Formal curriculum-based learning is not sufficient to achieve the desired impact on working practice. The acquired knowledge needs to be reflected on in a self-regulated manner to contextualise it. Technology can help in this endeavour by prompting to set goals and transfer the acquired knowledge for bridging the training and working context.
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https://www.coursera.org/
https://badgeos.org/developers/
https://pixabay.com/illustrations/graduation-certificate-diploma-2663918/
https://www.codlearningtech.org/2015/11/23/5-questions-what-you-need-to-know-about-moocs/
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Eraut, M. (2000). Non-formal learning and tacit knowledge in professional work. The British Journal of Educational Psychology, 70(1), 11336.
Eraut, M. (2004). Informal learning in the workplace. Studies in Continuing Education, 26(2), 247273.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64-70.
Littlejohn, A., Milligan, C., & Margaryan, A. (2012). Charting collective knowledge: Supporting self-regulated learning in the workplace. Journal of Workplace Learning, 24(3), 226-238.
Goal Setting and
Actuation is
Important
(Zimmermann, 2002;
Littlejohn, Milligan &
Margaryan, 2012)
Reflecting on
Personal Experience
& Received
Knowledge is
Important
(Eraut, 2000/2004)
5. 息 Know-Center GmbH Research Center for Data-Driven Business and Big Data Analytics 2019
Technology can help by
5
Supporting documentation
6. 息 Know-Center GmbH Research Center for Data-Driven Business and Big Data Analytics 2019
Technology can help by
6
Supporting discussion
7. 息 Know-Center GmbH Research Center for Data-Driven Business and Big Data Analytics 2019
Technology can help by
7
Prompting goal setting and reflection
Learning Prompt
Reflective Prompt
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Two Promising Ways to AI-literacy
Hands-on Training & Self-Regulated Learning about AI
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https://commons.wikimedia.org/wiki/File:Two-ways-of-life.png
Experiential
9. 息 Know-Center GmbH Research Center for Data-Driven Business and Big Data Analytics 2019
AI Familiy Challenge: 40h of Learning
Build 3 types of
models to practice
Machine
Learning training
Brainstorm
and identify
problem in your
community
Build your invention
Hands-on design
challenges to
introduce
foundational
concepts
(neural networks,
sensors)
Hands-on
Experience in AI,
but no relation
to working context
of mentors!
Largest AI-literacy and mentoring program
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https://commons.wikimedia.org/wiki/File:Two-ways-of-life.png
Contextualization
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Bridging Training & Organizational Context
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Explicate Value of Mentoring to Increase Impact and Motivation
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Most Promising Way to AI-literacy
Best to be implemented in combination
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https://commons.wikimedia.org/wiki/File:Two-ways-of-life.png
Hands-on Training & Self-Regulation
13. 息 Know-Center GmbH
Know-Center GmbH
Research Center for Data-Driven
Business and Big Data Analytics
Inffeldgasse 13/6
8010 Graz, Austria
Firmenbuchgericht Graz
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gef旦rdert durch das Programm COMET (Competence Centers for Excellent Technologies), wir danken unseren F旦rdergebern:
General Manager
office@know-center.at
Prof. Stefanie Lindstaedt
Teamlead
vpammer@know-center.at
Viktoria
Senior Researcher
sdennerlein@know-center.at
Sebastian