TITLE:
Predicting Academic Achievement of High-School Students Using Machine Learning
AUTHORS:
Hudson F. Golino, Cristiano Mauro Assis Gomes, Diego Andrade
KEYWORDS:
Machine Learning, Assessment, Prediction, Intelligence, Learning Approaches, Metacognition
JOURNAL NAME:
Psychology,
Vol.5 No.18,
November
25,
2014
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.