Random Forest Analysis of Factors Predicting Science Achievement Groups: Focusing on Science Activities and Learning in School

Abstract

This study explored science-related variables that have an impact on the prediction of science achievement groups by applying the educational data mining (EDM) method of the random forest analysis to extract factors associated with students categorized in three different achievement groups (high, moderate, and low) in the Korean data from the 2015 Programme for International Student Assessment (PISA). The 57 variables of science activities and learning in school collected from PISA questionnaires for students and parents were analyzed. Variables related to students’ past science activities, science teaching and learning methods, and environmental awareness were found to played important roles in predicting science achievement. When checking partial dependence plots for major variables, science activities and instructional strategies had a high probability of changing the prediction of an achievement group. This study focused on science-related contextual variables that can be improved through government policies and science teachers’ efforts in the classroom.

Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades

“…In recent years, machine learning methods have gained traction for analyzing extensive educational assessment data, estimating the influence of individual factors, and constructing predictive models [40]. Among these techniques, the RF approach has been proposed to assess the significance of individual factors in predicting outcomes [18,19,21].…”

“…Within the present study, 20% of the samples were designated for validation data; tree numbers were fixed to 500, 1000, and 2000 trees; each tree drew on 50% of the training data. Two variables (the square root of the total number of variables, i.e., √ 5 ≈ 2) were randomly selected for node splitting in each decision tree generated from bootstrapped datasets [40,53]. Therefore, n(Train) = 17,609; n(Test) = 4402.…”