{"title":"Examining the contextual factors of science effectiveness: a machine learning-based approach","authors":"Jie Hu, Yi Peng, H. Ma","doi":"10.1080/09243453.2021.1929346","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":47698,"journal":{"name":"School Effectiveness and School Improvement","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"School Effectiveness and School Improvement","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/09243453.2021.1929346","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 3
Abstract
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.
期刊介绍：
School Effectiveness and School Improvement presents information on educational effectiveness, practice and policy-making across primary, secondary and higher education. The Editors believe that the educational progress of all students, regardless of family background and economic status, is the key indicator of effectiveness and improvement in schools. The journal strives to explore this idea with manuscripts that cover a range of subjects within the area of educational effectiveness at the classroom, school or system level, including, but not limited to: •Effective pedagogy •Classroom climate •School ethos and leadership •School improvement and reform programmes •Systemwide policy and reform
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