dc.contributor.author | Ismaila, Lukman Enegi | |
dc.date.accessioned | 2019-08-05T13:24:07Z | |
dc.date.available | 2019-08-05T13:24:07Z | |
dc.date.issued | 2017-12-09 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/123456789/4889 | |
dc.description.abstract | Recent 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.sponsorship | AUST and AfDB. | en_US |
dc.language.iso | en | en_US |
dc.subject | 2017 Computer Science and Engineering Theses | en_US |
dc.subject | Prof. Dr Steve Adeshina | en_US |
dc.subject | Ismaila, Lukman Enegi | en_US |
dc.subject | Active Appearance Model | en_US |
dc.subject | Adaptive Learning framework | en_US |
dc.subject | Smart Learning Environment | en_US |
dc.subject | Multimedia Learning facilities | en_US |
dc.subject | Facial Expression Recognition | en_US |
dc.subject | Deep learning | en_US |
dc.title | Adaptive Multimedia Learning Framework with Facial Recognition | en_US |
dc.type | Thesis | en_US |