Metrics in Higher Education Organizations

What do we mean by metrics and big data in higher education for assessing teaching, research and learning outcomes?

Marc Jacquinet

Keywords – Big data, higher education, metrics, assessment, work

What do we mean by metrics and big data in higher education for assessing teaching, research and learning outcomes?

Recent research has highlighted the relevance of big data and the use of measures and rankings for promoting markets into higher education. In the present communication we take aim at (1) understanding better what is the meaning of big data and metrics and (2) what are the main mechanisms and issues in these processes that can be viewed as an extension of market capitalism into higher education and research activities.

After a brief introduction and some conceptual issues, a theoretical framework is presented (section 1) in order to contextualize the issue of the meaning of metrics (section two) in universities before discussing the main mechanisms and relevant aspects of the use of metrics (section 3). 

A central argument is about the nature and structure of the information that is used in metrics and big data and how the different categories of information are collected. The way data is collected is not neutral and has implications on individuals and group behaviours, strategies of organizations and the transformation of values and referents.One way to collect data (and big data) is through apps and algorithms.  

Next it is critical to understand the meaning of metrics and big data, and this is the beginning of our second section. Metrics and big data can be considered as tools for (1) market building (introduction of private organizations and the financing of private corporations into universities and (2) competition enhancement in higher education, which is the transformations of universities (usually public or not-for-profit universities) into organizations closer to the functioning of private corporations and firms. These two processes are converging on a global scale.

We do not limit the meaning of metrics and big data to quantitative information, contrary to some authors. It consists also of qualitative data aiming at the ranking of activities and forcing or buttressing competition. 

Finally, the communication will close with a discussion of the mechanisms behind the collect of information and the use of that information in universities (at different levels, from top management, to teaching and learning assessments, among others). Metrics aim not just at describing “reality” and collecting information about outcomes and events, they are part of the workings of the management of universities and the elaboration of public policies and higher education policies. Metrics and big data aim at profiling and categorizing people, behaviours, attitudes, etc. with an aim that goes beyond the decision process in higher educations institutions and promotes prediction and the eradication of uncertainties. Categories and prediction through data collection are a way to make the management of future risks smoother, so we hope it to be.

We see that the very new opportunities bring along new challenges and new uncertainties.

Cite this article as: {Jacquinet, Marc}, Metrics in Higher Education Organizations,"{Management Critique}," in {}, 27/12/2019,

Marc Jacquinet

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