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Adaptive Multimedia Learning Framework with Facial Recognition

dc.contributor.authorIsmaila, Lukman Enegi
dc.description.abstractRecent breakthrough in mobile technology, wireless communication and sensing ability of smart devices promote the ease to detect real-world learning status of students as well as the context aware for learning. Targeted information can be provided to individual students in the right place and at the right time. This work is one of the three major modules of our Smart Learning Framework, others include Multimedia Module Contents (MMC) and Learning Style Index (LSI). However, this module of our work aimed to perfect efforts to correctly make decision during an academic learning process. This was based on the fact that adaptive decisions can be made to protect learner enthusiasm, promote learning grid and enhances general understanding of an adaptive learning environment if user’s immediate behavior and concern is well considered. This approach implements facial expression recognition on a smart phone (Android) using effective SDK. This enables correct detection of facial expression for further understanding of the meaning in a learning environment. The output of this module is used for learners Behavior Analysis which then provide result of general evaluation of individual learner.en_US
dc.description.sponsorshipAUST and AfDB.en_US
dc.subject2017 Computer Science and Engineering Thesesen_US
dc.subjectProf. Dr Steve Adeshinaen_US
dc.subjectIsmaila, Lukman Enegien_US
dc.subjectActive Appearance Modelen_US
dc.subjectAdaptive Learning frameworken_US
dc.subjectSmart Learning Environmenten_US
dc.subjectMultimedia Learning facilitiesen_US
dc.subjectFacial Expression Recognitionen_US
dc.subjectDeep learningen_US
dc.titleAdaptive Multimedia Learning Framework with Facial Recognitionen_US

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  • Computer Science105

    This collection contains Computer Science Student's Theses from 2009-2022

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