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Self-motivation and Academic Performance In Computer Programming Language Using a Hybridised Machine Learning Technique
Isaac Kofi Nti, Juanita Ahia Quarcoo
Pages - 12 - 30     |    Revised - 30-04-2019     |    Published - 01-06-2019
Volume - 8   Issue - 2    |    Publication Date - June 2019  Table of Contents
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KEYWORDS
Self-Confidence, Self-Efficacy, Individual-Interest, Positive-Thinking, Focus, Personal Goals, Motivating Environment, Academic Performance, Random Forest, Super Vector Machine.
ABSTRACT
The advancement in artificial intelligence (AI) and Machine learning (ML) have made it easier to foreknown feature happens from current and past trends. Once Self-efficacy and self-confidence are believed to be, an individual trait associated with academic brilliance. Using a hybridised Random Forest and Support Vector Machine (RFSVM) ML model we predicted students' academic performance in computer programming courses, based on their self-confidence, self-efficacy, positive thinking, focus, big goals, a motivating environment and demographic data. Benchmarking our RFSVM model against Decision Tree (DT) and K-Nearest Neighbour (K-NN) model, the RFSVM recorded and accuracy of 98% as against 95.45% for DT and 36.36% for K-NN. The error between actual values and predicted values of the RFSVM model was better (RMSE = 0.326401, MAE = 0.050909) and compared with the K-NN (RMSE = 2.671397, MAE = 1.954545) and DT models (RMSE = 0.426401, MAE = 0.090909). The results further revealed that students with a high level of self-confidence, self-efficacy and positive thinking performed well in computer programming courses.
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Mr. Isaac Kofi Nti
Department of Computer Science Sunyani Technical University - Ghana
ntious1@gmail.com
Mrs. Juanita Ahia Quarcoo
Department of Computer Science Sunyani Technical University - Ghana


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