Brian Greer
Portland State University
42 Papers
207 Citations
Brian Greer is an academic researcher from Portland State University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 14, co-authored 28 publications.
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Papers
Culturally Responsive Mathematics Education
Brian Greer,Swapna Mukhopadhyay,Arthur B. Powell,Sharon Nelson-Barber +3 more
- 23 Mar 2009
TL;DR: In this paper, D'Ambrosio et al. present a perspective on the development of mathematics education in the United States from an anthropomorphic perspective, and discuss the importance of cultural diversity in mathematics education.
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Modelling for Life: Mathematics and Children’s Experience
Brian Greer,Lieven Verschaffel,Swapna Mukhopadhyay +2 more
- 01 Jan 2007
TL;DR: The authors make the case for introducing fundamental ideas about modelling early, in particular through reconceptualizing word problems that describe real-world situations as exercises in modelling, and argue for modelling as a means of giving children a sense of agency through recognizing the potential of mathematics as a critical tool for analysis of issues important in their lives.
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Reconceptualising Word Problems as Exercises in Mathematical Modelling
Lieven Verschaffel,Wim Van Dooren,Brian Greer,Swapna Mukhopadhyay +3 more
- 05 Feb 2010
TL;DR: A review and discussion of research on the phenomenon of “suspension of sense-making” when doing school arithmetic word problems, including a summary of earlier work culminating in the book by Verschaffel et al. (2000) and with special attention to the more recent empirical work.
84
Connecting mathematics problem solving to the real world
Erik De Corte,Lieven Verschaffel,Brian Greer +2 more
- 01 Jan 2000
TL;DR: In this article, Verschaffel, Greer, and De Corte present a series of recent studies that provide robust empirical evidence showing the omnipresence and the strenght of disconnecting word problem solving from the real world.
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Statistical Thinking and Learning
TL;DR: In this paper, statistical thinking and learning is used to train a classifier for statistical reasoning and classification problems with respect to a set of properties of a given set of variables, i.e.,
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