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Transforming mentorship in STEM by training scientists to be better leaders

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      Abstract

      Effective mentoring is a key component of academic and career success that contributes to overall measures of productivity. Mentoring relationships also play an important role in mental health and in recruiting and retaining students from groups underrepresented in STEM fields. Despite these clear and measurable benefits, faculty generally do not receive mentorship training, and feedback mechanisms and assessment to improve mentoring in academia are limited. Ineffective mentoring can negatively impact students, faculty, departments, and institutions via decreased productivity, increased stress, and the loss of valuable research products and talented personnel. Thus, there are clear incentives to invest in and implement formal training to improve mentorship in STEM fields. Here, we outline the unique challenges of mentoring in academia and present results from a survey of STEM scientists that support both the need and desire for more formal mentorship training. Using survey results and the primary literature, we identify common behaviors of effective mentors and outline a set of mentorship best practices. We argue that these best practices, as well as the key qualities of flexibility, communication, and trust, are skills that can be taught to prospective and current faculty. We present a model and resources for mentorship training based on our research, which we successfully implemented at the University of Colorado, Boulder, with graduate students and postdocs. We conclude that such training is an important and cost‐effective step toward improving mentorship in STEM fields.

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      Most cited references 108

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      An alternative "description of personality": the big-five factor structure.

       Tony Goldberg (1990)
      In the 45 years since Cattell used English trait terms to begin the formulation of his "description of personality," a number of investigators have proposed an alternative structure based on 5 orthogonal factors. The generality of this 5-factor model is here demonstrated across unusually comprehensive sets of trait terms. In the first of 3 studies, 1,431 trait adjectives grouped into 75 clusters were analyzed; virtually identical structures emerged in 10 replications, each based on a different factor-analytic procedure. A 2nd study of 479 common terms grouped into 133 synonym clusters revealed the same structure in 2 samples of self-ratings and in 2 samples of peer ratings. None of the factors beyond the 5th generalized across the samples. In the 3rd study, analyses of 100 clusters derived from 339 trait terms suggest their potential utility as Big-Five markers in future studies.
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        Active learning increases student performance in science, engineering, and mathematics.

        To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes--although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.
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          Psychometric Properties of the HEXACO Personality Inventory.

          We introduce a personality inventory designed to measure six major dimensions of personality derived from lexical studies of personality structure. The HEXACO Personality Inventory (HEXACO-PI) consists of 24 facet-level personality trait scales that define the six personality factors named Honesty-Humility (H), Emotionality (E), Extraversion (X), Agreeableness (A), Conscientiousness (C), and Openness to Experience (O). In this validation study involving a sample of over 400 respondents, all HEXACO-PI scales showed high internal consistency reliabilities, conformed to the hypothesized six-factor structure, and showed adequate convergent validities with external variables. The HEXACO factor space, and the rotations of factors within that space, are discussed with reference to J. S. Wiggins' work on the circumplex.
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            Author and article information

            Affiliations
            [ 1 ] Department of Ecology and Evolutionary Biology University of Colorado Boulder Colorado
            [ 2 ] Hawkesbury Institute for the Environment Western Sydney University Richmond New South Wales Australia
            [ 3 ] Department of Animal and Range Sciences New Mexico State University Las Cruces New Mexico
            [ 4 ] Department of Veterinary Sciences University of Wyoming Laramie Wyoming
            [ 5 ] Department of Integrative Biology Michigan State University East Lansing Michigan
            [ 6 ] Department of Biological Sciences California State Polytechnic University Pomona California
            Author notes
            [* ] Correspondence

            Amanda K. Hund, Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO.

            Email: Amanda.Hund@ 123456Colorado.edu

            Contributors
            ORCID: http://orcid.org/0000-0001-7758-6757, Amanda.Hund@Colorado.edu
            Journal
            Ecol Evol
            Ecol Evol
            10.1002/(ISSN)2045-7758
            ECE3
            Ecology and Evolution
            John Wiley and Sons Inc. (Hoboken )
            2045-7758
            02 October 2018
            October 2018
            : 8
            : 20 ( doiID: 10.1002/ece3.2018.8.issue-20 )
            : 9962-9974
            6206201
            10.1002/ece3.4527
            ECE34527
            © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

            This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

            Counts
            Figures: 1, Tables: 0, Pages: 13, Words: 11154
            Product
            Funding
            Funded by: National Science Foundation
            Categories
            Academic Practice in Ecology and Evolution
            Academic Practice in Ecology and Evolution
            Custom metadata
            2.0
            ece34527
            October 2018
            Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.5.1 mode:remove_FC converted:30.10.2018

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