BAYESIAN NETWORK MODELING OF ENGINEERING-STUDENT PERCEPTIONS OF SUCCESS IN TERMS OF VOCATION

Abstract

Background: The influence of vocation on success can be studied through various surveys, but a model is needed that will validate the results of such surveys with a high probability of accurately representing the reality under study. Bayesian networks meet this requirement Purpose: Students who enroll on an engineering degree may have to choose engineering disciplines that differ from their initial choice. In some cases, these differences can undermine their vocational commitment and leave them with the feeling that their final choice was mistaken. This study is intended to gauge engineering student perceptions of success in terms of vocation and to remedy weaknesses that may discernible in their attitudes. Method: Bayesian networks, by choosing a variable, proper graphs, probabilistic calculation and survey validations with the ROC curve (Receiver Operating Characteristic) can identify variable-dependent survey results and filter them accordingly. Results: Issues relating to professional expectations and learning have more influence on students whether they are or are not in the specific field of engineering that reflected their vocation. Success rates increase considerably when students perceive variables such as "find useful work", "find a job easily" or "enjoy learning" in a positive light. Conclusions: What helps students to feel that they are not wrong is to think that they will find employment and that their jobs will make them feel useful. It is therefore essential to talk about professional life from the very first year through to graduation and students should do so believing fully in their choice of degree.