MST-School of Engineering and Technology

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This collections contains bibliographic information and abstracts of Master theses and dissertation in the School of Engineering and Technology held in Kenyatta University Library

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Browsing MST-School of Engineering and Technology by Subject "E-Learning"

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    An Intelligent Based System for Supporting Personalised E-Learning
    (kenyatta university, 2023) Kivuva, Faith Ngami; Elizaphan Maina
    Most traditional e-learning systems fail to provide the intelligence to guide a learner according to their learning style. However, intelligent agents can be created to perform the role of guide to a student depending on a predetermined learning style. In view of this, the study discusses how to design, develop and implement intelligent agents for supporting personalized e-learning based on a predetermined learning style. The main objective of this study was to design and implement an intelligent e-learning system based on intelligent agents for supporting personalized e-learning. The system, which is based on intelligent agents, provides some intelligence and supports dynamic learning. Each learner has different levels of achievement depending on their learning styles and gets personalized feedback/recommendations. Three intelligent agents were developed; a learner agent, a tutor agent, and an information agent. The learner agent, which has an AI engine, uses deep neural networks to provide a recommendation to the learners based on their learning styles. The tutor agent accesses what the learner has accessed and passes this information to the learner agent which then recommends the appropriate materials. The information agent presents the recommendations/feedback of the learners through the Moodle user interface. The learning styles of the students are determined by filling out a Visual, Aural, Read/Write, and Kinesthetic (VARK) questionnaire. The three agents were developed using the Prometheus methodology. They were also tested and integrated into Moodle Learning Management System (LMS). This integration allows learners who are using LMS such as Moodle to learn based on their learning style. The results indicate that it is possible to train a learner agent using deep neural networks and provide personalized learning to the learner based on the learning style. Future studies need to focus on using data collected in a learning management system to identify learner styles instead of using the VARK questionnaire. Additionally, it is necessary to use other learning styles models, such as the Filder-Silverman model, and the Kolb learning style model among others, to identify learning styles and conduct an experimental study to determine their effectiveness in personalized learning with intelligent agents.