engleski

Attitudes Towards Future Employment and Acquired Essential Teaching Skills in Predicting Students' Achievement

It is understandable why educational policies and educational institutions put primary focus on students’ achievement. Bearing in mind that students’ achievement is influenced by many factors, this research explores the idea of predicting students’ academic achievement when students’ attitudes toward their future employment and acquired teaching skills are considered as predictor variables. 229 students of class teacher studies participated in this research. Besides revealing students’ viewpoints regarding these important issues, this research, using neural network approach, aims to increase the knowledge of non-obvious factors that could be useful in estimating students’ achievement, such as students’ attitudes to their employment in labour market, in their profession, after university education. In addition, it aims to test the ability of this data mining method to build an effective classification model with acceptable generalization ability for predicting students’ achievement based on their attitudes. In order to fulfil these aims, statistical relationships between variables were determined and Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural network models were used. MLP neural network model with satisfactory overall classification accuracy on test sample and good generalization ability on validation sample was chosen as the most suitable model and variables with relevant impact on this model’s performance were presented.

future employment, teaching skills, achievement, neural network, students’ attitudes

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