Cross-Lagged Models of Mathematics Achievement and Motivational Factors Among Hispanic and Non-Hispanic High School Students

Saw, Guan; Chang, Che‐Wei

doi:10.1177/0739986318766511

Cited by 10 publications

(4 citation statements)

References 24 publications

(42 reference statements)

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“…Beyond the typical student factors such as gender, cognitive abilities, and metacognitive (Desoete and De Craene 2019;Lindberg et al 2010), research has confirmed the importance of a range of non-cognitive social psychological factors predictors of student academic success (Lindberg et al 2010;Kim and Choi 2021). In mathematics achievement, these factors include motivation (Levpušček et al 2013;Saw and Chang 2018), goal orientations (Dela Rosa and Bernardo 2013), attitudes (Gjicali and Lipnevich 2021), self-beliefs (Damrongpanit 2019;Szumski and Karwowski 2019) and academic emotions Bernardo 2013, 2016). There are more specific student factors that relate to these social psychological factors such as the students' educational and career aspirations; students who have higher career aspirations that also require higher educational qualifications showing stronger motivations related to achieving in mathematics (Watt et al 2019;Webster and Fisher 2000).…”

Section: Predictors Of Mathematics Learning and Achievementmentioning

confidence: 99%

Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning

et al. 2022

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Filipino students performed poorly in the 2018 Programme for International Student Assessment (PISA) mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The ten variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. However, there were other distinct variables that relate to students’ motivations, family and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools.

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Section: Predictors Of Mathematics Learning and Achievementmentioning

confidence: 99%

Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning

et al. 2022

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“…All eight of the articles described educational settings in the United States. Four of the articles used qualitative methods (Collins & Jones Roberson, 2020; McGee, 2016; Renbarger & Long, 2019; Rinn & Plucker, 2019), two used quantitative methods (Saw & Chang, 2018; Workman, 2020), and two used mixed methods (O’Brennan et al, 2019; Steenbergen-Hu, Olszewski-Kubilius, & Calvert, 2020). A theme that was identified among the psychosocial intervention articles was that psychosocial skills (e.g., self-beliefs, motivation) can support student academic achievement, access to postsecondary talent development, and persistence within advanced learning opportunities.…”

Section: Resultsmentioning

confidence: 99%

Reducing Excellence Gaps: A Systematic Review of Research on Equity in Advanced Education

Meyer

Shen

Plucker

2023

Review of Educational Research

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Unequal access to advanced learning opportunities is among the most complex and controversial issues in American K–12 schools. Interventions that address policy, programming, and instruction can provide opportunities for students with advanced learning needs in school systems that prioritize minimum grade-level standards. Excellence gaps are differences in advanced performance among student subgroups that result from inequities in education and society. In this systematic review of the literature, the authors identified 80 empirical research studies on strategies for reducing excellence gaps published between 2010 and 2021 and identified themes related to the seven facets of the Excellence Gap Intervention Model (K–12 school accountability support, teacher professional learning, expanded advanced learning opportunities, universal screening with local norms, frontloading, flexible ability grouping, psychosocial interventions). This analysis revealed substantial evidence of intervention development over the past decade and suggests a revised approach to equitable, advanced education that begins with preparation (e.g., teacher professional learning, student frontloading) and is followed by placement, evaluation, and adjustment as students’ learning needs change.

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“…Most studies used only single-item survey measures to assess the impact of STEM summer programs on student outcomes, which have low level of measurement reliability and validity. Moreover, student samples in prior studies were not sociodemographically diverse, and therefore, had limited generalizability and offered no estimates for different sociodemographic subgroups, which could be informative evidence for understanding the changes in STEM motivations among underrepresented groups [23,24]. Improving on prior work, this study aimed to assess the associations between OST STEM program participation and students attitudes toward and career interests in STEM by analyzing multiple years of data from a STEM summer program based in San Antonio, Texas, USA, with a sizable and diverse student sample and a rich set of measures on students' attitudes toward and career interests in math and science.…”

Section: Literature Reviewmentioning

confidence: 99%

Out-of-school time STEM program: Students’ attitudes toward and career interests in mathematics and science

Saw

Swagerty

Brewington

et al. 2019

IJERE

Self Cite

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<span>Internationally, out-of-school time (OST) science, technology, engineering, and mathematics (STEM) programs abound. However, rigorous evidence of their impacts on student outcomes is scarce. This study evaluated the relationships between OST STEM program participation and student motivational factors in math and science by analyzing survey and administrative data of 1.017 middle school students who participated in the seven-week, STEM-focused Prefreshman Engineering Program (PREP) in San Antonio, Texas, from 2015 to 2017. Multiple regression results indicated that the PREP participation was positively associated with students’ attitudes toward math and interests in math-related careers, whereas the effects on students’ attitudes toward science and career interests in science were negligible. No evidence was found to suggest that the associations between PREP participation and student motivational factors in math and science differed by gender, race/ethnicity, or socioeconomic status.</span>

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Cross-Lagged Models of Mathematics Achievement and Motivational Factors Among Hispanic and Non-Hispanic High School Students

Cited by 10 publications

References 24 publications

Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning

Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning

Reducing Excellence Gaps: A Systematic Review of Research on Equity in Advanced Education

Out-of-school time STEM program: Students’ attitudes toward and career interests in mathematics and science

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