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IJSTR >> Volume 10 - Issue 10, October 2021 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



K-Means, Clustering Algorithm For Student’s Selection And Performance Prediction

[Full Text]

 

AUTHOR(S)

Osei Wusu Brempong Jnr

 

KEYWORDS

k-means, clustering, academic performance, prediction, GPA.

 

ABSTRACT

Machine learning, an application of Artificial intelligence uses computer algorithms designed to make decisions and predict outcomes based on analyzing huge data sets.[1] ML enables systems to automatically change and increase in accuracy without being programmed. One major advantage of ML technology in education is student’s selection and prediction of their academic performance. ML beneficial in education is its ability to track learner’s progress and also adjust courses to respond to student's needs which helps in increasing student and teacher engagement [2]. ML feedbacks also put instructors in position to analyze and understand student’s potential and interest, identify struggling students and provide extra support to struggling student’s to overcome learning challenges. GES (Ghana Education Service) has already begun digitalizing the Ghana education system by implementing the computerized school selection and placement system (CSSPS) which is an automated merit base computerized system that uses a deferred acceptance algorithm for assignment[3]. In this system, students are ranked according to their priority levels, they are then proposed as a match to their first choice school in order of their test score ranking. In this paper, we propose a machine learning algorithm K-means clustering to grouping students into ranks of their grades and to analyze their results based on cluster analysis. The student’s evaluation factors like average First and Second semester exams, mid-term quizzes are studied. This analysis will enable teachers and school academic administrators to establish prior knowledge of student's grades and predict their performance

 

REFERENCES

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