Browsing by Author "Muthui, Dennis M."

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    Predicting campus admission through assessment of soft skills using random forest algorithm
    (KCA University, 2025) Muthui, Dennis M.
    This study develops a machine learning model to predict college admission success in Kenya by assessing soft skills using the random forest algorithm. The research addresses the growing importance of soft skills in academic and professional success, and current limitations in evaluating these abilities during admissions. The study identifies key soft skills, creates a comprehensive assessment tool, develops and tests a random forest model, and evaluates its performance, interpretability, and fairness. The methodology involves a quantitative predictive modeling design, employing stratified random sampling and rigorous data collection procedures. Results indicate that soft skills, particularly communication and problem-solving, are strong predictors of admission success, often outweighing traditional academic metrics. The random forest model achieved 98.36% accuracy in predicting admissions outcomes, with mathematics performance emerging as the most influential factor (22% importance), followed by GPA (18%), KCSE scores (16%), and science grades (15%), while soft skills showed more modest but meaningful contributions (communication 8%, leadership 5%, problem-solving 4%). The model demonstrated consistent performance across demographic groups, with perfect equal opportunity across gender, school type, location, and school level categories. However, the model reflected existing demographic disparities in admission rates that mirror broader equity challenges in educational access. The study concludes that incorporating soft skills assessments in admissions processes could provide a more holistic evaluation of applicants, though current practices continue to prioritize traditional academic achievement. Recommendations include integrating soft skills development in secondary education curricula and incorporating structured soft skills assessments in university admissions processes. This research contributes to the ongoing dialogue about evolving higher education admissions to better align with 21st-century workforce needs while promoting fairness and transparency in the admission process.