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Modeling Student Motivation and Students’ Ability Estimates From a Large-Scale Assessment of Mathematics

Bibliographic Data

ID16913079
AuthorsCarlos Zerpa, Carlos Zerpa-García (0000-0002-7150-384X, Lakehead University, corresponding author), Krystal K Hachey (University of Ottawa), Krystal Hachey, Christina Van Barneveld (Lakehead University), Marielle Simon (0000-0002-3936-8035, University of Ottawa)
Year2011
Volume1
Issue2
Publication date2011-07-01
Open AccessYes
TypeARTICLE
VenueSAGE Open (JOURNAL)
Journal identifiersISSN: 2158-2440 • E-ISSN: 2158-2440
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/2158244011421803
OpenAlexW2139901893
LanguageEN
References cited43

When large-scale assessments (LSA) do not hold personal stakes for students, students may not put forth their best effort. Low-effort examinee behaviors (e.g., guessing, omitting items) result in an underestimate of examinee abilities, which is a concern when using results of LSA to inform educational policy and planning. The purpose of this study was to explore the relationship between examinee motivation as defined by expectancy-value theory, student effort, and examinee mathematics abilities. A principal components analysis was used to examine the data from Grade 9 students ( n = 43,562) who responded to a self-report questionnaire on their attitudes and practices related to mathematics. The results suggested a two-component model where the components were interpreted as task-values in mathematics and student effort. Next, a hierarchical linear model was implemented to examine the relationship between examinee component scores and their estimated ability on a LSA. The results of this study provide evidence that motivation, as defined by the expectancy-value theory and student effort, partially explains student ability estimates and may have implications in the information that get transferred to testing organizations, school boards, and teachers while assessing students’ Grade 9 mathematics learning

Expectancy theory · Mathematics education · Multilevel model · Statistics · Education, Achievement, and Giftedness · Innovative Teaching and Learning Methods · Mathematics · Psychology · Social Psychology · Student Assessment and Feedback

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Highly citedNo

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