Browsing by Author "Maina, Elizaphan"

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    An Adaptive Gamification Model for E-Learning
    (IST-Africa 2020 Conference Proceedings, 2020) Kamunya, Samuel; Mirirti, Evans; Oboko, Robert; Maina, Elizaphan
    Gamification has gained currency in the recent past and has widely being deployed in various disciplines such as health, education, marketing amongst others. The main driving factor of deploying gamification is due to its motivational element. Gamification, particularly in education, has been used to motivate and elicit engagement in learners. However the implementation of gamification within elearning platforms has been of the "One size fits all" i.e., uniform application of gamification elements to all learners, albeit learners possess different characters which are distinct from each other. The need to embrace the "One size does not fit all" approach necessitates introduction of adaptive gamification. This study sought to develop an adaptive gamification model. The study used the Design science research methodology (DSRM) using the problem instantiation approach to develop the adaptive gamification model, which can be used to guide and implement adaptivity within e-learning platforms. In the development of the model the study reviewed 15 adaptive gamification studies from which the key components of an adaptive gamification model were synthesized and a final model proposed.
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    A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments
    (IGI Global, 2021) Araka, Eric; Oboko, Robert; Maina, Elizaphan; Gitonga, Rhoda K.
    Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.
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    Enhancing Exploratory Learning Using Computer Simulation in an E-learning Environment: A Literature Review
    (Center for Open Access in Science, 2020) Kanyaru, Paul; Maina, Elizaphan
    Computer simulation has been shown to elicit exploratory behavior and creativity in learners. Various researches have indicated that during an exploratory learning process students can acquire knowledge either through inquiring or exploring an open learning environment. Further, the research shows that as opposed to instruction-based learning, exploratory learning is mainly based on self-motivation by learners. Therefore, computer simulation when used to enhance exploratory learning concept especially in an e-learning platform, has been seen to achieve the learning objectives as explained by Bloom taxonomy. In this regard, computer simulation has been seen to help learners conceptualize important concepts especially in science subjects. Additionally, the use of simulation is regarded as an aid to improving understanding of various concepts as well, as helping increase breadth of knowledge.
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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.
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    Using Machine Learning Algorithms to Predict Students’ Performance and Improve Learning Outcome: A Literature Based Review
    (Stratford Peer Reviewed Journals and Book Publishing, 2020) Ofori, Francis; Maina, Elizaphan; Gitonga, Rhoda
    The application of machine learning techniques in predicting students’ performance, based on their background and their in-term performance has proved to be a helpful tool for foreseeing poor and good performances in various levels of education. Early prediction of students’ performance is useful in taking early action of improving learning outcome. The prediction of the student's academic performance is important as it helps increase graduation rates by appropriately guiding students, guiding changes in university academic policies, informing instructional practices, examining efficiency and effectiveness of learning, providing meaningful feedback for teachers and learners and modifying learning environments. A high prediction accuracy of the students’ performance is helpful to identify the low performance students at the beginning of the learning process. However, to achieve these objectives, large volume of student data must be analyzed and predicted using various machine learning models. Moreover, it is not clear which model is best in predicting performance and which machine learning model is appropriate in improving learning in among students. The paper through intensive literature review attempts to identify best machine learning model in predicting student performance and appropriate machine learning model in improving learning. The empirical review indicated contentious results on machine learning model that best predicts students’ performance. Moreover, it is not clear among the various machine learning algorithms which one derives the best approach in predicting students’ performance while improving learning outcome. The varying prediction level by various machine learning models may be as a result of differences in socioeconomic. It may also be important to note that student’s academic performances are affected by many factors, like socioeconomic factors of students like family income, parental level of education and employment status of students or parents but are not considered when testing the accuracy of various machine learning models in predicting students’ performance. Moreover, the various machine learning models did not identify the most appropriate machine learning model in improving students’ outcome. Most models focused largely in predicting students’ performance without considering mechanisms to improve learning outcome of students. As a result, it is important to test the accuracy of various machine learning models that best predicts students’ performance and the one that is most appropriate in improve learning outcome while considering socio economic and demographic factors of the students. The study makes a conclusion that predicting students’ performance is of the highest priority for any learning institution across the globe. Using various machine learning methods to accurately predict student’s performance would be highly required. It is important to accurately rank machine models based on their prediction capabilities in predicting students’ performance and in improving learning outcome.