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The Influence of Clarity on Career Choice and Academic Success in First Year

In this study, we use various machine learning algorithms to understand the link between the grades that students achieve and their choice of career path within a first year biological sciences degree. This research will shed light on the students’ academic success in relation to their motivation to pursue a career within the field which they are studying. Student success is classified into two classes, pass or fail, using the student career choices, their interest factor that leads to the career choice, the learning style they use in university, and their results in first year. We used six machine learning models to classify the students under two classes (Fail or Pass). The Support Vector Machine model had an accuracy of 74% which was the best model compared to the other 6 models, it had 10-fold crossvalidation. The Simple Logistics model achieved the second-best accuracy of 73% also with 10-fold cross-validation.