Gender Prediction of the European School’s Teachers Using Machine Learning: Preliminary Results

Abstract

An experiential study is conducted to solve binary classification problem on big dataset of European Survey of Schools: ICT in Education (known as ESSIE) using IBM modeler version 18.1. The survey was conducted by ESSIE at various levels [1]-[3] of schools ISCED (International Standard Classification of Education). To predict the gender of teachers based on their answers, the authors applied 4 supervised machine learning algorithms filtering out of 12 classifiers using auto classifiers on ISCED-1 and ISCED-2 level of schools. Out of total 158 attributes, self-reduction and auto classifier stabilized only 134 attributes for the Bayesian Network (BN) and Random Tree (RT) at level-1 and 134 attributes for logistic regression and 41 attributes for Decision Tree (C5) at level-2. The MissingValue filter of Weka 3.8.1 tool handled well 55641 in ISCED-2 level and 19415 at the ISCED-1 level and normalization is also applied as well. The outcomes of the study reveal that decision tree (C5) classifier outperformed the logistic regression (LR) after feature extraction at ISCED-2 level schools and Random Tree classifier predicted more accurately gender of the teacher as compare to the Bayesian Network at level-1 schools. Further, presented predictive models stabilized 134 attributes with 2926 instances for predict gender of teachers of level-1 schools and 134 attributes with 7542 instances for level-2 schools.