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Colleagues as Change Agents: How Department Networks and Opinion Leaders Influence Teaching at a Single Research University.

ABSTRACT: Relationships with colleagues have the potential to be a source of support for faculty to make meaningful change in how they teach, but the impact of these relationships is poorly understood. We used a mixed-methods approach to investigate the characteristics of faculty who provide colleagues with teaching resources and facilitate change in teaching, how faculty influence one another. Our exploratory investigation was informed by social network theory and research on the impact of opinion leaders within organizations. We used surveys and interviews to examine collegial interactions about undergraduate teaching in life sciences departments at one research university. Each department included discipline-based education researchers (DBERs). Quantitative and qualitative analyses indicate that DBERs promote changes in teaching to a greater degree than other departmental colleagues. The influence of DBERs derives, at least partly, from a perception that they have unique professional expertise in education. DBERs facilitated change through coteaching, offering ready and approachable access to education research, and providing teaching training and mentoring. Faculty who had participated in a team based-teaching professional development program were also credited with providing more support for teaching than nonparticipants. Further research will be necessary to determine whether these results generalize beyond the studied institution.

SUBMITTER: Andrews TC

PROVIDER: S-EPMC4909337 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Colleagues as Change Agents: How Department Networks and Opinion Leaders Influence Teaching at a Single Research University.

Andrews T C TC Conaway E P EP Zhao J J Dolan E L EL

CBE life sciences education 20160101 2

Relationships with colleagues have the potential to be a source of support for faculty to make meaningful change in how they teach, but the impact of these relationships is poorly understood. We used a mixed-methods approach to investigate the characteristics of faculty who provide colleagues with teaching resources and facilitate change in teaching, how faculty influence one another. Our exploratory investigation was informed by social network theory and research on the impact of opinion leader ...[more]

PMID: 27174582

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Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Dataset Information

Colleagues as Change Agents: How Department Networks and Opinion Leaders Influence Teaching at a Single Research University.

Publications

Colleagues as Change Agents: How Department Networks and Opinion Leaders Influence Teaching at a Single Research University.

Similar Datasets

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