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Background: National ranking from the triennial Programme of International Student Assessment (PISA) often serves as a barometer of national performance and human capital. Though excessive student- and school-level covariates (n > 700) may prove intractable for traditional least-squares estimate procedures, shrinkage methods may be more suitable for subset selection.

Aims: With a focus on the United States, this paper proposes sparse regression for PISA 2012 to discover salient student- and school-level predictor variables for mathematical literacy achievement.

Sample: The sparse regression analysis was conducted on 10 top-tiered OECD countries/economies, Canada, and the United States in mathematical literacy on the 2012 PISA. Two- and three-level hierarchical regression analyses were performed on Canadian and US students (N = 26,522) along with five of the ten top-tiered countries/economies (N = 58,385).

Methods: Using the 'least absolute shrinkage and selection operator' (LASSO) technique, the study (1) identified salient predictor variables of mathematical literacy performance for the top-tiered countries/economies, Canada, and the United States and (2) used these salient variables to perform two- and three-level hierarchical regression on data from Canada and the United States along with five top-tiered countries/economies. Weights and replicates were used to account for complex sample design. A weighted, two-level confirmatory factor analysis was performed to identify latent constructs. Missing data were handled through multiple imputation.

Results: Separate two-level hierarchical models accounted for 32-35% student-level and 58-70% school-level variance in Canada and the United States, respectively; three-level models accounted for 33% of level-one variance, 62-65% level-two variance, and 13-44% of level-three variance for the US/Canada and US/Canada/top-tiered students, respectively. Following top-tiered countries/economies, Canadian students had high levels of self-efficacy, were more likely to encounter advanced concepts in class, were less activity/small group-centred, and were more likely to consider truancy a learning hindrance. Factor analyses revealed a positive relation with rigour and class organization (teacher-centred) for top-tiered countries and Canada, though not for the United States. For all countries, there was a strong relation between rigour and self-beliefs.

Conclusion: Compared to top performers, a less rigorous curriculum, coupled with class and school factors, may explain lag in US performance.

Titel:	Significant predictors of mathematical literacy for top-tiered countries/economies, Canada, and the United States on PISA 2012: Case for the sparse regression model.
Autor/in / Beteiligte Person:	Brow, MV
Link:	Full Text Finder (Volltext)
Zeitschrift:	The British journal of educational psychology, Jg. 89 (2019-12-01), Heft 4, S. 726-749
Veröffentlichung:	<2012-> : Chichester : Wiley-Blackwell ; <i>Original Publication</i>: Edinburgh : Scottish Academic Press, 2019
Medientyp:	academicJournal
ISSN:	2044-8279 (electronic)
DOI:	10.1111/bjep.12254
Schlagwort:	Adolescent Canada Child Developed Countries Humans United States Academic Success Education statistics & numerical data Machine Learning Mathematics statistics & numerical data Schools statistics & numerical data Self Efficacy Students statistics & numerical data
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Br J Educ Psychol] 2019 Dec; Vol. 89 (4), pp. 726-749. <i>Date of Electronic Publication: </i>2018 Oct 29. MeSH Terms: Academic Success* ; Machine Learning* ; Self Efficacy* ; Education / statistics & numerical data ; Mathematics / statistics & numerical data ; Schools / statistics & numerical data ; Students / statistics & numerical data ; Adolescent ; Canada ; Child ; Developed Countries ; Humans ; United States References: Austin, E. (2011, 20 May). The United States v. Canada: Why the differing views on immigration. The Economist. Retrieved from http://www.economist.com/blogs/democracyinamerica/2011/05/immigration. ; Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall. ; Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman. ; Bong, M., & Clark, R. E. (1999). 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Contributed Indexing: Keywords: PISA 2012; least absolute shrinkage and selection operator; machine learning; mathematical literacy; sparse regression Entry Date(s): Date Created: 20181031 Date Completed: 20200410 Latest Revision: 20200410 Update Code: 20231215

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Significant predictors of mathematical literacy for top-tiered countries/economies, Canada, and the United States on PISA 2012: Case for the sparse regression model.