Conference paper 2021

Competency-Based Experiential-Expertise and Future Adaptive Learning Systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference · Vol. 12793 LNCS · pp. 93-109
Abstract

In the near future an international career and life-long learning ecosystem will be developed to not only support the growing dependence of international/global remote work teams and roles but to facilitate new technology that will enable learning to be more ubiquitous and available at the point-of-need. This paper describes the competency-based experiential-expertise (CBEE) learning and performance management support model that is designed for this future learning ecosystem. The model stems from applied research conducted with the military over the last decade, and other research going as far back as the early 1970’s [1] that argued today’s industrial education system does not prepare people for real occupational work, and limits access to those most in need of education. It’s suggested the current academic model simply cannot keep up with the growing or changing performance ability demands from new industries or new jobs in old industries [2, 3]. There are efforts in play to change the industrial-based academic model of learning for all its obsolescence, and to adopt a more competency-based approach [4–7]. However, even if successful, the academic model still doesn’t provide a means to manage the development and tracking of ability regarding existing, new or future workers in the nation's occupational labor force. Therefore, many hours and lots of money will still be required and spent for learning that either is not needed, doesn’t meet expectations or does not fix problems in occupational performance. This condition can be avoided if a single, data-driven, competency standard is followed and integrated into a joint academic/vocational training approach. To help support this idea, this paper describes a model and methodology of learning that works within the coming learning ecosystem, and consists of new ALS technology that must be researched further, and invested in to make learning more cost-effective regarding our future labor force, and provide learners with a greater return-on-investment. © 2021, Springer Nature Switzerland AG.

Keywords

Author Keywords

Competence Expertise experience Competency andragogy Adaptive-learning Neuroscience

Index Keywords

Learning systems Industrial research Ecosystems Cost effectiveness Life long learning Performance management Obsolescence Adaptive learning systems Adaptive systems Automatic identification Applied research Cost effective Industrial education Learning ecosystems New industry
Author Affiliations
Applied Research Laboratories, The University of Texas at Austin, Austin, TX, United States
Funding & Acknowledgements
No funding information
References 10 References
1 McClelland, David C., Testing for competence rather than for "intelligence"., The American psychologist, 28, 1, pp. 1-14, (1973)
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5 Itin, Christian M., Reasserting the Philosophy of Experiential Education as a Vehicle for Change in the 21st Century, Journal of Experiential Education, 22, 2, pp. 91-98, (1999)
6 undefined
7 Journal of Competency Based Education, (2016)
8 Schatz S, (2019)
9 Experience and Education, (1938)
10 Colleges Arent Preparing Students for the Workforce What this Means for Recruiters, (2015)
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