Machine Learning based Smart Course Feedback System for UTAS Oman

Education plays key role for the development of any country. Oman provides strong education for their citizens to develop the country. Oman vision 2040 is attract the talents in the labour market. This is the reason Oman plays major attention to provide quality education for all the citizen. To provide quality education, courses must be developed as per Higher Education Institute (HEI) Quality Assurance (QA) Standard. HEI measure the quality through course feedback, faculty assessment, adding facilities and developing research environment. Most of the HEI and UTAS Suhar collects course feedback survey through multiple choice questions. The need for the new system is that some limitations of the old system are discovered. The problem with the old system is that the evaluation may not be very satisfactory or very effective. Sometimes some students may evaluate on behalf of their colleagues. In addition, some students do not care about a complete and clear assessment for everyone. Therefore, it is possible to manipulate the assessment. To avoid the shortcomings of the assessment in the system, a new system will be implemented using deep learning technology, where the algorithms that analyse the videos include a time during which the feelings of the students are accurately identified, this paper focuses on capture the state of students' images during learning on the University campus and by detecting their emotions through Machine learning that analyzes their emotions. The proposed system will capture their student’s emotion when they are attending the class in UTAS Suhar, based on their emotion, we will study whether they are able to understand the class and feel environment is good. Detect the emotions carried by the student during the class activities using Machine learning techniques. Because feelings give the best feedback of the students’ activities in the campus. Based on students' emotions, the proposed system will give effective feedback about the course and faculty. Through the system, the data will be saved in the cloud, then the data and evaluations will be sent to the course instructor, so the teaching method and course presentation will be upgraded based on the results.