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Guglielmi, R. Sergio; Brekke, Nancy – Journal of Educational Psychology, 2018
Math proficiency is a strong predictor of postsecondary attainment, yet cross-national comparative assessments show that U.S. students consistently score below many of their peers, especially those in East Asia. Similar math achievement disparities exist across ethnic/racial groups within the U.S., where Asian American students routinely…
Descriptors: Mathematics Achievement, Educational Attainment, Postsecondary Education, Predictor Variables
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Cheema, Jehanzeb R.; Sheridan, Kimberly – Issues in Educational Research, 2015
This study investigated the effect of time spent on homework and mathematics anxiety on mathematics achievement. Data from a nationally representative US sample consisting of 4,978 cases was used to predict mathematics achievement from time spent on homework and mathematics anxiety while controlling for demographic differences such as gender,…
Descriptors: Homework, Mathematics Anxiety, Mathematics Achievement, Predictor Variables
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Cheema, Jehanzeb R.; Galluzzo, Gary – Research in Education, 2013
The US portion of the Program for International Student Assessment (PISA) 2003 student questionnaire comprising of 4,733 observations was used in a multiple regression framework to predict math achievement from demographic variables, such as gender, race, and socioeconomic status, and two student-specific measures of perception, math anxiety and…
Descriptors: Mathematics Achievement, Gender Differences, Predictor Variables, Mathematics Anxiety
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Cheema, Jehanzeb; Kitsantas, Anastasia – Educational Psychology, 2016
This study investigated the predictiveness of preferred learning styles (competitive and cooperative) and classroom climate (teacher support and disciplinary climate) on learning strategy use in mathematics. The student survey part of the Programme for International Student Assessment 2003 comprising of 4633 US observations was used in a weighted…
Descriptors: High School Students, Learning Strategies, Prediction, Preferences
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Cheema, Jehanzeb R.; Kitsantas, Anastasia – International Journal of Science and Mathematics Education, 2014
The present study investigated the role of disciplinary climate in the classroom and student math self-efficacy on math achievement. The student part of the Program for International Student Assessment (PISA) 2003 survey containing 4,199 U.S. observations was employed in a weighted least squares nested multiple regression framework to predict math…
Descriptors: Mathematics Instruction, Mathematics Achievement, Least Squares Statistics, Multiple Regression Analysis
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Cooper, Kristy S. – American Educational Research Journal, 2014
This case study analyzes how and why student engagement differs across 581 classes in one diverse high school. Factor analyses of surveys with 1,132 students suggest three types of engaging teaching practices--connective instruction, academic rigor, and lively teaching. Multilevel regression analyses reveal that connective instruction predicts…
Descriptors: Teaching Methods, High School Students, Learner Engagement, Regression (Statistics)
Catapano, Michael – ProQuest LLC, 2013
Strong mathematical abilities are important for the continuation of a successful society. Mathematics is required and involved in all aspects of daily life: banking, communications, business, education, and travel are just a few examples. More specifically the areas of finance, engineering, architecture, and technology require individuals with…
Descriptors: High School Students, Grade 10, Secondary School Mathematics, Mathematics Skills
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Cheema, Jehanzeb R.; Zhang, Bo – International Journal of Education and Development using Information and Communication Technology, 2013
This study looked at the effect of both quantity and quality of computer use on achievement. The Program for International Student Assessment (PISA) 2003 student survey comprising of 4,356 students (boys, n = 2,129; girls, n = 2,227) was used to predict academic achievement from quantity and quality of computer use while controlling for…
Descriptors: Academic Achievement, Computer Use, Educational Quality, Incidence