An Efficient Generic Review Framework for Assessment of Learners Ability using KNN Algorithm

Abstract

. Nowadays online review and recommendation system plays major role for maintaining quality of any kind of product with various domain. The customer has to evaluate the product and provides the positive as well as negative reviews based on their interest. The education domain has various stages of the evaluation namely staff related, student related and organization related and so on. Normally the review is restricted to the particular college or university because of their own protocols and system. The proposed framework is to implement the generic review system based on the respective configuration. There are various templates introduced to get the accurate review with good evaluation result. This framework is modeled using the layer of abstraction. User interface layer provides the complete support for the user who uses the system with customized design. Review layer handles all data in different by retrieving and storing the credentials for further review process. Application configuration layer is used to provide the template for assigning credentials and authorization with complete configuration of the system. The final reports are taken for the evaluation process for taking corrective action for further improvement. The classification of the review is carried out in order to achieve high level of accuracy. The main objective of the proposed framework is to classify the student review using KNN (K Nearest Neighbor) algorithm with high efficiency.