Predicting Academic Achievement of High-School Students Using Machine Learning

Abstract

The present paper presents a relatively new non-linear method to predict academic achievement of high school students, integrating the fields of psychometrics and machine learning. A sample composed by 135 high-school students (10th grade, 50.34% boys), aged between 14 and 19 years old (M = 15.44, DP = 1.09), answered to three psychological instruments: the Inductive Reasoning Developmental Test (TDRI), the Metacognitive Control Test (TCM) and the Brazilian Learning Approaches Scale (BLAS-Deep Approach). The first two tests have a self-appraisal scale attached, so we have five independent variables. The students’ responses to each test/scale were analyzed using the Rasch model. A subset of the original sample was created in order to separate the students in two balanced classes, high achievement (n = 41) and low achievement (n = 47), using grades from nine school subjects. In order to predict the class membership a machine learning non-linear model named Random Forest was used. The subset with the two classes was randomly split into two sets (training and testing) for cross validation. The result of the Random Forest showed a general accuracy of 75%, a specificity of 73.69% and a sensitivity of 68% in the training set. In the testing set, the general accuracy was 68.18%, with a specificity of 63.63% and with a sensitivity of 72.72%. The most important variable in the prediction was the TDRI. Finally, implications of the present study to the field of educational psychology were discussed.