A Machine-Learning Approach to Predicting the Achievement of Australian Students Using School Climate; Learner Characteristics; and Economic, Social, and Cultural Status

Authors

• Myint S. Khine, Curtin University
• Yang Liu, Shanghai Maritime University
• Vivek K. Pallipuram, University of the PacificFollow
• Ernest Afari, University of Bahrain

Document Type

Article

Publication Title

Education Sciences

Department

Electrical and Computer Engineering

ISSN

22277102

Volume

14

Issue

12

DOI

10.3390/educsci14121350

First Page

1

Last Page

21

Publication Date

12-1-2024

Abstract

The Programme for International Student Assessment (PISA) is a global survey conducted by the Organisation for Economic Co-operation and Development (OECD) to assess educational systems by evaluating the academic performance of 15-year-old school students in mathematics, science, and reading. In PISA 2022, 13,437 students from Australia participated in the test. While the PISA main questionnaire assesses the subject knowledge, the student background questionnaire solicits contextual information such as school climate, learner characteristics, and socioeconomic status. This study analyses how these contextual variables predict student achievement using the machine-learning models Ridge Linear Regression, K-Nearest Neighbours, Decision Trees, eXtreme Gradient Boosting, and Support Vector Machines, and it reports the evaluation matrices and the most accurate model in predicting student achievement. The analysis shows that contextual variables are associated with student achievement and account for 42% of the variance in achievement. In addition to evaluating multiple machine-learning regressors, Shapley Additive Explanation (SHAP) analysis is conducted to explain the model predictions and evaluate feature importance. Using SHAP analysis, this paper demonstrates how educators and school administrators may effectively interpret the machine-learning results and devise strategies for student success.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Khine, Myint S.; Liu, Yang; Pallipuram, Vivek K.; and Afari, Ernest, "A Machine-Learning Approach to Predicting the Achievement of Australian Students Using School Climate; Learner Characteristics; and Economic, Social, and Cultural Status" (2024). Pacific Faculty Work. 37.
https://scholarlycommons.pacific.edu/all-faculty/37