Resources and Publications

Journal articles

Hora, M. T. (2015). Toward a Descriptive Science of Teaching: How the TDOP Illuminates the Multidimensional Nature of Active Learning in Postsecondary Classrooms. Science Education.

Detailed accounts of teaching can shed light on the nature and prevalence of active learning, yet common approaches reduce teaching to unidimensional descriptors or binary categorizations. In this paper, I use the instructional systems-of-practice framework and the Teaching Dimensions Observation Protocol (TDOP) to advance an approach to thinking about teaching in science classrooms in more multidimensional terms. Using descriptive statistics and social network analysis, I examine the teaching practices employed by a group of science and engineering faculty (n = 56). Results indicate the extensive use of lecturing with premade visuals (observed in 65% of all 2-minute intervals comprising a class). However, the majority of instructors (n = 34) lectured for periods of 20 minutes or less. Using the Differentiated Overt Learning Activities (Chi & Wylie, 2014) framework to interpret TDOP codes, the data reveal lower rates of active learning modalities including “being active” (students answering questions, 28%; students problem solving (PS),15%), “being constructive” (students asking questions, 4%; students doing creative tasks, 2%), and “being interactive” (students working with peers to do creative tasks, 2%). Results indicate variation across disciplines and course contexts, that active learning is embedded within PowerPoint lectures, and that small group work exercises are not synonymous with constructivist activities. Implications for research, practice, and policy are discussed.

Sample formative feedback reports

As part of the final phase of the TPDM study, the research team has developed a new type of formative feedback on teaching report currently being field-tested in 3 California universities. These reports include data from a pre-observation interview, classroom observations, and a student survey, and are intended to provide the instructor with a quick yet detailed account of their classroom practice. Follow-up research is being conducted to ascertain whether these reports are viewed as useful by faculty, and the degree to which departments can adopt instruments and approaches exemplified by this approach.

Working papers

Matthew T. Hora, Jana Bouwma-Gearhart, and Hyoung Joon Park (2014). Exploring Data-Driven Decision-Making in the Field: How Faculty Use Data and Other Forms of Information to Guide Instructional Decision-making (WCER Working Paper No. 2014-3). Wisconsin Center for Education Research, University of Wisconsin-Madison.

One of the defining characteristics of current U.S. educational policy is a focus on using evidence, or data, to inform decisions about resource allocation, teacher hiring, and curriculum and instruction. But perhaps the biggest challenge to data-driven decision-making (DDDM) is the fact that data alone does not automatically result in high-quality decisions or improved teaching and learning. Research on data use indicates that translating raw data into useable information and actionable knowledge for teachers in the field requires not only adequate technical and social supports, but also an awareness of how educators in real-world settings actually use information and make decisions accordingly. Yet little is known about DDDM in higher education in general, and about how postsecondary faculty make sense of and utilize data and other information as part of their instructional decision-making processes in particular.

In this paper we address this gap in the literature by using naturalistic decision-making theory to generate practice-based descriptions of how 59 STEM faculty at three large public research universities used data as part of their course planning activities. Interview transcripts and notes taken while observing planning meetings were analyzed using an inductive approach to content analysis. In practice, respondents used different types of data (i.e., numeric) and other information (i.e., verbal, narrative, and information held in personal memory) obtained from sources such as student assessments, end-of-semester evaluations, and conversations with colleagues. Results also indicated that faculty generally engaged in the collection and analysis of data on their own in informal, ad-hoc scenarios ungoverned by institutional policy.  Exceptions included disciplines (e.g., mechanical engineering) with accreditation pressures and team-taught courses where structured (and supported) opportunities existed for faculty to collect, analyze, and reflect upon data about student learning. 

Thus, while numeric data are clearly viewed by this population of faculty as the most rigorous, in practice it is clear that faculty—even those with highly sophisticated systems that utilize quantitative data—also use other sources of information. These results suggest an opportunity for educational leaders to design policies and professional development initiatives that facilitate a more formal collection of and reflection on data by faculty. In pursuing such technical solutions, however, policymakers and educational leaders must carefully negotiate the tension between rigor and relevance, and learn from the challenges experienced in the K–12 sector regarding DDDM.

Policy briefs

Original proposal
Proposal by Matthew Hora, Jana Bouwma-Gearhart, and Richard Halverson submitted through WCER on January 12, 2012 to the NSF’s Transforming Undergraduate Education in Science (TUES) program.


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.