Adaptive Network Based Fuzzy Inference System and the Future of Employability

Saada Khadragy ,Sherief Abdallah

doi:10.7176/ncs/12-04

Abstract

Educational data is considered by researchers and data scientists as an indicator for the future predictions. The current research study aims for classifying IT alumni students into employed and unemployed. The data collected from two universities in Jordan. 781 of IT alumni students in two universities in Jordan participate in the current study. Three classifiers are compared to determine the most suitable one for predicting the future of IT students’ employability. The results show that Adaptive Network Based Fuzzy Inference System came as a suitable classifier for predicting IT students’ employment in Jordan. As gender, programming skills, and communication skills came as the most effective factors affecting IT recruitment field, a set of recommendations is presented to the ministry of higher education based on the significant factors affecting IT graduates employment. Keywords: employability, ANFIS, classification, data mining DOI: 10.7176/NCS/12-04 Publication date: January 31 st 2021

Highlights

These tools are defined as educational data mining (EDM) and learning analytics (LA)
Precision = True positives (TP) / (TP + False positives (FP)) F-measure: We compute an F-measure which applies Harmonic Mean in place of Arithmetic Mean as it disciplines the extreme values more
In this research study the 10-fold cross validation process is implemented to evaluate the accuracy of the model that was built using the neuro-fuzzy inference system (ANFIS)

Summary

Introduction

For all of the above, in this research study the concept of employability is highlighted for Computer Science, Computer Information System, and Computer Engineering graduates and the most effective factors affecting their abilities and skills to get a job. The implementation of computer networks is an integral characteristic of online learning, the face to face schools and universities are using extensive networkconnected electronic tools such as mobile phones, tablets, and computers, which directly and indirectly affect graduates’ employability in all fields and majors. To predict an appropriate model for the newly registered students in both CS and IT in Jordanian universities This model would help in classifying those students between will be employed, will not be employed, and undetermined situation

Objectives

Methods

Results