A Prediction Model of Newly Admitted Students in the Level Exam Using Data Mining
DOI:
https://doi.org/10.33977/2106-000-004-003Keywords:
Data Mining, logistic regression, neural networks, level exam grade, K-means, EM Algorithm, Kohonen’s Self-Organizing Map (KSOM) and Clustering.Abstract
In this research, we will use the Data Mining technique as a prediction model to predict the student’s grade in the level exam. At the same time, we are interested in finding the main factors that affect the grade. In order to analyze and predict what will happen during the various stages of the enrollment process at the University, data mining models will be used. This will help the university determine the interventions and measures needed and take the required action accordingly at the right time. To perform the analytics and the predictions, we used Waikato’s Knowledge Analysis Environment (WEKA) tool and different algorithms such as K-Means, logistic regression, Kohonen’s Self Organizing Map (KSOM), as well as EM to identify the most influential factors that predict student’s grade in the level exam. The results of this research showed that EM offers great value to determine the main parameters that affect the student’s final grade in the level exam. The other three algorithms, logistic regression, K-Means, and KSOM are advanced predictive models for the student’s grade in the level exam.
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