Academic Evaluation System Using Adaptive Categorized and Authenticated Recommendation

Teacher performance is an important issue for any academic institution, so these institutions try to develop methodologies and systems to evaluate the performance of its academic staff based on measurable and scientific metrics. These metrics include collecting data from students about classes and how the teacher is performing in the classroom and measuring the learning objectives from students. Major objective of this research work is to develop a web-based multi-user system for academic performance and a recommendation system that depends on the performance and skills of the teacher. The system includes multiple entry points for best factor such as the feedback unit, student attendance unit, teacher classification unit, and teacher recommendation unit, which can be strictly accessed by the administrative authority for academic teachers. The attendance module and the student feedback module are required to achieve better results in the teacher rating module and the teacher recommendation module. A reliability factor has been incorporated to achieve the accuracy of the proposed system. The performance of the system was improved due to the integration of the paperless approach and resulted in less data loss. The developed system was tested for a large university in Kurdistan region, Iraq. For data analysis an adaptive unsupervised learning method was used to categorize teachers according to their performance in the classroom. The system proved very successful in the academic evaluation and recommendation process in the university. This research proved that using student feedback data is the best factor in determining academic performance.