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Social Networks and Instructional Reform in STEM: The Teaching-Research Nexus.

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Title: Social Networks and Instructional Reform in STEM: The Teaching-Research Nexus.
Authors: Skvoretz, John1 jskvoretz@usf.edu, Kappelman, Katherine2, Marcy, Ariel3, McAlpin, Jacob D.1, Lewis, Jennifer E.1, Ziker, John P.2, Mertens, Karl2, Earl, Brittnee2, Shadle, Susan E.2, Couch, Brian A.3, Feola, Stephanie1, Prevost, Luanna B.1, Lane, A. Kelly4, Whitt, Blake5, Stains, Marilyne6
Source: Innovative Higher Education. Aug2023, Vol. 48 Issue 4, p579-600. 22p.
Subject Terms: *Social networks, *Communication, *Higher education, Earth sciences, Biology
Abstract: Instructional reform in STEM aims for the widespread adoption of evidence based instructional practices (EBIPS), practices that implement active learning. Research recognizes that faculty social networks regarding discussion or advice about teaching may matter to such efforts. But teaching is not the only priority for university faculty – meeting research expectations is at least as important and, often, more consequential for tenure and promotion decisions. We see value in understanding how research networks, based on discussion and advice about research matters, relate to teaching networks to see if and how such networks could advance instructional reform efforts. Our research examines data from three departments (biology, chemistry, and geosciences) at three universities that had recently received funding to enhance adoption of EBIPs in STEM fields. We evaluate exponential random graph models of the teaching network and find that (a) the existence of a research tie from one faculty member i to another j enhances the prospects of a teaching tie from i to j , but (b) even though faculty highly placed in the teaching network are more likely to be extensive EBIP users, faculty highly placed in the research network are not, dimming prospects for leveraging research networks to advance STEM instructional reforms. [ABSTRACT FROM AUTHOR]
Copyright of Innovative Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Instructional reform in STEM aims for the widespread adoption of evidence based instructional practices (EBIPS), practices that implement active learning. Research recognizes that faculty social networks regarding discussion or advice about teaching may matter to such efforts. But teaching is not the only priority for university faculty – meeting research expectations is at least as important and, often, more consequential for tenure and promotion decisions. We see value in understanding how research networks, based on discussion and advice about research matters, relate to teaching networks to see if and how such networks could advance instructional reform efforts. Our research examines data from three departments (biology, chemistry, and geosciences) at three universities that had recently received funding to enhance adoption of EBIPs in STEM fields. We evaluate exponential random graph models of the teaching network and find that (a) the existence of a research tie from one faculty member i to another j enhances the prospects of a teaching tie from i to j , but (b) even though faculty highly placed in the teaching network are more likely to be extensive EBIP users, faculty highly placed in the research network are not, dimming prospects for leveraging research networks to advance STEM instructional reforms. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Innovative Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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