Career-Driven Study Pathways Prediction for Secondary School Students Using Hybridized Machine Learning
DOI:
https://doi.org/10.11113/ijic.v16n1-2.698Keywords:
Study pathway recommendation, Machine learning, Data-driven education, Predictive modeling, Student achievementAbstract
Future study pathway determination is paramount for the secondary students’ further academic development especially in preparing themselves for their careers. Without sufficient information and guidance, students might be exposed to information-limited advice and choices which tend to risk their future undertaking. Addressing this crucial decision-making challenge in choosing the study pathway while considering the students’ career dreams, this paper proposes hybridized machine learning models which are career-driven in guiding the students to make the right study pathway decision. Upon pre-processing the dataset containing essential students’ academic performance-related data as well as their career aspirations, four different hybridized machine learning models are developed to be trained and tested with the data for predicting the students’ study pathways. The machine learning models studied include random forests, decision trees as well as neural networks which are hybridized between each other to benefit the diversifying strengths of each model. The performance metrics such as accuracy and precision have been measured cross-validated to verify the model’s dependability. The obtained results demonstrated that the proposed career-driven hybridized models perform better than the typical existing machine learning models, which echo into better decisions offered by the proposed hybridized models in advising students’ future study pathway more effectively.
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