readiness and pedagogical adaptation, advocating for policy support, ethical data governance, and teacher capacity-building to fully realize IoT’s potential in shaping adaptive, equitable, and intelligent learning ecosystems.
Predicting and Preventing Academic Misconduct Using Behavioral Analytics: An Ethical Framework for Fair Detection and Human Oversight Oise, Godfrey
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 7 (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.v4i7.594

Abstract

This study introduces the Ethical Behavioral Analytics Framework (EBAF), a fairness-driven and explainable artificial intelligence system designed to predict and prevent academic misconduct. The framework integrates behavioral analytics, deep learning (LSTM), and human oversight to ensure ethical transparency and accountability in academic integrity management. By combining behavioral indicators such as submission timing, editing duration, and engagement regularity with textual features, EBAF identifies deviations from normal learning behavior that may indicate misconduct. Using a dataset of student behavioral and performance data sourced from Kaggle, the model achieved an overall accuracy of 85%, effectively distinguishing between authentic and plagiarized submissions while maintaining minimal bias. The incorporation of explainable AI tools, including SHAP and LIME, provided interpretable reasoning behind predictions, allowing educators to understand and validate model decisions. A human-in-the-loop mechanism further ensured that automated outputs were reviewed contextually, promoting fairness, accountability, and trust. The findings demonstrate that ethical and explainable AI can coexist with high predictive performance, advancing the responsible application of machine learning in education. By embedding fairness auditing, transparency, and human oversight, EBAF transforms academic misconduct detection from a punitive process into a preventive and educational approach. This work contributes to both research and practice by aligning computational intelligence with ethical accountability. Future research will expand the framework across diverse academic environments, incorporating multimodal behavioral data and adaptive feedback systems to enhance fairness, interpretability, and scalability in AI-based academic integrity solutions.
Co-Authors Atake, Onoriode Michael Babalola Eyitemi , Akilo COMFORT, Olayinka Tosin Cyprian C. KONYEHA Ejenarhome Otega Prosper Ejenarhome Otega Prospera EJENARHOME, Prosper Emmanueld, Chukwuma Ozobialu IGBUNU, Richard Jenarhome, Otega Prosper Konyeha, Susan Nkem Belinda, Unuigbokhai Nwabuokei, Clement Nwabuokei, Onyemaechi Clement Onwuzo Chioma JULIA Oyedotun Samuel ABIODUN Ozobialu, chukwuma Emmanuel