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Does Concrete Content Help People to Reason Scientifically?: Adaptation of Scientific Reasoning Scale.

ABSTRACT: In this paper, we explored the scientific literacy of a general sample of the Slovak adult population and examined factors that might help or inhibit scientific reasoning, namely the content of the problems. In doing so, we also verified the assumption that when faced with real-life scientific problems, people do not necessarily apply decontextualized knowledge of methodological principles, but reason from the bottom up, i.e. by predominantly relying on heuristics based on what they already know or believe about the topic. One thousand and twelve adults completed three measures of scientific literacy (science knowledge, scientific reasoning, attitudes to science) and several other related constructs (numeracy, need for cognition, PISA tasks). In general, Slovak participants' performance on scientific reasoning tasks was fairly low and dependent on the context in which the problems were presented-there was a 63% success rate for a version with concrete problems and a 56% success rate for the decontextualized version. The main contribution of this study is a modification and validation of the scientific reasoning scale using a large sample size, which allows for more thorough testing of all components of scientific literacy.

Supplementary information

The online version contains supplementary material available at 10.1007/s11191-021-00207-0.

SUBMITTER: Basnakova J

PROVIDER: S-EPMC8035062 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWe address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain.ResultsThe main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology.ConclusionsWe first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain Architecture Management System (BAMS).

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Does Concrete Content Help People to Reason Scientifically?: Adaptation of Scientific Reasoning Scale.

Supplementary information

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