Data Analytics: Adaptive Network-based Fuzzy Inference System for prediction of computer science graduates’ employability

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The British University in Dubai (BUiD)
The increased amount of data generated in the world of today in all fields is considered to be an indicator for future predictions. In recent decades, in any field and as a result of developments in information technology, a huge amount of data has been provided from the educational field, by which students’ Employability Prediction has become a main concern for higher education institutions. The question of employability has become a critical consideration not only for graduates but for the educational institutions themselves. This research study compares a number of classifiers to determine the effective classifier that accurately and efficiently categorizes CS and IT graduates into employed, unemployed, or other, and predict the future employability of CS and IT students in Jordan. For this purpose, an Adaptive Network Fuzzy Inference System (ANFIS) is applied in this research study. The data of 1095 CS and IT graduates was obtained from three universities in Jordan. This data was collected through a set of tracer studies that were carried out by these universities. ANFIS, Decision Tree, SVM, MLP, and Naïve Bayes classifiers were applied in order to find the classifier with the highest accuracy and efficiency. The final outcomes showed that ANFIS has the highest accuracy, with a percentage of 94% accuracy for its predictions. A set of recommendations is presented by the researcher according to the most effective factors that influence the CS and IT employment market in the Middle East. The researcher suggests for the ministries of higher education to focus on developing the CS and IT students’ programming skills and communication skills, which emerged as essential for increasing CS and IT students’ employment prospects. affecting the employment market for CS and IT.
Fuzzy systems., educational data mining, data analytics, ANFIS, classification, employability