This paper builds on the current literature base about learning progressions in science to address the question, “What is the nature of the learning progression in the content domain of the structure of matter?” We introduce a learning progression in response to that question and illustrate a methodology, the Construct Modeling (Wilson, ) approach, for investigating the progression through a developmentally based iterative process. This study puts forth a progression of how students understand the structure of matter by empirically inter‐relating constructs of different levels of sophistication using a sample of 1,087 middle grade students from a large diverse public school district in the western part of the United States. The study also shows that student thinking can be more complex than hypothesized as in the case of our discovery of a substructure of understanding in a single construct within the larger progression. Data were analyzed using a multidimensional Rasch model. Implications for teaching and learning are discussed—we suggest that the teacher's choice of instructional approach needs to be fashioned in terms of a model, grounded in evidence, of the paths through which learning might best proceed, working toward the desired targets by a pedagogy which also cultivates students’ development as effective learners. This research sheds light on the need for assessment methods to be used as guides for formative work and as tools to ensure the learning goals have been achieved at the end of the learning period. The development and investigation of a learning progression of how students understand the structure of matter using the Construct Modeling approach makes an important contribution to the research on learning progressions and serves as a guide to the planning and implementation in the teaching of this topic. © 2017 Wiley Periodicals, Inc. J Res Sci Teach 9999:1024–1048, 2017
This meta-analysis shows that early child care attendance is not significantly associated with the risk of asthma or wheeze in children 6 years of age or older.
With the creation in 1992 of the Congestion Pricing Pilot Program, later renamed the Value Pricing Pilot Program, the U.S. Department of Transportation endorsed an expanded investigation into new tolling and pricing applications throughout the United States leveraging electronic toll-collection systems, demand-managing toll rates, and various infrastructure additions and conversions. As of 2005, 15 states had enrolled in the program, and each was attempting some form of tolling or pricing on their highway and road systems. Systematic study of the feasibility of such systems, as required by the program, revealed definitive public attitudes concerning new applications of tolling and pricing. Furthermore, the knowledge gained by earlier practitioners allowed those working on future projects to refine their approach and messages, thereby better managing public perception. This paper identifies the prevailing trends in public opinion concerning tolling and pricing. Using case studies from California, Texas, and Minnesota–where substantial pre- and post-implementation public opinion data are available–the paper identifies common barriers to public acceptance, the selling points of tolling and pricing, the potential supporters and opponents of pricing and tolling proposals, and strategies for educating the public on a proposed program.
Item explanatory models have the potential to provide insight into why certain items are easier or more difficult than others. Through the selection of pertinent item features, one can gather validity evidence for the assessment if construct-related item characteristics are chosen. This is especially important when designing assessment tasks that address new standards. Using data from the Learning Progressions in Middle School Science (LPS) project, this paper adopts an "item explanatory" approach and investigates whether certain item features can explain differences in item difficulties by applying an extension of the linear logistic test model. Specifically, this paper explores the effects of five features on item difficulty: type (argumentation, content, embedded content), scenario-based context, format (multiple-choice or open-ended), graphics, and academic vocabulary. Interactions between some of these features were also investigated. With the exception of context, all features had a statistically significant effect on difficulty.
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