13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Growing a growth mindset: characterizing how and why undergraduate students’ mindsets change

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          The extent to which students view their intelligence as improvable (i.e., their “mindset”) influences students’ thoughts, behaviors, and ultimately their academic success. Thus, understanding the development of students’ mindsets is of great interest to education scholars working to understand and promote student success. Recent evidence suggests that students’ mindsets continue to develop and change during their first year of college. We built on this work by characterizing how mindsets change and identifying the factors that may be influencing this change among upper-level STEM students. We surveyed 875 students in an organic chemistry course at four points throughout the semester and interviewed a subset of students about their mindsets and academic experiences.

          Results

          Latent growth modeling revealed that students tended to shift towards viewing intelligence as a stable trait (i.e., shifted towards a stronger fixed mindset and a weaker growth mindset). This trend was particularly strong for students who persistently struggled in the course. From qualitative analysis of students’ written survey responses and interview transcripts, we determined that students attribute their beliefs about intelligence to five factors: academic experiences, observing peers, deducing logically, taking societal cues, and formal learning.

          Conclusions

          Extensive prior research has focused on the influence of mindset on academic performance. Our results corroborate this relationship and further suggest that academic performance influences students’ mindsets. Thus, our results imply that mindset and academic performance constitute a positive feedback loop. Additionally, we identified factors that influence undergraduates’ mindset beliefs, which could be leveraged by researchers and practitioners to design more persuasive and effective mindset interventions to promote student success.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: not found
          • Article: not found

          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Three approaches to qualitative content analysis.

            Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              lavaan: AnRPackage for Structural Equation Modeling

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                International Journal of STEM Education
                IJ STEM Ed
                Springer Science and Business Media LLC
                2196-7822
                December 2020
                July 08 2020
                December 2020
                : 7
                : 1
                Article
                10.1186/s40594-020-00227-2
                f0a38c40-16bf-400b-bf19-6f6c0ef131d3
                © 2020

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

                History

                Comments

                Comment on this article