Re-Designing the Course Design Process: Developing a Comprehensive Framework to Support Data-Informed Revision

Concurrent Session 4
Streamed Session

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Brief Abstract

While traditional online program revisions rely on the calendar, learning analytics provide more precise metrics to inform priorities. During this session, presenters will share a robust framework to support using learning analytics in an instructional design unit, including ethical, privacy, and security considerations and practical strategies for operationalizing analytics.

Extended Abstract

While online learning is a complex process, online courses and programs have traditionally relied on a calendar-based revision process that dictates revisiting courses on a recurring basis. However, advancements in learning analytics enable us to leverage empirical evidence alongside learning science to allow faculty and learning designers to be much more strategic about how they approach course revisions in support of improved learning outcomes. 

Scaling these opportunities across a portfolio of online programs requires careful attention to establishing a shared set of values and ethical standards that support strong practices around student and instructor data privacy, security, and transparency. With these standards in place, design units can develop data-informed course revision processes and workflows situated within their contexts and to meet their unique needs. 

This session will articulate a practical design model for leveraging learning data, course quality evaluations, and analyses to triangulate problems and enable educators to make precise course design improvements. Additionally, presenters will share their process for creating a comprehensive framework for applying the model, which includes developing robust ethical standards, articulating data risk categories, and establishing a step-by-step learning analytics project workflow.

The session objectives are as follows:

  • Situate learning analytics in the context of real-world learning design challenges
  • Identify methods for establishing a data-informed learning design process that is approachable to institutions at all levels of data maturity
  • Describe specific examples of how a large instructional design unit has utilized learning analytics to improve outcomes in several online programs

Presenters will engage audience members through interactive questions and answers to share how they have used data to inform decisions at their institutions. Presenters will collect specific learning design challenges and identify potential data sets and methodologies for addressing those challenges. The presenters will share their full Analytical Design Model including worksheets and other useful tools and facilitate an activity with the audience to apply these ideas in their contexts.