Tracking the Processes of Data Driven Decision-Making in Higher Education

In seeking to enhance the efficacy of pedagogical reforms at the K-12 level, researchers and policymakers suggest that educators should utilize data-driven decision making (DDDM) systems, instead of making curricular and programmatic decisions based solely on anecdote or tradition. Yet the provision of data alone is not a panacea, as data must be robust, salient to local practice, and supported by adequate technical and administrative systems. However, little is known about the nature of curricular decision-making processes within higher education in general, and STEM disciplines in particular.  Further inhibiting the use of DDDM in many organizations is the lack of high-quality data that is useful for administrators, instructors, and designers.  Based on prior research on postsecondary teaching we developed a new approach to studying teaching and learning across disciplines and courses that includes insights into faculty planning decisions, nuanced accounts of classroom teaching based on in-depth observations, and student-based accounts of instructional quality and the effects of classroom teaching on study habits, motivation, and learning styles.

The goals of this study are to (1) to identify whether or not formal systems exist for curricular decision-making in STEM departments, and if so, what types of data are used in these processes, (2) to collect data about instructor planning, classroom teaching, and student classroom experiences, (3) to prepare reports based on these data for departmental decision-makers to see if they enhance local systems.  Besides providing important insights into the nature of curriculum design in STEM departments, the study will also result in new empirical findings about the relationships between classroom teaching and student experiences.  In order to increase the prospects for long-term adoption of our approach to studying teaching and learning we will also provide training to administrators and STEM education leaders at each of our study sites in the collection and analysis of data. 

For more information about the project that led to this work see, and about the Teaching Dimensions Observation Protocol (TDOP) instrument see



TPDM Tracking the Processes of Data Driven Decision-Making in Higher Education is housed within the Wisconsin Center for Education Research at the School of Education, University of Wisconsin-Madison. This project is funded by the National Science Foundation under Award #1224624. Copyright ©2012, The Board of Regents of the University of Wisconsin System.