It is widely understood that the current political climate is not friendly to higher education. Moreover, the contexts in which the contemporary university functions—social, political, cultural, scientific, and technical—are shifting rapidly. At the same time, the fundamental structure and function of academic institutions have not changed a great deal in response to these radical changes; and it is difficult for university leaders to effectively assess the value of their faculty members and their research.
What are we to do? How can we find our relevance in today’s society? And how can the university retain (or regain) its importance in a rapidly changing society?
The fragmentation of disciplines and increasing specialization within nearly every field makes it difficult for even seemingly “direct” colleagues to assess importance and influence of another faculty member’s work.
It’s all about context. For all of us. Systems theorists going back to the early 1900s (before they even called themselves that) understood the interrelationships between systems, subsystems, and systems of systems. We assume that context is everything, and that in order to evaluate (in institutional terms) the “impact” of the faculty of a university (and to therefore justify its existence within the current political climate), a deep analytics of content and context is essential. One cannot be separated from the other. ScholarStat is a platform for such an analytics.
The ScholarStat platform presents a full picture of all faculty members within an institution, taking into account context and the situated nature of knowledge within a department, institution, discipline, and region. As with other major scholarly analytics firms, we leverage every major scholarly index and we weight these indices based on discipline. But this is only the very tip of the iceberg in terms of the work we do in order to develop a holistic approach to faculty development and assessment.
Our platform also includes a dynamic and variable set of products and services that gently “nudge” faculty members towards utilizing the tools through which we can better assess their activities -- not only their activities as academics but, from a systems perspective, the activities that might influence their activities as academics.
One of the most interesting components ScholarStat is the Faculty Performance Index “Ticker,” which displays key faculty performance indicators in real-time, and sits outside a faculty member's office. The “ticker” is part of a broader analytics program that also includes relationships that we have cultivated with fitness tracking companies and companies entering the “smart furniture” market. These personal and environmental data are a significant part of the “context” in which faculty members produce their work and are inseparable from the work itself.
Another facet of this advanced analytics network of faculty support are the intranet applications we develop for various institutions that integrate the research profiles of faculty along with other contextual data in order to help faculty identify opportunities for dissemination that will give them the most return for their effort.
Decreased state spending and other austerity measures have put immense pressure on institutions, which have increased class sizes and begun offering online courses in departments that had traditionally never done so. In this environment, being an effective educator while not sacrificing important time for research in an increasingly competitive funding landscape means having the right tools at your disposal. For administrators, it means that understanding faculty performance is more than just grant awards and publications—painting a full picture of faculty excellence is about meeting the institution’s educational goals as well.
ScholarStat has partnered with platforms that integrate powerful analytic tools for both synchronous and asynchronous course experiencesm, such as text analysis software built specifically to interpret student writing and identify the achievement of learning outcomes. Driven by machine learning that adapts to specific subjects and educational outcomes, such a tool will help educators make more efficient and effective judgments about grading and student performance, and will provide a consistent and objective approach to the interpretation of student learning.
We are also working to produce a suite of classroom analytics that cater towards real-time feedback to teachers of in-person educational content. Such tools include integrated skills for Amazon’s Alexa that produce real-time analysis of student discussion contributions using voice recognition and sentiment analysis. Other research in which ScholarStat has become involved is in the use of facial expression recognition to gauge student alertness and emotional response, especially in large lecture settings. Linking these various synchronous and asynchronous measures of the efficacy of particular modes of instruction, ScholarStat will be able to produce predictive analytics about student comprehension and engagement, which can help faculty tailor lesson plans to student progress, adapting in response to “just-in-time” information and building a more agile classroom.
Want to pilot/beta test the ScholarStat platform? Want to learn about employment opportunities? Or just want to stay in touch? Let us know. (We don't share your info with anyone. Promise.)