Formatively assessing novices’ capabilities with modeling content

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Why classroom equity tools aren't always equal

First author draf

Comparative Analysis of Zoning of Food Retail and Urban Agriculture for Richmond, Virginia

This research partnership between local public health practitioners and urban food systems scholars suggests improvements to City of Richmond, Virginia’s zoning code related to food retail and urban agriculture by drawing inspiration from other American central cities. The authors created an empirical process to identify potential sister cities to Richmond as a source for high quality comparative examples. Next, the authors then engaged in a non-empirical, purposive process of identifying potential zoning code improvements from both identified sister cities, as well as other communities. Time and capacity constraints dictated the non-empirical nature of this search. Recommendations for improvement to Richmond’s zoning code are included. Local government officials and potential urban food entrepreneurs of jurisdictions with comparable characteristics to the City of Richmond could benefit from this analysis

A web-based normative calculator for the uniform data set (UDS) neuropsychological test battery

Introduction: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the neuropsychological tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets. Methods: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all neuropsychological tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model. Results: For each neuropsychological test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output. Conclusions: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease

Exploring the impact of discussion-leading professional development on teachers’ practice

SUBK00016187 - University of MichiganPublished versio

Identify fractions and decimals on a number line

To examine students' understanding of this core mathematics content, analyze their erroneous strategies.</jats:p

Connecting mathematical knowledge with engagement in mathematics teaching practices

International audienceResearch suggests that mathematical knowledge is likely to influence how mathematics is taught. In turn, how mathematics is taught impacts students’ opportunities to learn mathematics. We report on a study examining the connection between preservice teachers’ mathematical knowledge and the nature of their eliciting and interpreting of student thinking. Our findings suggest that preservice teachers elicit and interpret student thinking with more emphasis on student understanding in situations in which they have strong mathematical knowledge of an algorithm used by the student compared to situations in which they have weaker mathematical knowledge about the algorithm used

Connecting mathematical knowledge with engagement in mathematics teaching practices

International audienceResearch suggests that mathematical knowledge is likely to influence how mathematics is taught. In turn, how mathematics is taught impacts students’ opportunities to learn mathematics. We report on a study examining the connection between preservice teachers’ mathematical knowledge and the nature of their eliciting and interpreting of student thinking. Our findings suggest that preservice teachers elicit and interpret student thinking with more emphasis on student understanding in situations in which they have strong mathematical knowledge of an algorithm used by the student compared to situations in which they have weaker mathematical knowledge about the algorithm used

Uncovering the Skills That Preservice Teachers Bring to Teacher Education: The Practice of Eliciting a Student’s Thinking

Although teacher education is the formal means by which novices are prepared for teaching, they come having already had significant experience in schools. Preservice teachers have formed habits of “teaching” which influence their learning to teach. This article reports a study of the specific knowledge of and skills with teaching practice that novices bring to teacher education with respect to one teaching practice, eliciting student thinking in elementary mathematics, and describes the use of a standardized teaching simulation to learn about novices’ skills. The findings reveal details about preservice teachers’ skills and habits of practice at the point that they enter formal teacher preparation. Preservice teachers’ ways of carrying out this particular practice are categorized into three distinct categories: (a) skills that need to be learned, (b) skills that can be built on, and (c) approaches that need to be unlearned.</jats:p