Scholars of the Future: Assessing faculty contributionsin a changing scholarly context

Faculty Success Meeting, May 2019 Dawn Bratsch-Prince, Iowa State University Annmarie Cano, Wayne State University

Tuesday 11-12:15pm

Who is the “Scholar of the Future”?

Scholarly context of higher education is changing because of: Experiences and perspectives of the next generation

of scholars Millenials and post-millenials Increased numbers of women

A more racially-diverse population

A changing global society Influenced by communication technology and social

media

scholars more in demand as expert-writers, e.g., as the journalist ranks shrink

What makes scholars of the future different?

Interdisciplinary scholarly work Greater emphasis on collaborative work Engaged in the community Interested in applications of

research/scholarly/creative activity Productivity viewed more broadly than the

traditional scholarly standards

How are they productive?

Traditional forms of peer-reviewed scholarshipBUT ALSO…

Writing for the public The Conversation, op eds and essays on their research and

personal experiences Social media dissemination

Disseminating their own work and the work of others to advance the discipline

Student success Mentoring students, peers, community members

Other products Speaking engagements, databases, applications, patents,

licenses, community-engagement projects with defined outcomes

Traditional metrics

Traditional metrics Citation indices (e.g., H-Index)

Digital Commons downloads

Scholarly speaking invitations

These metrics do not provide evidence about the other non-traditional forms of productivity

What are alt-metrics?

Alternative metrics include a wider range of evidence of impact:

Citations by respected non-academic institutions or influence in the media

Citations in syllabi indicate significance of the work (see: HuMetrics “Humane indicators of excellence”)

# of reads/re-posts of social media and online content

# of times a new resource has been accessed or used

Reports on multimedia, science communication, community engagement work

Small group discussion

Does your institution encourage faculty to engage in non-traditional scholarship? If so, how?

How has your institution begun to use alt-metrics or other ways of recognizing the contributions of scholars of the future, either for awards and recognition, or promotion and tenure?

What challenges have you faced when discussing the adoption of alt-metrics? How were these challenges resolved?

Documenting research in new ways: ISU examples

Things to consider in Documenting Your Research

Include an overview of your research foci in lay terms for an educated audience Explain why your focus/i is/are important

Annotate your research products Your role and contribution

Students you mentor/ed

Impact measures

Next slides document ways faculty have articulated their scholarly framework, products, and impact

Whole-personWell-being

SuccessfulDevelopment

& Aging

Cognition & Everyday

Functioning

Environment,Context, &

Support

• Real-world functioning and competence• Individual differences (age, gender,

resources)• Contextual differences (task, residence)

Ecological Relevance

• Life-span developmental theory & contextualist and organismic approaches

• Varied methods (self-report, proxy report, observation, performance)

Theory and

Methods

• Social context (support, collaboration)• Formal intervention (training, technology)• Resilience and vitality

Plasticity

Figure 4. Principles guiding research approach.

Figure 3. Substantive research emphases. Overview of research

areas and approach

Credit: J. Margrett

Siegel, Z.D., Zhou, N., Zarecor, S., Lee, N., Campbell, D.A., Andorf, C.M., Nettleton, D., Lawrence-Dill, C.J., Ganapathysubramanaian, B., Friedberg, I., and Kelly, J.W. Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning. (preprint at doi: 10.1101/265918)

Lawrence-Dill Research Group Contributions: CJLD organized the proposal that supported the research (serving as the PI), assembled the research team, coordinated with other labs to conduct the research, coordinated paper writing sessions, and wrote portions of the paper. SZ created the Qualtrics data collection system. DAC managed the Amazon MTurk system.

Findings: Segmenting phenotypes from image data can be accomplished via machine learning, but training datasets are needed. To generate ground truth training data for machine learning classifiers, multiple crowdsourcing groups were compared. Master MTurk workers generate the best datasets followed by non-Master MTurk workers. Undergraduate students working for course credit produce lower quality datasets than either classification of MTurk workers.

Significance: Researchers can now markup image-based datasets for ground truth quickly.

Journal Impact Factor: 4.587 (Highlighted on PLoS Computational Biology frontpage)

Preprint metrics - Views: 2,450 Downloads: 391 Tweets: 41 Altmetric: 21 (top 5%)

DATA PRODUCTS

Data Collection Instruments

Dorius, Shawn F. and Abhinav Yedla. (2016). Google Autocomplete.

Dorius, Shawn F. and Abhinav Yedla. (2016). Wikipedia Edits, Views & Top 1000.

Dorius, Shawn F. and Abhinav Yedla. (2016). Social Mention Extract.

Dorius, Shawn F. and Joel Willers. (2014). Google Corpus Extract.

Harmonized Data Sets and Social Indicators: (13,119 views, 1,085 downloads, 2/2019)

Dorius, Shawn F. (2017). Country Socioeconomic Status, 1880-2010 Dataset. (386 downloads)

Dorius, Shawn F. and Abhinav Yedla. (2017). Quantifying the Value of Citizenship, 2016. (64 downloads)

Dorius, Shawn F. and Xioachi Jin. (2016). Voice of the People Integrated Dataset. (44 downloads)

Dorius, Shawn F. and Xioachi Jin. (2016). Global Social Survey Data Infrastructure Dataset. (155 downloads)

Dorius, Shawn F. (2016). Population Socioeconomic Status, 1880-2010 Dataset.(436 downloads)

Resources

See handout for a quick list of resources and current postings on valuing and assessing new forms of faculty scholarship