A Model to measure online student engagement using eye tracking and body movement analysis

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dc.contributor.authorMido, Jude Austin
dc.date.accessioned2022-03-23T07:32:53Z
dc.date.available2022-03-23T07:32:53Z
dc.date.issued2021
dc.descriptionA Thesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technologyen_US
dc.description.abstractMany institutions are adopting remote learning as way of expanding and offering their programs to mostly undergraduate students and adults seeking further education or training, and as a way of doing this at low costs; without constructing new buildings. However, measuring student engagement so as to focus attention on students who are struggling and manage students in a large class presents an extra challenge to teachers, when they have to do it virtually on eLearning platforms. The focus of this study was to build a model to measure student engagement using eye tracking and body movement analysis through web cameras, to help in tracking student engagement. The proposed solution is aimed at assisting in maintaining student engagement during remote classes, as it would be in a traditional classroom, and to enhance the learning process for both the student and the teacher. The proposed solution aimed to achieve this using computer vision algorithms; using computer vision to track the eyes and detect the hand, then analyze the movements to provide indices used to measure the engagement level. The model was tested using a prototype that analyzed recorded videos of students attending a remote class. The model achieved an accuracy of above 80%.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12751
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectStudents engagementen_US
dc.subjectOnline student engagementen_US
dc.subjectMachine learningen_US
dc.subjectEye trackingen_US
dc.subjectComputer visionen_US
dc.subjectBody movement analysisen_US
dc.titleA Model to measure online student engagement using eye tracking and body movement analysisen_US
dc.typeThesisen_US
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MSIT Theses and Dissertations (2021)