(untitled) (International Journal of Engineering and Computer Science, 2016)
Measuring trainee’s performance in technical/vocational trades involves some peculiar considerations. In this study a survey research method was adopted to generate relevant predictor variables. Our primary data was collected using a simple survey instrument on the regular and sandwich students; the rest was from the Examination Unit of the University Registrars Department and also from various assessment records and instructors’ competency test records in the students department. The secondary data set included three year assessment records 2013 to 2015 in a course, VTE 201: Students Industrial Work Experience of the faculty of Vocational and Technical Education, University of Nigeria Nsukka. The raw data was preprocessed and converted to a required format. A total of 187 student records were obtained. This was used to train four selected classification algorithms whose accuracies were compared. Results showed that Decision Tree Algorithms performed best in predicting student’s terminal performance in four categories of technical trades