Thursday May 18th - 6:00 PM (UTC)

Skillifying at Scale

Uncovering Skills in Course Material via the LMS


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Articulating the career-relevant skills taught in courses is a powerful way to make the value of academic programs more transparent. But doing it effectively and at scale can be a challenge. In this webinar, see how Indiana University (IU) is testing and exploring ways to efficiently surface skills from the content in their learning management system (LMS), making career connections more visible for faculty, and learners.

Join us to learn and explore:

  • The potential benefits and applications of “skillifying” the content in your LMS — from career and academic advising, to skills-denominated learner records

  • Advice and early findings from IU’s pilot project involving six courses across various levels and disciplines 

  • How the Lightcast skills taxonomy helps to connect learning content to the in-demand skills employers value


  • Adam Maksl (Indiana University) - Faculty Fellow for eLearning Design & Innovation

  • Maggie Ricci (Indiana University) - Manager of eLearning Services

  • Morgan Halpert (Lightcast) Enterprise Account Executive

More about Indiana University’s approach to skillifying:

Indiana University, partnering with Lightcast, is exploring technology that can be used to analyze content within an institution’s LMS to surface the career-relevant skills embedded in those courses. Many universities have used Lightcast (formerly Emsi Burning Glass) tools to analyze course catalogs/syllabi to identify market-aligned skills embedded in those courses. But catalogs and syllabi are general documents that may not include language easily aligned to what employers use to describe desired skills. Course material, assessment prompts, rubrics and other data often included in course pages within an LMS are more likely to include such language. 

IU’s ultimate goal in assessing curricular material within the LMS is to develop an integration that allows a faculty member to analyze their course, discover the skills embedded in it using employer-relevant language, and choose which skill tags should be associated with this course. The data produced by such a system could be used in a variety of applications, from helping students choose courses based on skills they want to learn to populating comprehensive learner records to show the skills one learned in a course. 

To integrate skills-based learning frameworks, we need tech solutions that help faculty articulate the skills embedded in courses. And we need to do that at scale. This session will present the conceptual framework in IU’s approach and an early look at the technical solutions we’re developing.

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