RP-School of Engineering And Technology

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Browsing RP-School of Engineering And Technology by Author "Araka, Eric"

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    Integrating Generative Artificial Intelligence in Assessment Generation for Higher Education: Computer Science Use Case
    (IST-Africa, 2025) Kituku, Benson; Araka, Eric; Muuro, Elizaphan
    The overburdened lecturers today face the dual challenge of monitoring online attendance and ensuring active student engagement, while also receiving instant feedback on concept comprehension during live lectures. They are further pressed to extend practical problem-solving experiences beyond the classroom and deliver higher-order thinking assessments - all without increasing their workload. In response, this paper presents a one-year experiment involving 590 students across seven computer science units. The study integrated generative AI-generated questions into live lectures, weekly discussion forums, and both formative and summative exams. The findings reveal that, with ethical use and proper human-inthe-loop, AI can significantly boost student engagement, promptly rectify misconceptions, and foster collaborative learning outside traditional settings and offer diverse question styles. However, given potential pitfalls such as low-quality outputs and overreliance, instructors must adhere to best practices and maintain rigorous oversight to ensure that assessments remain balanced, engaging, and of high quality, eventually benefiting both educators and learners.
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    Network Intrusion Detection Using Extreme Machine Learning Algorithm with Extreme Gradient Boosting for Feature Selection
    (Journal of the Kenya National Commission for UNESCO, 2024) Ntwiga, Alex; Araka, Eric
    This study addresses the challenge of improving the performance of the Extreme Learning Machine model, particularly in accurately identifying minority classes in unbalanced datasets like UNSW-NB15 and NSLKDD. The research question guiding this study is: How can we improve the ELM model's performance for better accuracy and minority class recognition in network intrusion detection? The methodology includes balancing the dataset to address the issue of poor minority class identification, using XGBoost for feature selection to reduce the curse of high data dimensionality, Particle Swarm Optimization finally used to optimize the model. The results show that the proposed approach outperformed other models when tested on the NSL-KDD dataset, achieving accuracies of 94.29% for binary classification and 89.02% for multiclass classification. However, on the UNSW-NB15 dataset, the model achieved a binary accuracy of 90.79%, which was lower than the performance of Random Forest (93.02%) and Decision Tree (92.76 In the multiclass classification the accuracy achieved was 78.79%, indicating underperformance compared to the other state-of-the-art models. The study concludes that although the suggested approach performs well in binary classification, future studies need to focus on improving detection accuracies of datasets that are heavily unbalanced with multiple classes like UNSW-NB15 dataset.
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    Promoting University Students’ Self-Regulated Learning Skills on E-Learning Platforms Using Educational Data Mining
    (IST-Africa, 2025) Araka, Eric; Wario, Ruth; Maina, Elizaphan
    Current e-learning platforms used by higher education institutions for open and distance learning often lack automation tools that offer personalized support to students. As a result, learners must take an active role in managing their learning process, as emphasized by the self-regulated learning theory. Given the flexibility of online learning, where students are not bound by fixed schedules, they must develop self-discipline and autonomy to effectively navigate and control their learning experience. This study examines the potential of educational data mining techniques to enhance students' self-regulated learning skills. A true experimental research design was deployed to assess the effectiveness of the developed educational data mining techniques -based interventions in fostering self-regulated learning. The study sample consisted of University students enrolled in a 12-week Data Science with Python course. Results from an independent t-test indicated that educational data mining interventions positively influenced students’ self-regulatory skills by strengthening their cognitive and behavioral learning strategies. However, no significant difference was found in final academic performance between students who received educational data mining interventions and those who received instructor-led support. By providing data-driven support, the study contributes to efforts aimed at improving student retention, reducing dropout rates, and enhancing academic performance in online learning environments. These findings provide a foundation for future research on integrating intelligent automation to support SRL in higher education.
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    Students’ Perceptions on the Use of Generative AI in Enhancing Teaching and Learning Computer Science Courses
    (IST-Africa, 2025) Mwaniki, Susan; Araka, Eric; Kituku, Benson; Maina, Elizaphan
    This research study examines the perception of third-year computer science students in Kenya towards the use of Generative Artificial Intelligence tools in their studies. The researchers used descriptive research design to understand student attitudes, the perceived usefulness of Generative Artificial Intelligence, and the challenges they face. The study finds that students generally see Generative Artificial Intelligence tools as beneficial for learning, especially in areas like coding and research. However, they also identify concerns about over-reliance on Generative Artificial Intelligence, accuracy of information, and ethical considerations, such as plagiarism. The study concludes that Generative Artificial Intelligence can be valuable in computer science education, however, it should be used reliably and balanced with traditional teaching methods to ensure critical thinking and creativity.