Structural model of metacognition and knowledge of geometry

Aydın, Utkun; Ubuz, Behiye

doi:10.1016/j.lindif.2010.06.002

Cited by 22 publications

(15 citation statements)

References 59 publications

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“…One line of research identified significant cognitive and noncognitive contributors to explain students’ individual differences in geometry abilities. Identified cognitive factors include spatial abilities (Clements et al, 1997; Geary, 1996; Jeung et al, 1997; Kyttälä & Lehto, 2008; Purcell & Gero, 1998; Spelke et al, 2010; Verstijnen et al, 1998), fluid intelligence and reasoning skills (Battista, 1990; Dawkins, 2015; Giofrè et al, 2014), working memory (Giofrè et al, 2013, 2014), geometry content knowledge (Bokosmaty et al, 2015; Lawson & Chinnappan, 1994), and meta-cognition (Aydın & Ubuz, 2010). In addition, motivation, persistence, and student emotions (e.g., boredom and enjoyment) are frequently cited to interpret students’ geometry learning difficulties and individual differences (Bailey et al, 2014; Super & Bachrach, 1957).…”

Section: Existing Research On Geometry Educationmentioning

confidence: 99%

Teaching Geometry to Students With Learning Disabilities: Introduction to the Special Series

Zhang

2020

Learning Disability Quarterly

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This article introduces and contextualizes the four articles that constitute the thematic special series on geometry instruction for students with learning disabilities or difficulties. The four articles, each emphasizing one important aspect of geometry learning and instruction for students with learning difficulties or disabilities, are aimed to answer critical questions raised by special education/math education researchers and practitioners on how to teach geometry to students with learning disabilities or difficulties.

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Section: Existing Research On Geometry Educationmentioning

confidence: 99%

Teaching Geometry to Students With Learning Disabilities: Introduction to the Special Series

Zhang

2020

Learning Disability Quarterly

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“…In particular, because geometry includes considerable proof-oriented problems, it is typically considered highly related to the deductive thinking (Dawkins, 2015) and verbal logical reasoning (Battista, 1990). Geometry learning difficulties can also be related to non-cognitive factors, including motivation and persistence (Nichols, 1996), emotions (Bailey et al, 2014), meta-cognition abilities (Aydın & Ubuz, 2010), knowledge (Bokosmaty et al, 2015), and how they use knowledge (Lawson & Chinnappan, 1994). And these non-cognitive problems universally exist in all mathematical domains for struggling students, which also challenges the hypothesis that geometry difficulties should be viewed as a specific and unique subtype of math learning difficulty.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Students With Specific Difficulties in Geometry: Exploring the TIMSS 2011 Data With Plausible Values and Latent Profile Analysis

Chen

Zhang

2020

Learning Disability Quarterly

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This study analyzed the Trends in International Mathematics and Science Study 2011 data on fourth-grade U.S. students’ mathematics performance to answer four research questions: (a) How did U.S. students’ geometry performance compare with their performance on the other Mathematics subscales? (b) What were the patterns of student achievement among the Mathematics subscales? (c) Was there a group of students who demonstrated specific difficulties in geometry only? and (d) Which demographic variables contributed to students’ classification in the group with geometry difficulties? We found that (a) U.S. students’ performance was poorer on the Geometry subscale than on other Mathematics subscales; (b) using latent profile modeling, we identified a group of students with the lowest scores across all three Mathematics subscales who showed a significant discrepancy between geometry and the other subscales that did not exist within the high-achieving and average-achieving groups; and (c) gender, age, home language, race, and preference for mathematics and science significantly influenced the probability of being classified in the group with the lowest performance and the largest gap between Geometry and other Mathematics subscales. Implications for educational theory and practice are discussed.

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“…Despite the equivocal structural validity evidence for JMAI scores (Sperling et al, 2002), the factors are frequently used in research studies. Researchers have used the JMAI to examine the relationships between students’ metacognition and other variables, including geometric knowledge (Aydin & Ubuz, 2010), memory in hypermedia environments (Schwartz, Andersen, Hong, Howard, & McGee, 2004), hypothesis development (Kim & Pedersen, 2010), perceptions of science learning environments (Yilmaz-Tüzün & Topçu, 2010), and science achievement, epistemological beliefs, socioeconomic status, and gender (Topçu & Yilmaz-Tüzün, 2009). Researchers have also used the JMAI to assess the impact of computer gaming programs (Ke, 2008a, 2008b) and tutoring programs (Vandevelde, Van Keer, & De Wever, 2011) on students’ metacognition.…”

Section: Metacognition Assessment Measuresmentioning

confidence: 99%

Comparing Metacognition Assessments of Mathematics in Academically Talented Students

Young

Worrell

2018

Gifted Child Quarterly

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Metacognition is a psychological construct that refers to people's knowledge and regulation of their thinking, learning, and problem-solving processes (Brown, 1987; Flavell, 1979). Over the past 35 years, there has been a growing recognition of the importance of metacognition in mathematics learning and performance (e.g., De Corte, Greer, & Verschaffel, 1996; Jacobse & Harskamp, 2012). Indeed, metacognition has been found to have significant effects on mathematics performance both at the elementary and secondary levels (Dignath & Buttner, 2008), and the National Research Council identified metacognition as one of three components central to learning and teaching (Bransford, Brown, & Cocking, 2000). Speaking to the continued importance of metacognition, researchers continue to examine metacognition with regard to mathematics performance (e.g., Jacobse & Harskamp, 2012) and gifted populations (e.g., Hannah & Shore, 2008; Snyder, Nietfeld, & Linnenbrink-Garcia, 2011). Instruments that provide reliable information from which valid inferences about students' metacognitive abilities can be drawn are essential for researchers who study the impact of metacognition on academic achievement and for educators who are responsible for developing and assessing students' metacognitive abilities. When examining the relationships between metacognition and mathematics performance, metacognition is measured in two different ways. In the psychology literature, researchers predominantly use quantitative scales such as self-report questionnaires to measure metacognition (

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Structural model of metacognition and knowledge of geometry

Cited by 22 publications

References 59 publications

Teaching Geometry to Students With Learning Disabilities: Introduction to the Special Series

Teaching Geometry to Students With Learning Disabilities: Introduction to the Special Series

Students With Specific Difficulties in Geometry: Exploring the TIMSS 2011 Data With Plausible Values and Latent Profile Analysis

Comparing Metacognition Assessments of Mathematics in Academically Talented Students

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