Abstract
Performance factors analysis has recently gained popularity as a method for assessing how students' environments affect their academic performance. However, most of the progress has been made in analyzing student behaviour during the learning process. Machine Learning provides many powerful methods that could improve student performance prediction. Our aim is to examine all features of students' environmental life using the machine learning paradigm to assess how students' environment affects their grades. These features are divided into three categories (personality, family, and education) and their impact factors are calculated. To improve predictive accuracy, different models (Random Forest, AdaBoost, Decision Tree, Naive Bayes, and Multi-Layer perceptron) are used to score the features in each group according to their contribution to the solution. Results show that personality features are a minor effect on students' academic performance with 53%. Concerning the educational factors, outcomes offer the average impact was 60%. Regarding family factors, results indicate that students' family life significantly affects academic achievement with 64%.
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