Abstract
This study uses a machine learning technique, a boosted tree model, to relate the student cognitive achievement in the 2018 data from the Programme of International Student Assessment (PISA) to other features related to the student learning process, capturing the complex and nonlinear relationships in the data. The SHapley Additive exPlanations (SHAP) approach is subsequently used to explain the complexity of the model. It reveals the relative importance of each of the features in predicting cognitive achievement. We find that instruction time comes out as an important predictor, but with a nonlinear relationship between its value and the contribution to the prediction. We find that a large weekly learning time of more than 35 hours is associated with less positive or even negative effect on the predicted outcome. We discuss how this method can possibly be used to signal problems in the student population related to learning time or other features.
Highlights
K12 education systems play a key role in empowering young citizens with the necessary knowledge, skills, mindsets and competencies ready for 21st century human capital workforce demand
Using ML techniques and the Programme of International Student Assessment (PISA) 2018 data, we studied the importance of 80 selected features in relation to predicting the score of students on the PISA cognitive test by measuring the feature contribution with SHapley Additive exPlanations (SHAP) values
We found that the average effect of the features on the model prediction decreases quite slowly from the most important to the least important feature
Summary
K12 education systems play a key role in empowering young citizens with the necessary knowledge, skills, mindsets and competencies ready for 21st century human capital workforce demand. The 2018 PISA assesses student cognitive learning outcomes in reading, math and science, and surveys factors relevant for academic learning from aspects of 1) non-cognitive and metacognitive constructs, 2) student background (i.e. socio-economic status, educational pathways in early childhood, etc.), 3) teaching and learning processes (i.e. teaching practices and classroom support, out-of-school experience, etc.), 4) school policies and governance (i.e. school climate, parental involvement, assessments, etc.) [1] These surveys, by design, are to investigate the “why” behind what the cognitive achievement outcomes say, and are designed to allow educators and policy makers to make evidence-based decisions
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