Authors : Gabriel Nishimwe; Dr. Wilson MUSONI

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://shorturl.at/OdaLF

Scribd : https://shorturl.at/0Gty9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG189

Abstract : This study addresses the challenges of career guidance in Rwanda's education system using machine learning. A predictive model was developed with a random forest algorithm to forecast student career paths based on annual academic performance. Students can input their desired careers, and if their interest matches one of the top five predicted careers, the system suggests it, displaying the accuracy of each prediction. If their interest is not among the top predictions, the system advises on the most suitable career based on the highest probability. The research aimed to seamlessly integrate this predictive model into an online platform, providing personalized career advice tailored to students' academic achievements. Rigorously, the model's accuracy was evaluated through system-generated outcomes, user feedback, and performance metrics to ensure its effectiveness in guiding students toward suitable careers. By optimizing career guidance and strengthening connections between education and industry in Rwanda, this study seeks to equip students with the necessary tools and support to navigate their career paths successfully. Comprehensive assessment methodologies, including user feedback analysis and performance metrics assessment, illuminate new ways to enhance career guidance. The overarching objective is to instill confidence in students and prepare them to thrive in the ever-evolving professional world. Ultimately, this research aimed to bridge the divide between education and industry, providing students with the insights and support needed to make informed career decisions and succeed in their chosen fields.

Keywords : Career Guidance, Rwanda Education System, Machine Learning, Predictive Model, Random Forest Algorithm, Academic Performance, Education-Industry Connection, Performance Metrics.

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