The aim of this study was to develop a standardized test addressed to measure preservice science teachers’ scientific reasoning skills, and to initially evaluate its psychometric properties. We constructed 123 multiple-choice items, using 259 students’ conceptions to generate highly attractive multiple-choice response options. In an item response theory-based validation study (N = 2,247), we applied multiple regression analyses to test hypotheses based on groups with known attributes. As predicted, graduate students performed better than undergraduate students, and students who studied two natural science disciplines performed better than students who studied only one natural science discipline. In contrast to our initial hypothesis, preservice science teachers performed less well than a control group of natural sciences students. Remarkably, an interaction effect of the degree program (bachelor vs. master) and the qualification (natural sciences student vs. preservice teacher) was found, suggesting that preservice science teachers’ learning opportunities to explicitly discuss and reflect on the inquiry process have a positive effect on the development of their scientific reasoning skills. We conclude that the evidence provides support for the criterion-based validity of our interpretation of the test scores as measures of scientific reasoning competencies.

Research in the field of students' understandings of models and their use in science describes different frameworks concerning these understandings. Currently, there is no conjoint framework that combines these structures and so far, no investigation has focused on whether it reflects students' understandings sufficiently (empirical evaluation). Therefore, the purpose of this article is to present the results of an empirical evaluation of a conjoint theoretical framework. The theoretical framework integrates relevant research findings and comprises five aspects which are subdivided into three levels each: nature of models, multiple models, purpose of models, testing, and changing models. The study was conducted with a sample of 1,177 seventh to tenth graders (aged 11-19 years) using open-ended items. The data were analysed by identifying students' understandings of models (nature of models and multiple models) and their use in science (purpose of models, testing, and changing models), and comparing as well as assigning them to the content of the theoretical framework. A comprehensive category system of students' understandings was thus developed. Regarding the empirical evaluation, the students' understandings of the nature and the purpose of models were sufficiently described by the theoretical framework. Concerning the understandings of multiple, testing, and changing models, additional initial understandings (only one model possible, no testing of models, and no change of models) need to be considered. This conjoint and now empirically tested framework for students' understandings can provide a common basis for future science education research. Furthermore, evidence-based indications can be provided for teachers and their instructional practice.

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Background: Recent developments in STEM and computer science education put a strong emphasis on twenty-first-century skills, such as solving authentic problems. These skills typically transcend single disciplines. Thus, problem-solving must be seen as a multidisciplinary challenge, and the corresponding practices and processes need to be described using an integrated framework. Purpose: We present a fine-grained, integrated, and interdisciplinary framework of problem-solving for education in STEM and computer science by cumulatively including ways of problem-solving from all of these domains. Thus, the framework serves as a tool box with a variety of options that are described by steps and processes for students to choose from. The framework can be used to develop competences in problem-solving. Sources of evidence: The framework was developed on the basis of a literature review. We included all prominent ways of domain-specific problem-solving in STEM and computer science, consisting mainly of empirically orientated approaches, such as inquiry in science, and solely theory-orientated approaches, such as proofs in mathematics. Main argument: Since there is an increasing demand for integrated STEM and computer science education when working on natural phenomena and authentic problems, a problem-solving framework exclusively covering the natural sciences or other single domains falls short. Conclusions: Our framework can support both practice and research by providing a common background that relates the ways, steps, processes, and activities of problem-solving in the different domains to one single common reference. In doing so, it can support teachers in explaining the multiple ways in which science problems can be solved and in constructing problems that reflect these numerous ways. STEM and computer science educational research can use the framework to develop competences of problem-solving at a finegrained level, to construct corresponding assessment tools, and to investigate under what conditions learning progressions can be achieved.

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