International Journal For Multidisciplinary Research

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

Author(s) Samuel Odoom, Eric Opoku Osei
Country Ghana
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.
Keywords Enhancing students’ performance, Decision support model, Educational data mining, Optimized Random Forest model, and Machine Learning
Field Computer > Data / Information
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-04
Cite This An Optimal Random Forest Model for Enhancing Decision-Making in Improving Students’ Performance via Educational Data Mining - Samuel Odoom, Eric Opoku Osei - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.11584
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.11584
Short DOI https://doi.org/gtdscr
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