Software for Feedback System Using Adaptive Categorization and Authenticated Recommendation

Abstract

The authors propose a web-based adaptive categorization and authenticated recommendation system, based on teacher performance. Distinct layers of the proposed framework have been operated from many geographically distributed locations. The system contains multiple entry points such as a student attendance module, a teacher categorization module, and a teacher recommendation module, strictly accessed by the administrative authority of an academic organization. The student attendance module is required for achieving better results on the teacher categorization module, and the teacher recommendation module. The reliability factor has been incorporated for realizing the accuracy of the proposed system. The administration authorities communicate with the server to categorize and recommend teachers by using teachers' performance. The replication and re-allocation transparencies have been maintained throughout the servers. Lightweight system performance has been enhanced due to the incorporation of a paperless approach and has provided less data loss. A linear time complexity is achieved due to usage of cellular automata as a tool.