Scientific Reasoning in Science Education: From Global Measures to Fine-Grained Descriptions of Students’ Competencies

Dear Colleagues,

In modern science- and technology-based societies, competencies that enable citizens to reason scientifically play a key role not only in science and technology-based careers but also for democratic co-determination (e.g., OECD, 2019). Developing these competencies is, hence, considered an important goal for science education in many countries around the globe (e.g., KMK, 2020; NRC, 2012).

Scientific reasoning competencies are defined as a complex construct that encompasses abilities such as identifying scientific problems, developing questions and hypotheses, categorizing and classifying entities, engaging in probabilistic reasoning, generating evidence through modeling, experimentation, etc., as well as communicating, evaluating, and scrutinizing claims (Lawson, 2004; NRC, 2012). These abilities require different forms of knowledge, such as content knowledge about the concepts of science, procedural knowledge about scientific methods, and epistemic knowledge of how such procedures warrant the claims that scientists advance (Osborne, 2014).

Research on scientific reasoning competencies is quite diverse. This diversity is—at least in part—caused by the manifold abilities that models of scientific reasoning comprise and the wide range of content, procedural, and epistemic knowledge that is deemed necessary to unfold these abilities. Differences exist, for instance, in the specific abilities that are addressed (e.g., applying the control-of-variables strategy: Reith & Nehring, 2020; handling of anomalous data: Chinn & Brewer, 2001; formulating questions and hypotheses: Vorholzer et al., 2016; 2020; developing and using models: Göhner & Krell, 2020). In addition, even studies that focus on similar abilities may use different theoretical frameworks and address different procedural and epistemic concepts (Vorholzer et al., 2016). Moreover, studies focus on a broad spectrum of respondents ranging from K-12 students (e.g., Koerber & Osterhaus, 2019; Mayer et al., 2014; Nehring et al., 2015; Vorholzer et al., 2016) to pre-service (e.g., Khan & Krell, 2019) and in-service teachers (e.g., Krell & Krüger, 2016).

Empirical research that focuses on scientific reasoning competencies typically describe the addressed competencies with a rather large grain-size: On the one hand, most studies offer a clear conceptual description of the addressed competencies, while the specific abilities, as well as the corresponding procedural and epistemic knowledge, often remain unclear (Vorholzer et al., 2016). For instance, a study may report that it focuses on students’ competencies to develop scientific investigations without stating whether that entails just knowledge of the control of variables strategy, or also knowledge of strategies, such as repeating measurements or measuring with large quantities. On the other hand, empirical studies often report aggregated measures, for instance in the form of a global scientific reasoning competency measure, a global measure of students’ procedural understanding of the control of variables strategy or a global measure of their epistemic understanding (e.g., naïve vs. sophisticated). This grain-size is completely sufficient when the goal of a study is, for instance, to investigate the effectiveness of a specific instructional intervention or to analyze the dimensionality of a competency model. Studies that utilize this grain-size have provided many vital insights regarding the modeling, assessment, and ways of fostering scientific reasoning competencies. However, goals such as designing instructions that match students’ current understanding and specific learning needs require more detailed insights. Therefore, from an educational point of view, insights into which procedural and epistemic knowledge students have and which they lack, respectively, and which abilities they have mastered and which they have not, is of vital importance for instructional practice. Such insights also provide manifold opportunities for further research, for instance in the development of students’ scientific reasoning competencies and the corresponding learning processes.

This Special Issue will provide a more fine-grained perspective on scientific reasoning competencies, illuminating specific abilities as well as the corresponding knowledge that constitute scientific reasoning, and the extent to which students of different age groups have these abilities and this knowledge. We invite studies from science education, educational psychology, and related fields that are concerned with the modeling and assessment of scientific reasoning competencies for all age groups. We are particularly interested in studies that focus on procedural and epistemic components of scientific reasoning competencies. We also invite contributions with a focus on content knowledge that have a strong link to scientific reasoning competencies (e.g., investigations of the relationship between content knowledge and the ability to reason in a given scientific context).

All contributions to this Special Issue are expected to provide an overview of their theoretical framework and fine-grained description of their specific operationalization of scientific reasoning competencies. In addition, we are looking for studies that provide detailed accounts of students’ abilities and their understanding of procedural and epistemic concepts that go beyond global measures. Contributions can be but are not limited to one of the following:

1. Theoretical contributions: How are or should scientific reasoning competencies be conceptualized and modeled?

Articles that adopt a fine-grained perspective on the knowledge and the abilities that constitute scientific reasoning competency, their structure, or their interrelationship.

2. Empirical contributions: What do we know about students’ scientific reasoning competencies?

Qualitative, quantitative, or mixed methods studies that provide detailed accounts of the scientific reasoning competencies and students’ understanding of corresponding (procedural or epistemic) concepts. Empirical contributions may also investigate interindividual or intraindividual differences, as well as relationships between scientific reasoning competencies and external factors. Re-analyses of existing studies that examine data on a finer grain size (e.g., on item level) are very welcome.

3. Methodological contributions: How to capture and assess scientific reasoning competencies on a fine-grained level?

Empirical or theoretical contributions that explicitly focus on novel or established methodological approaches to capturing and assessing scientific reasoning competencies on a fine-grained level as well as the challenges and chances that come along with them.

Interested contributors are asked to send a short structured abstract (cf., https://www.mdpi.com/journal/education/instructions) to one of the editors or the Editorial Office ([email protected]).

References:

Chinn, C. A., & Brewer, W. F. (2001). Models of data: A theory of how people evaluate data. Cognition and Instruction, 19(3), 323–393.

Göhner, M. & Krell, M. (2020). Preservice science teachers’ strategies in scientific reasoning: The case of modeling. Research in Science Education. https://doi.org/10.1007/s11165-020-09945-7

Khan, S. & Krell, M. (2019). Scientific reasoning competencies: A case of preservice teacher education. Canadian Journal of Science, Mathematics and Technology Education, 19, 446–464.

KMK [Sekretariat der Ständigen Konferenz der Kultusminister der Länder in der BRD] (Eds.). (2020). Bildungsstandards im Fach Biologie für die Allgemeine Hochschulreife. https://www.kmk.org/fileadmin/Dateien/veroeffentlichungen_beschluesse/2020/2020_06_18-BildungsstandardsAHR_Biologie.pdf

Koerber, S. & Osterhaus, C. (2019). Individual differences in early scientific thinking: Assessment, cognitive influences, and their relevance for science learning. Journal of Cognition and Development, 20(4), 510–533.

Krell, M., & Krüger, D. (2016). Testing models: A key aspect to promote teaching activities related to models and modelling in biology lessons? Journal of Biological Education, 50, 160–173.

Lawson, A. (2004). The nature and development of scientific reasoning: A synthetic view. International Journal of Science and Mathematics Education, 2(3), 307–338.

Mayer, D., Sodian, B., Koerber, S. & Schwippert, K. (2014). Scientific reasoning in elementary school children: Assessment and relations with cognitive abilities. Learning and Instruction, 29, 43–55.

Nehring, A., Nowak, K. H., Upmeier zu Belzen, A. & Tiemann, R. (2015). Predicting students’ skills in the context of scientific inquiry with cognitive, motivational, and sociodemographic variables. International Journal of Science Education, 37, 1343–1363.

NGSS Lead States (Eds.). (2013). Next Generation Science Standards: For states, by states. Washington, DC: The National Academies Press.

National Research Council (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.

OECD (2019). Conceptual learning framework: Learning Compass 2030. https://www.oecd.org/education/2030-project/teaching-and-learning/learning/learning-compass-2030/OECD_Learning_Compass_2030_concept_note.pdf

Osborne, J. (2014). Scientific practices and inquiry in the science classroom. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education. Volume 2 (pp. 579–599). New York: Routledge/Taylor & Francis Group.

Reith, M., & Nehring, A. (2020). Scientific reasoning and views on the nature of scientific inquiry: testing a new framework to understand and model epistemic cognition in science. International Journal of Science Education. doi:10.1080/09500693.2020.1834168

Vorholzer, A., von Aufschnaiter, C., & Kirschner, S. (2016). Entwicklung und Erprobung eines Tests zur Erfassung des Verständnisses experimenteller Denk- und Arbeitsweisen. Zeitschrift für Didaktik der Naturwissenschaften, 22(1), 25–41.

Vorholzer, A., von Aufschnaiter, C. & Boone, W. (2020). Fostering upper secondary students’ ability to engage in practices of scientific investigation: A comparative analysis of an explicit and an implicit instructional approach. Research in Science Education, 50, 333–359.

Dr. Moritz Krell
Prof. Dr. Andreas Vorholzer
Prof. Dr. Andreas Nehring
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• science education
• scientific reasoning
• competencies
• assessment
• 21st-century skills