Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Journal of Digital Learning and Distance Education Radinka Journal of Science and Systematic Literature Review
Oise, Godfrey perfectson
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Digital Learning and Distance Education

 1 
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.  
DistilBERT-Based Detection of AI-Generated Text in Online Assessments: Ethical and Pedagogical Implications EJENARHOME, Prosper Otega; Oise, Godfrey Perfectson; AIRHIAVBERE, Augustine Osazee; Odimayomi, Joy Akpowehbve
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 9 (2026): 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.v4i9.651

Abstract

The rapid shift toward online and distance learning has positioned digital assessment as a cornerstone of higher education while also challenging academic integrity due to the accessibility of generative artificial intelligence (GenAI). While technical research into AI detection is expanding, there remains a critical gap in understanding how detection outcomes can be ethically and pedagogically integrated into digital learning environments. This study evaluates a fine-tuned DistilBERT-based model for detecting AI-generated text, situating its technical performance within a learning-centered framework. Using a large-scale dataset of over 28,000 human-written and AI-generated essays, the model demonstrated exceptional robustness, achieving an overall accuracy of 99%, an AUC of 0.9999, and balanced F1 scores of 0.99. Beyond technical metrics, this research redefines AI detection by shifting the narrative from a punitive, surveillance-oriented mechanism to a supportive learning analytics tool. By interpreting detection results alongside instructional indicators, the study demonstrates how these technologies can inform assessment redesign, enhance transparency, and foster learner trust. The findings contribute to the field of digital education by providing a roadmap for the responsible integration of AI detection into assessment ecosystems, ensuring that technological precision serves the broader goals of fairness and pedagogical innovation.
Co-Authors Agwam Gladys IFEOMA AIRHIAVBERE, Augustine Osazee Augustine Osazee AIRHIAVBERE Ejenarhome Otega Prosper EJENARHOME, Prosper Otega Konyeha, Susan Odimayomi, Joy Akpowehbve