Misconceptions in Van den Broeck's representation of misconceptions about learning disability research

The methods and conclusions of Van den Broeck (in this issue) are evaluated from two perspectives: (a) statistical considerations and (b) theoretical models of IQ and achievement, specifically reading achievement. We consider the statistical model proposed by Van den Broeck for the regression-based discrepancy model (RDM) to be either irrelevant or conceptually inconsistent with current models of IQ and achievement. The resulting simulation produced exemplar cases that are not realistic in terms of practice. The theoretical representations of IQ and achievement were, in our understanding, inconsistent with contemporary models of either. We suggest that acceptable models support the use of the RDM as it has been proposed by us and by others as one component of the determination of the presence or absence of a learning disability.     

Patterns in how pre-service elementary teachers formulate evidence-based claims about student cognition

Large-scale, robust implementation of teacher data-driven decision making (DDDM) is a challenging endeavor, impeded by numerous organizational, and teacher, factors. One well-documented barrier to teacher DDDM is underdevelopment of teacher data literacy. This study examines common errors made by pre-service elementary teachers in the formulation of evidence-based claims about student cognition (e.g. student strengths and weaknesses relative to content mastery) in a United States of America context. Teachers in the sample commonly made several errors in the articulation of evidence-based claims based on classroom assessments, including providing nonspecific, irrelevant evidence for claims, and misinterpreting statistical evidence. Specific implications for pre-service teacher education and future research are discussed.