The Evidence-Based Reasoning Framework: Assessing Scientific Reasoning

Brown, Nicholas K.; Furtak, Erin Marie; Timms, Michael J; Nagashima, Sam O.; Wilson, Mark

doi:10.1080/10627197.2010.530551

Cited by 76 publications

(54 citation statements)

References 27 publications

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“…According to this skip-pattern we see that 62% of observers and 45% of students perceived that no discussion happened during class. In terms of reform assessment, this is a noteworthy finding by itself (Brown, Furtak, Timms, Nagashima, & Wilson, 2010;Clare & Aschbacher, 2001). As a result of this skip-pattern the KMO/Bartlett statistic indicated that sampling was not adequate for this construct.…”

Section: Test Of Hypothesismentioning

confidence: 99%

Development of a Student Self-Reported Instrument to Assess Course Reform

Morris

Parker

Nelson

et al. 2014

Educational Assessment

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This study examines the development and implementation of a survey-based instrument assessing the effectiveness of a course redesign initiative focused on student centeredness at a large midwestern university in the United States. Given the scope of the reform initiative under investigation in this study, researchers developed an instrument called the Classroom Experience Questionnaire (CEQ), which was administered to students enrolled in redesigned courses. Early findings demonstrate strong construct validity and internal reliability of the CEQ instrument as well as concurrent validity between the CEQ and observation data gathered in concert with self-report data. The authors conclude that in the absence of trained classroom observers, the developed student selfreport protocol can serve as a useful tool for measuring the constructivist orientation of pedagogy and student-centered nature of the learning environment in a higher education setting.

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Section: Test Of Hypothesismentioning

confidence: 99%

Development of a Student Self-Reported Instrument to Assess Course Reform

Morris

Parker

Nelson

et al. 2014

Educational Assessment

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show abstract

“…Thus, a construct map defines what is to be measured or assessed in terms general enough to be interpretable within a curriculum and potentially across curricula, but specific enough to guide the development of the other components.”Researchers have developed and used construct maps as assessment tools in a variety of studies (e.g., Arya & Maul, ; Becker et al, , ; Brandriet et al, ; Briggs, Alonzo, Schwab, & Wilson, ; N. J. Brown, Furtak, Timms, Nagashima, & Wilson, ; Claesgens et al, ; Loertscher et al, ; Rivet & Kastens, ; Schwarz et al, ; Sevian & Talanquer, ). Construct maps are distinct from scoring guides (sometimes referred to as rubrics), as scoring guides are typically developed for a single item, whereas construct maps provide a broader description of how learners' thinking might theoretically progress (Wilson, ).…”

Section: Literature Backgroundmentioning

confidence: 99%

Mapping undergraduate chemistry students' epistemic ideas about models and modeling

et al. 2019

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Developing and using scientific models is an important scientific practice for science students. Undergraduate chemistry curricula are often centered on established disciplinary models, and assessments typically provide students with opportunities to use these models to predict and explain chemical phenomena. However, traditional curricula generally provide few opportunities for students to consider the epistemic nature of models and the process of modeling. To gain a sense of how introductory chemistry students understand model changeability, model multiplicity, the evaluation of models, and the process of modeling, we use a construct‐mapping approach to characterize the sophistication of students' epistemic knowledge of models and modeling. We present a set of four related construct maps that we developed based on the work of other scholars and empirically validated in an undergraduate introductory chemistry setting. We use the construct maps to identify themes in students' responses to an open‐ended survey instrument, the models in chemistry survey, and discuss the implications for teaching.

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“…The ability to read and organize data, as well as to interact with numerical information, is referred to as Statistical Literacy (SL) in mathematics education (Groth, 2007). Numerous approaches that investigate the ability to systematically analyze evidence and to critically evaluate given conclusions (ER, or Evidence-based Reasoning) is addressed in Science Literacy (Brown, Furtak, Timms, Nagashima, & Wilson, 2010) and Critical Thinking research (Dunn, Halonen, & Smith, 2008). Furthermore, Groß Ophoff, Wolf, Schladitz, and Wirtz (accepted) present evidence to support the idea that one dominant factor that represents ERL in general can be distinguished from three secondary factors (IL, SL, and ER), which represent the particular requirements of various research steps.…”

Section: Conceptual Frameworkmentioning

confidence: 99%