J
Jeffrey Bisanz
Researcher at University of Alberta
Publications - 65
Citations - 5250
Jeffrey Bisanz is an academic researcher from University of Alberta. The author has contributed to research in topics: Cognition & Cognitive development. The author has an hindex of 36, co-authored 65 publications receiving 4866 citations.
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Journal ArticleDOI
Pathways to mathematics: longitudinal predictors of performance.
Jo-Anne LeFevre,Lisa Fast,Sheri-Lynn Skwarchuk,Brenda L. Smith-Chant,Jeffrey Bisanz,Deepthi Kamawar,Marcie Penner-Wilger +6 more
TL;DR: A model of the relations among cognitive precursors, early numeracy skill, and mathematical outcomes was tested for 182 children and highlighted the need to understand the fundamental underlying skills that contribute to diverse forms of mathematical competence.
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Home numeracy experiences and children’s math performance in the early school years.
Jo-Anne LeFevre,Sheri-Lynn Skwarchuk,Brenda L. Smith-Chant,Lisa Fast,Deepthi Kamawar,Jeffrey Bisanz +5 more
TL;DR: This paper found that children's numerical competence in kindergarten is highly predictive of their acquisition of mathematics in Grade 1 and Grade 2, suggesting that experiences at home before schooling are important in understanding how numeracy develops.
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Selection of Procedures in Mental Addition: Reassessing the Problem Size Effect in Adults
TL;DR: Ashcraft et al. as mentioned in this paper found that the problem size effect in simple addition is mainly due to participants' selection of non-retrieval procedures on larger problems (i.e., problems with sums greater than 10).
Journal ArticleDOI
Representation and working memory in early arithmetic.
Carmen Rasmussen,Jeffrey Bisanz +1 more
TL;DR: Assessing performance on different components of working memory in conjunction with different types of arithmetic problems provided new insights into the developing relations between working memory and how children do arithmetic.
Journal ArticleDOI
Multiple routes to solution of single-digit multiplication problems.
Jo-Anne LeFevre,Jeffrey Bisanz,Karen E. Daley,Lisa Buffone,Stephanie L. Greenham,Gregory S. Sadesky +5 more
TL;DR: This paper found that participants were slower to retrieve problems that were most likely to be solved by non-retrieval procedures and faster to retrieve problem that were usually solved by retrieval, indicating that direct retrieval models are incomplete accounts of adults' performance and support a continuing influence of learning and experience on the mental representation of simple multiplication problems.