An Optimal Random Forest Model for Enhancing Decision-Making in Improving Students’ Performance via Educational Data Mining

Abstract

The need for measures to enhance students’ performance, especially in Science and Math as a panacea for development in this 4th industrial revolution has become a global call. The large stream and heterogeneous nature of educational data make it difficult to use traditional statistical methods for data analysis to support decisions. However, Educational data mining has been effective and efficient in addressing this issue but much focus of researchers has been on only students' academic records to determine performance. This study sought to propose a Random Forest model to predict and enhance students’ performance. The study adopted a 5-stage mining process to mine psycho-socio-economic demographics educational data with Mutual information, Chi-squared test, Featurewiz, RandomizedSearch CV, and GridSearch CV to optimize the model’s performance. The study’s outcome revealed the key factors affecting students’ performance and that the model was enhanced by a 10.4% increment in precision and f1-score and 9.1% recall value, 7.1secs (62%) improvement in execution time and 78.7% improvement in Root Mean Square Error. This outcome remains a contribution to guiding decision-making in the educational setting and a basis for further studies on model optimization.