In today’s accountability climate, institutional leaders create and rely on data- driven decisions to promote the success of their institutions. Much of this data collection is delegated to internal stakeholders, such as faculty members, to obtain and systematically report. I along with two graduate students have a new book chapter coming out on the faculty perspectives on the use of data in higher education. In today’s post, I want to share what we see as the five challenges facing higher education in the area of faculty and data strategy.
Shifting Institutional Data Practices
To meet the demands of accountability, accreditation requires evidence of continuous improvement, especially regarding student learning outcomes, through faculty-driven assessment. The production of data on student learning outcomes relies heavily on the efforts of faculty members, who are left feeling that reporting this data increases their workload with little perceivable benefit. Faculty’s lack of clarity on the benefit of these data serves as a root cause of much of the skepticism that attends institutionally solicited data.
Challenge 1: Definition of “Data”
The word “data” can mean many things at different levels of an institution. Thus, one common challenge is the lack of a clear, institution-wide definition of the term. Faculty and their institutions may not share the same view of the importance of various data. What faculty view as important data may differ from the data valued by their institutions. At times, data solicited from faculty by their institution can feel irrelevant and be frustrating to produce. Data managers should consider the clarity and potentially negative perception of data-related terms when creating and communicating a data strategy plan or process with faculty.
Challenge 2: Where Does the Data Go?
When faculty members are asked to report analytics about their courses or students, for instance, they are all too often left out of the loop on the findings and end use of the data. Therefore, producing data can at times feel like tossing work into an abyss, causing faculty to question why they should contribute to such efforts in the first place. To help mitigate this issue, institutional leaders must work purposefully to secure faculty buy-in for the data their institution seeks and ensure that faculty are included in final reporting distributions.
Challenge 3: Perceived Harms of Data
Along the same lines as Challenge 2, a lack of clarity as to the process, purpose, and benefits of data collection may cause faculty to wonder how data can hurt or help them. Faculty reactions to both of these questions can be problematic. If driven by fear, faculty may be hesitant to participate willingly in data collection, drag their feet on fulfilling reporting requirements, or express skepticism and frustration about the value of the data. If driven by perceived benefit, however, faculty may be inclined to produce potentially suspect data that could paint them or their work in a positive light. For example, if an institution wants to see more summative growth in student learning at end-of-course exams, a faculty member may grade less harshly on such an exam for fear that lower grades would reflect poorly on her teaching performance. To counteract such unintended and negative outcomes, institu- tions should clearly convey how the data that faculty are tasked with produ- cing will be used (and not used) as part of the data strategy process.
Challenge 4: Technology
Typically, institutions today employ online systems or software to help streamline and automate data usage. Often these systems integrate with other institutional systems already in regular use; however, at times they stand alone and necessitate a unique process for data collection. As with all technology, there is a learning curve involved in using data collection software effectively. Therefore, in addition to needing to report data metrics, faculty are tasked with learning software programs that they may only use infrequently. To combat this challenge, institutional leaders and data managers must consider the learning curve inherent to their data collection systems and help faculty get up to speed as quickly as possible.
Challenge 5: Overreliance on Quantitative Data
Much of the data solicited by institutional leaders and external stakeholders is traditionally “grounded in quantitative measurements that emphasize percentages and benchmarks because they are easy to collect, interpret and dis- tribute” (Contreras-McGavin & Kezar, 2007, p. 70). Accountability systems often require measurable (i.e., numeric) metrics to prove success. However, not all the work that faculty do to aid student learning can be captured with a number. Nor can all classroom learning be represented—much less analyzed—quantitatively. Qualitative measures, such as firsthand student accounts of coursework or reflections on institutional curricula, may be useful for a more holistic understanding of student learning successes and shortcomings.
In sum, it is important to note that faculty of all ranks and positions face challenges with data. Those described above, rooted in the all too common uncertainty and lack of clarity surrounding the use and purpose of data, can be experienced by faculty in any role. Institutions can alleviate much faculty stress concerning data through carefully crafted communication and education, as well as purposeful efforts to include faculty voices when determining what types of data are valued.
This post is an excerpt from Faculty Perspectives in Data Strategy in Colleges and Universities: From Understanding to Implementation edited by Kristina Powers (Routledge, 2019).
