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dc.contributor.advisorElizaphan Mainaen_US
dc.contributor.authorNdirangu, Peter Ndegwa
dc.date.accessioned2023-08-10T12:38:45Z
dc.date.available2023-08-10T12:38:45Z
dc.date.issued2023
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26757
dc.descriptionThis Project Report Is Submitted for the Partial Fulfillment of the Requirements for the Award of the Degree of Masters of Science in Computer Science in the School of Engineering and Technology of Kenyatta University, April 2023.en_US
dc.description.abstractThe examination process is a key activity in evaluating what the learner has gained from the study. Institutions of Higher Learning (IHL) perform the activity by administering tests which comprises of questions and answers. Cognitive level, weight of the question, and topic coverage are key factors to consider when setting exams. The world today has largely focused on the automation of exam generation which has been ongoing with dire need during the period of the Covid-19 pandemic when education was greatly affected, leading to embracing online learning and examination. The process has taken shape; however, the automation process can be improved by incorporating machine learning algorithms in the process of setting examination. In view of this, the project focused on implementation of a question classification model that uses Neural-Network algorithm (NN) and Natural Language Processing (NLP) to determine questions cognitive levels based on the revised Bloom's Taxonomy. The iterative method of software development was adopted to provide room for continuous improvement. The developed model was put under test with a couple of questions obtained online. The effectiveness of the model was determined by subjecting it into database of 600 questions resulting to an accuracy of about 71%. An Application Programming Interface (API) and Moodle Learning Management System (LMS) plugin were consequently developed to allow integration of the model with an existing system. The deep learning approach was applied to predict cognitive levels of questions based on Bloom’s taxonomy and the resulting questions were made available to the instructor through the LMS interface. Future research should focus on the use of convolutional reinforcement learning to establish its effectiveness in question classification as well as perform comparison with various algorithms.en_US
dc.description.sponsorshipKenyatta Universityen_US
dc.language.isoenen_US
dc.publisherKenyatta Universityen_US
dc.subjectAutomated Examination Generationen_US
dc.subjectNatural Language Processingen_US
dc.subjectArtificial Neural Networken_US
dc.titleAutomated Examination Generation using Natural Language Processing and Artificial Neural Networken_US
dc.typeThesisen_US


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