Examining the contextual factors of science effectiveness: a machine learning-based approach

ABSTRACT

This research intended to identify key contextual factors that synergistically influence high- and low-performing students’ science outcomes by drawing upon a dynamic model of educational effectiveness. The dataset, the Programme for International Student Assessment (PISA) 2015, consisted of 79,963 science scores for secondary students (49,924 high performers at proficiency Level 6 and 30,039 low performers at proficiency Levels 1a and 1b) from 53 countries/economies along with students’ and school principals’ responses to the PISA questionnaires. By applying a support vector machine (SVM) and SVM-recursive feature elimination (SVM-RFE) sequentially, this study successfully (a) identified 30 key factors of the total 127 contextual factors at the school, classroom, and student levels that synergistically differentiate high and low achievers and (b) provided evidence to support the validity of the dynamic model of educational effectiveness by recognizing the multidimensionality of the contextual factors.

KEYWORDS:

• Dynamic model of educational effectiveness
• machine learning
• support vector machine
• high and low performers
• PISA 2015

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was supported by the Philosophical and Social Sciences Planning Project of Zhejiang Province in 2020 [grant number 20NDJC01Z], the Humanities and Social Sciences Fund of the Ministry of Education in China [grant number 21YJC740038], the Fundamental Research Funds for the Central Universities and the Teaching Reform Research, and the Fundamental Research Funds for Central Universities (Interdisciplinary Research 2020–2022).

Notes on contributors

Jie Hu

Jie Hu, PhD, is a professor at the Department of Linguistics, School of International Studies, Zhejiang University, China. She has focused on secondary education for more than 10 years after her PhD graduation from the University of Warwick, UK. Her research interests include ICT-based education, educational data mining, and learning analysis.

Yi Peng

Yi Peng is a Master’s student majoring in educational studies at the Department of Linguistics, School of International Studies, Zhejiang University, China. Her research interests lie in science education, computer-assisted science learning, and educational data mining.

Hong Ma

Hong Ma is currently an assistant professor at the Department of Linguistics, School of International Studies, Zhejiang University, China. Her primary research interests are computer-assisted language learning, educational assessment, and educational data mining.