Forward Thinking about Backward Design: Making Data-Driven Decisions to Support Programming
Concurrent Session 7
This interactive presentation will focus on using backward design principles to understand instructor and student needs and to implement programmatic and departmental changes regarding tech-enhanced, blended, and online learning. We will discuss results from past projects and ways these data have driven decision-making at two University of Wisconsin System institutions.
How can data be gathered from key audiences and be used to make decisions at the programmatic and departmental levels? McTighe and Wiggins’ Backward Design framework has been a popular method for guiding curriculum, assessment, and instruction in K12 and higher education since the late 1990s. In this interactive session, the presenters will share their experiences at two University of Wisconsin System institutions utilizing this framework as a guide to making data-driven decisions about programming, as well as how it may be used to enhance the vision, mission, goals, and activities of departments and centers that support tech-enhanced, blended, and online learning.
Specifically, this session will focus on providing participants with a Backward Design Framework to data-driven decision-making that features the following three phases:
Identifying desired results (i.e., What do you want to be able to do?)
Determining acceptable evidence (i.e., What do you need to know to be able to do what you want to do?)
Designing activities (i.e., How do you collect data in a way that allows you to provide acceptable evidence--who has the information you need and how/when is the best way to get that information? How do you analyze collected data and report results in a way that makes a case for change? After you have your cases, what do you do with them? How does this speak to the literature and trends in higher education?)
These three phases will be further described and the presenters will provide two concrete examples of how these phases were utilized at each of their institutions. The first example will focus on how data were collected from instructors and facilitators to inform programmatic decisions around an online and blended faculty development program. The second example will feature how an instructor support survey and student focus groups were used in data-driven decision making around evaluating and enhancing a Learning Technology Center's vision, mission, goals, and supporting activities to ensure audience needs are being met. Both examples will provide concrete examples of defining this process; collecting and analyzing data; reporting results; creating cases; and applying what is learned to make decisions at the programmatic and departmental levels.
The session will conclude with a discussion of rewards and challenges regarding the use of this framework, leaving time for participants to share their experience using data to inform decision-making at their institutions and ask questions.
Presenters intend to engage the audience at various points in this interactive session through the utilization of the online polling system, Kahoot. Kahoot has found a great use in conference presentations as a way for presenters to actively engage participants through competitive gaming. Throughout the session participants will be asked to engage in answering questions about the process and the data that were collected (e.g., What was the topic related to emerging technology and pedagogy that instructors were most interested in learning more about? What did instructors cite as their biggest barrier to participating in faculty development?) in the two examples provided, with participants earning the most points for quick and correct answers.
By the conclusion of this session, participants will be able to:
Describe the Backward Design Framework to data driven decision-making;
Apply the Backward Design Framework to planning programs or departmental vision, mission, goals, and activities at their own institution;
Determine different types of data that could provide answers to key questions, further supporting and enabling decision-making; and
- Discuss rewards and challenges of data-driven decision making.