Student Performance Appraisal System

摘要

In the past few years, various appraisal systems have been analyzed and studied in order to keep track of educational activities. This paper enlightens the evaluation scheme of student's performance by using a machine learning algorithm that helps us to achieve better results when a large number of data is to be handled and managed for the storage of student's data. Filtration is going to be done over the analysis system on the idea of grades or the marks of the students. Our aim is to design a system of records with user-friendly Graphical user interface which can assists student in addition to the evaluation of their performance. Although in recent days various institutions have been come up with their own appraisal systems with machine learning and advanced data analytics techniques, there are some issues regarding data and results of the students of previous years. This paper attempts to introduce such a system, which will not only work for performance appraisal but also tries to avoid network congestion and unnecessary traffic over a wireless network. K-means clustering provides versatility in filtering the student's records based on their grades will be a big factor to handle and recollect the data load from the Cassandra database which acts as a storage house of our current proposed system. Nonetheless, results which are defined as an outcome of this project are more accurate and easier to understand.