Session Title: ROI is a False Number: Moving Towards Evidence-Based Learning

 

Session Number: 130

 

Room: Fiesta 5

 

Day and Timeslot: Monday - 9:45am to 11:45am

 

Format: Keynote Follow-Up

 

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Session Description: This director of the new doctoral program for Learning Leaders will present a powerful analysis of how we can respond to the call for "ROI". Yes, we must move towards a real and authentic evidence approach to the impact of learning. Coming from an economist, this is a critically important perspective.

 

What I'd like to do is engage you around the issue and explore both why we feel the need for ROI and what are some of the challenges around it. I'll post here in the next day or two a couple of articles to read as well.

 

Led by: Doug Lynch - Wharton School of Business/U of Penn Graduate School of Education

 

Doug Lynch is the vice dean at the graduate school of education at the University of Pennsylvania. Trained as an economist, he also did doctoral work in education evaluation, Political Theory and also has an MBA. He is particularly known for innovative partnerships between higher education and corporate learning programs and has had over 120,000 "corporate students". Doug has sat on gubernatorial boards, testified before congress and the United Nations on e-learning and has read both for the Sloan Foundation and the U.S. Department of Education. He sits on several editorial boards and professional association boards. He is the chair of the US delegation to ISO 232 - Standards in non-formal education.

 

 

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  • Recent comments:
    Jodi Schwendeman:I thoroughly enjoyed this session and would love to tap into your brain for a week solid..LOL! I love the concept of presenting the evidence for impact as opposed to trying to tie hard numbers to training.
    George Koch:Doug What to make sure we connect after Learning 2007 concerning you and your program making a site visit to the ADL Co-Laboratory Hub in Alexandria VA. Look forward to a follow up. George
    Matthew Campbell:I think this is a case of make your own example. After running a program you have to determine what will show if it worked or not. Once you know what those things are, see if your program worked! They key point I got out of this was to not waste time coming up with an arbitrary percentage.
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