Technical and Vocational Education Analytics Using Punjab TEVTA Students’ Data

Abstract

The development of informative workforce that is skilled in a specific profession is considered as the most recommended and desirable feature of any advanced state. Technical Education & Vocational Trainings provide golden opportunity of growth regarding the output of individuals and prosperity of employers. Subsequently it is the dire need of developing countries to invest in public vocational education and training sector (VET) for the progression of skillful societies. Process of manual predictions and analysis on the basis of students’ data to make decisions that will improve the overall teaching and learning is very difficult and tiring. Data mining is exceptionally helpful when we are talking about education data analysis and prediction. Data mining techniques are being used successfully in different areas especially in student educational and learning analytics called as Educational Data Mining (EDM). In this work, TEVTA students’ data is shaped as a ready-to-mine data set and then various data mining techniques are applied to derive interesting patterns that can potentially derive important decisions for improvement of learning process, enhancement of teaching method and overall development of whole system of technical education and vocational trainings. Besides presenting interesting analytics of TEVTA data, we develop classification problems to predict status of students after completing TEVTA courses. This classification can also help in evaluating success of TEVTA programs. This work can help in analyzing and predicting the aspects affecting students’ as well as institutes’ performance from different dimensions.