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Browsing PhD Theses by Author "Khakata, E. N. G."

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    Predicting student performance trajectory by analysing internet technology utilization behavioural patterns: case of Kenyan university students
    (Strathmore University, 2019) Khakata, E. N. G.
    Learning within universities today is continuously being revolutionized by the presence and advancements made on internet technology. The use of internet technology by students in the learning process is greatly influenced by the adoption and utilization of the technology within their learning institutions. However, despite the investments made by the institutions for the provision of internet technology, it is not possible to determine whether the technology positively contributes to better student performance. Similarly, the students expend a certain level of effort in order to use the technology in the learning process. Nonetheless, it is not possible to determine whether their effort contributes to positive performance in their studies. Likewise, taking into account a student’s behaviour and the result they expect to achieve at the end of a learning process, it is not possible to determine the degree to which the effort of the student (effectiveness of student effort) contributes to improved performance. Therefore, there is need to develop a student performance prediction model that considers the investments made by institutions, the student effort expended and the effectiveness of student effort in the utilization of internet technology. The scientific contribution of this thesis involved generation of the student performance trajectory and the development of a student performance prediction model that focuses on student behaviour within a learning environment at a specific instance in time. This model will help educational practitioners to analyse the existing contextual factors within an institution and how the factors influence student performance without carrying out a longitudinal research that will be time and resource intensive. This research considered three major factors in the prediction of student performance, that is, the investment costs, the student effort and the effectiveness of student effort. Investment costs consider student behavioural costs such as the time budget, the physical costs and the mental budget. Student effort encompasses the behavioural intentions and the actions of the students. The effectiveness of student effort considers the expected outcome from performing an action and the behavioural costs. The time budget was mainly influenced by time spent using internet technology and the physical costs are determined by the physical environment and general infrastructure in the universities. The behavioural intentions and actions of a student were examined using the capability of the student, the attitude of the student, the relevance of the technology in the learning process, the productivity achieved in using the technology and the knowledge of a student in the use of the technology in the learning process. The key findings of this research showed that internet technology was a useful resource in the learning process of students and the students had embraced its use in their learning with vigour. The students perceived the technology as easy to use and useful in their studies. They had sufficient knowledge in the use of the technology in learning and they had used the technology to accomplish a number of tasks in their learning process. Furthermore, some universities had invested sufficiently for the provision of internet technology and hence, their students had benefited greatly from the technology. The study concluded by formulating the input factors based on key research findings that were used in the prediction of the student performance perceptions and the student performance trajectory. These formed the major research output and they could be used in predicting student performance at a given instance in time. Keywords: Internet technology, internet utilization, Cobb-Douglas theorem, student performance, predictive model, prediction algorithms, decision tree.