Research Article
Adoption of Generative AI and Learning Analytics for Personalized Learning in Nigerian Higher Education: Effects on Student Engagement, Learning Outcomes, and Academic Integrity
Ayonote Williams Elenode*
Issue:
Volume 11, Issue 1, February 2026
Pages:
1-5
Received:
28 October 2025
Accepted:
20 December 2025
Published:
16 January 2026
Abstract: This study examines the integration of generative artificial intelligence (GenAI) and learning analytics (LA) in fostering personalized learning within Nigerian higher education institutions. Specifically, it explores how these emerging technologies influence student engagement, academic performance, and perceptions of academic integrity in digitally mediated learning environments. A mixed-method research design was adopted, involving 420 undergraduate students and 60 academic staff drawn from three Nigerian universities. Quantitative data were collected through structured questionnaires assessing GenAI usage patterns, learning analytics engagement, levels of digital literacy, and integrity perceptions, while qualitative insights were obtained from semi-structured interviews with academic staff and information and communication technology (ICT) administrators. The findings reveal that 64% of respondents actively use GenAI tools such as ChatGPT and Gemini to support learning activities, while 58% regularly access learning analytics dashboards for performance monitoring and feedback. Regression analysis indicates that GenAI-assisted learning and engagement with learning analytics jointly predict improved academic performance (β = 0.36, p < 0.01) and higher levels of student engagement (β = 0.42, p < 0.001). However, the study also finds that unregulated use of GenAI tools is positively associated with increased concerns about plagiarism and academic misconduct (r = 0.31, p < 0.05). The study concludes that the pedagogically guided integration of GenAI and learning analytics can significantly enhance personalized learning experiences in Nigerian higher education. Nevertheless, this potential can only be realized through the development of robust governance frameworks that emphasize ethical use, digital literacy, academic integrity, and data privacy. The study therefore recommends targeted capacity-building for educators, the establishment of transparent institutional AI policies, and continuous evaluation of the pedagogical impact of GenAI technologies.
Abstract: This study examines the integration of generative artificial intelligence (GenAI) and learning analytics (LA) in fostering personalized learning within Nigerian higher education institutions. Specifically, it explores how these emerging technologies influence student engagement, academic performance, and perceptions of academic integrity in digital...
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Research Article
Development of Rational Design Parameters for the Traction Drive and Motor-Wheel of an Electric Vehicle
Tukhtabayev Mirzokhid*,
Valiev Bobur
Issue:
Volume 11, Issue 1, February 2026
Pages:
6-11
Received:
11 December 2025
Accepted:
26 December 2025
Published:
20 January 2026
Abstract: The article analyzes main directions of development of soft magnetic powder materials. It includes research on certain materials developed using soft magnetic materials in production of electric motors. The elimination of known shortcomings in production of electric motors, acceleration of its assembly process is studied. The production of magnetic materials with low energy loss during magnetization reversal is one of the urgent problems of industry today. Despite the fact that research and development of such materials has been carried out since the beginning of the last century, studying the mechanism of magnetization reversal and improving the quality of these materials is still relevant today. This is due to the fact that soft magnetic materials are widely used in various technical devices (electric generators, electric motors, measuring instruments, inductors, etc.), the quality level of which is determined by the properties of modern varieties of such materials. The purpose of the study. Acceleration of electric motor development processes, increase in economic efficiency and study of the performance of electric motors. Results of the study. During the period of use of the materials, small dimensions of the product are obtained, in which, when the directional effect of the magnetic fluxes is changed, a reverse change in magnetization is achieved throughout the thickness of the part.
Abstract: The article analyzes main directions of development of soft magnetic powder materials. It includes research on certain materials developed using soft magnetic materials in production of electric motors. The elimination of known shortcomings in production of electric motors, acceleration of its assembly process is studied. The production of magnetic...
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Research Article
Recognition of Urban Buildings in Challenging Images Using Bag of Features and SVM
Issue:
Volume 11, Issue 1, February 2026
Pages:
12-19
Received:
22 December 2025
Accepted:
5 January 2026
Published:
23 January 2026
DOI:
10.11648/j.eas.20261101.13
Downloads:
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Abstract: Urban building recognition plays a central role in applications such as urban mapping, heritage documentation, autonomous navigation, and smart city monitoring. Although recent advances have been driven mainly by deep learning approaches, classical visual pipelines remain an attractive alternative in scenarios where datasets are limited, interpretability is required, and computational resources are constrained. In this study, a systematic evaluation of a Bag-of-Features (BoF) representation combined with a Support Vector Machine (SVM) classifier is presented for urban building recognition using the Sheffield Building Image Dataset (SBID). The experimental protocol includes dataset balancing, a reproducible training–testing split, and an extensive investigation of visual vocabulary sizes ranging from 100 to 3000 visual words. The results indicate that increasing the vocabulary size generally improves recognition performance up to a saturation point, with the best trade-off achieved using 2000 visual words. Under this configuration, the proposed approach achieved an overall accuracy of 97.5% while maintaining an average inference time below 25 ms per image, demonstrating competitive performance with low computational cost. A detailed analysis based on confusion matrices and per-class metrics (accuracy, precision, recall, and F1-score) shows that most building categories were recognized with high reliability, while misclassifications were mainly concentrated among visually similar façade types. These findings confirm that BoF representations, when properly tuned, remain highly effective for structured urban recognition tasks. Moreover, the obtained results are consistent with those commonly reported in the literature for the same dataset and problem domain, reinforcing the robustness of the proposed pipeline. Overall, the results highlight the continued relevance of classical computer vision methods in contexts where transparency, reproducibility, and efficiency are essential. Future work will investigate hybrid strategies that combine BoF representations with deep convolutional descriptors, as well as more robust evaluation protocols, aiming to improve generalization across different building datasets and urban environments.
Abstract: Urban building recognition plays a central role in applications such as urban mapping, heritage documentation, autonomous navigation, and smart city monitoring. Although recent advances have been driven mainly by deep learning approaches, classical visual pipelines remain an attractive alternative in scenarios where datasets are limited, interpreta...
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