Personalizing Education at Scale Designing for Equity, Inclusion, - - PowerPoint PPT Presentation

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Personalizing Education at Scale Designing for Equity, Inclusion, - - PowerPoint PPT Presentation

Personalizing Education at Scale Designing for Equity, Inclusion, and Learning Tim McKay, University of Michigan, @TimMcKayUM Birth of the industrial university By 1900, enrollment had tripled, to 1949


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Personalizing Education at Scale

Designing for Equity, Inclusion, and Learning

Tim McKay, University of Michigan, @TimMcKayUM

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Birth of the industrial university

  • By 1900, enrollment had tripled, to

3482: the industrial era had begun

  • By 1950, enrollment expanded by

more than 10x, to 43,683

  • Michigan became a model for a

modern public research university

  • Graduation indoors became

impossible…

  • Today: students from all 50 US

states and 125 countries

1949 Graduation

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#1: Data for decision support

Providing data to students helps to support better decision making.

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Atlas provides students, faculty, and staff with historical data about courses, instructors, and academic majors. The goal is to support better decision making by all.

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Atlas course reports describe who takes a course, how they do, what they take before and after, what they go on to major in...

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Atlas major reports describe who majors in a subject, what they take along the way, how long it takes for them to complete their degree...

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Data for decision support

How can we extend this beyond course and major selection to support decision making for life?

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A caution about decision support...

Data like this can also be used to make forceful recommendations, as is regularly the case in the commercial world. We should be careful to preserve student exploration and support real freedom of choice.

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#2: Data for personalization

How can use data and behavioral science to tailor messaging and shape more successful students, even when teaching thousands?

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Large foundational courses present a challenge.

How to provide every student w/personalized feedback, encouragement, & advice?

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Designing and testing research-based interventions

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  • Dr. Michael

Brown

  • Dr. Patricia

Chen

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– – –

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#3: Data for discovery

Careful analysis of prior student experiences can be used to explore student success and test learning environments for equity.

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Intro Econ Lecture Course

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#4: Data to drive change

Discoveries based on robust data can motivate change, both within an institution and across the landscape of higher education.

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– – –

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