A Neural-Assessment System Based on Emirates (QFE).

In order to strengthen the teaching and learning phase it is assumed that the assessment of course results dependent upon student grades is important. Our analysis methods and workflows leverage the benefits of AI, for example the capacity to evaluate vast data sets and detect correlations more accurately than humans would use artificial intelligence technologies to help classify large data. It would be used to assess the course learning results based on QF-Emirates (Qualifiers Frame of the United Arab Emirates) criteria with actual data and use it to recommend teaching and learning interventions. We investigate and validate the right neural networks architecture that produces full performance. To that end, a modern algorithm has been improvised. Application to a database to store data and provide data regarding the review of course learning results would be deployed for our suggested recommendation framework. The suggestion method is evaluated and findings are promising as a machine-learning framework. Our neural network based system was able to generate solutions for new cases and provide support in the assessment of courses learning outcomes.