Data Collection for Career Path Prediction Based on Analysing Body of Knowledge of Computer Science Degrees

Measuring and analysing student performance in higher education are considered essential tasks for improving the quality of degree programs and their graduates. This work investigates a new artificial neural network (ANN) approach for career path prediction (CPP) based on the analysing computer science's body of knowledge (BoK) in degree programs. It proposes a proof-of-concept of a data collection strategy to build the required CPP dataset for a promising data-driven system. An initial design, for validating purpose, of a single-layer ANN is introduced, trained, tested and applied to real-world graduate records to classy them into groups or most appropriate career path for each. The results of the applied experiment show the capability of the proposed CPP approach to classify real-world student records into groups.