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CREATE cornerstone: introduction to scientific thinking, a new course for STEM-interested freshmen, demystifies scientific thinking through analysis of scientific literature.

ABSTRACT: The Consider, Read, Elucidate hypotheses, Analyze and interpret data, Think of the next Experiment (CREATE) strategy for teaching and learning uses intensive analysis of primary literature to improve students' critical-thinking and content integration abilities, as well as their self-rated science attitudes, understanding, and confidence. CREATE also supports maturation of undergraduates' epistemological beliefs about science. This approach, originally tested with upper-level students, has been adapted in Introduction to Scientific Thinking, a new course for freshmen. Results from this course's initial semesters indicate that freshmen in a one-semester introductory course that uses a narrowly focused set of readings to promote development of analytical skills made significant gains in critical-thinking and experimental design abilities. Students also reported significant gains in their ability to think scientifically and understand primary literature. Their perceptions and understanding of science improved, and multiple aspects of their epistemological beliefs about science gained sophistication. The course has no laboratory component, is relatively inexpensive to run, and could be adapted to any area of scientific study.

SUBMITTER: Gottesman AJ

PROVIDER: S-EPMC3587857 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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CREATE cornerstone: introduction to scientific thinking, a new course for STEM-interested freshmen, demystifies scientific thinking through analysis of scientific literature.

Gottesman Alan J AJ Hoskins Sally G SG

CBE life sciences education 20130101 1

The Consider, Read, Elucidate hypotheses, Analyze and interpret data, Think of the next Experiment (CREATE) strategy for teaching and learning uses intensive analysis of primary literature to improve students' critical-thinking and content integration abilities, as well as their self-rated science attitudes, understanding, and confidence. CREATE also supports maturation of undergraduates' epistemological beliefs about science. This approach, originally tested with upper-level students, has been ...[more]

PMID: 23463229

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Project description:BACKGROUND:Text mining (TM) methods have been used extensively to extract relations and events from the literature. In addition, TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events, e.g. negation, speculation, certainty and knowledge type. However, most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information. In this paper, we describe a novel, supervised method to extract new MK dimensions that encode Research Hypotheses (an author's intended knowledge gain) and New Knowledge (an author's findings). The method incorporates various features, including a combination of simple MK dimensions. METHODS:We identify previously explored dimensions and then use a random forest to combine these with linguistic features into a classification model. To facilitate evaluation of the model, we have enriched two existing corpora annotated with relations and events, i.e., a subset of the GENIA-MK corpus and the EU-ADR corpus, by adding attributes to encode whether each relation or event corresponds to Research Hypothesis or New Knowledge. In the GENIA-MK corpus, these new attributes complement simpler MK dimensions that had previously been annotated. RESULTS:We show that our approach is able to assign different types of MK dimensions to relations and events with a high degree of accuracy. Firstly, our method is able to improve upon the previously reported state of the art performance for an existing dimension, i.e., Knowledge Type. Secondly, we also demonstrate high F1-score in predicting the new dimensions of Research Hypothesis (GENIA: 0.914, EU-ADR 0.802) and New Knowledge (GENIA: 0.829, EU-ADR 0.836). CONCLUSION:We have presented a novel approach for predicting New Knowledge and Research Hypothesis, which combines simple MK dimensions to achieve high F1-scores. The extraction of such information is valuable for a number of practical TM applications.

Dataset Information

CREATE cornerstone: introduction to scientific thinking, a new course for STEM-interested freshmen, demystifies scientific thinking through analysis of scientific literature.

Publications

CREATE cornerstone: introduction to scientific thinking, a new course for STEM-interested freshmen, demystifies scientific thinking through analysis of scientific literature.

Similar Datasets

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