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Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy

Among myriad complex challenges facing educational institutions in this era of a rapidly evolving job marketplace is the development of career self-efficacy among students. Self-efficacy has traditionally been understood to be developed through the direct experience of competence, the vicarious expe...

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Autores principales: Brass, Tamishka, Kennedy, JohnPaul, Gabriel, Florence, Neill, Bec, Devis, Deborah, Leonard, Simon N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248419/
https://www.ncbi.nlm.nih.gov/pubmed/37304524
http://dx.doi.org/10.3389/frai.2023.1173099
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author Brass, Tamishka
Kennedy, JohnPaul
Gabriel, Florence
Neill, Bec
Devis, Deborah
Leonard, Simon N.
author_facet Brass, Tamishka
Kennedy, JohnPaul
Gabriel, Florence
Neill, Bec
Devis, Deborah
Leonard, Simon N.
author_sort Brass, Tamishka
collection PubMed
description Among myriad complex challenges facing educational institutions in this era of a rapidly evolving job marketplace is the development of career self-efficacy among students. Self-efficacy has traditionally been understood to be developed through the direct experience of competence, the vicarious experience of competence, social persuasion, and physiological cues. These four factors, and particularly the first two, are difficult to build into education and training programs in a context where changing skills make the specific meaning of graduate competence largely unknown and, notwithstanding the other contributions in this collection, largely unknowable. In response, in this paper we argue for a working metacognitive model of career self-efficacy that will prepare students with the skills needed to evaluate their skills, attitudes and values and then adapt and develop them as their career context evolves around them. The model we will present is one of evolving complex sub-systems within an emergent milieu. In identifying various contributing factors, the model provides specific cognitive and affective constructs as important targets for actionable learning analytics for career development.
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spelling pubmed-102484192023-06-09 Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy Brass, Tamishka Kennedy, JohnPaul Gabriel, Florence Neill, Bec Devis, Deborah Leonard, Simon N. Front Artif Intell Artificial Intelligence Among myriad complex challenges facing educational institutions in this era of a rapidly evolving job marketplace is the development of career self-efficacy among students. Self-efficacy has traditionally been understood to be developed through the direct experience of competence, the vicarious experience of competence, social persuasion, and physiological cues. These four factors, and particularly the first two, are difficult to build into education and training programs in a context where changing skills make the specific meaning of graduate competence largely unknown and, notwithstanding the other contributions in this collection, largely unknowable. In response, in this paper we argue for a working metacognitive model of career self-efficacy that will prepare students with the skills needed to evaluate their skills, attitudes and values and then adapt and develop them as their career context evolves around them. The model we will present is one of evolving complex sub-systems within an emergent milieu. In identifying various contributing factors, the model provides specific cognitive and affective constructs as important targets for actionable learning analytics for career development. Frontiers Media S.A. 2023-05-25 /pmc/articles/PMC10248419/ /pubmed/37304524 http://dx.doi.org/10.3389/frai.2023.1173099 Text en Copyright © 2023 Brass, Kennedy, Gabriel, Neill, Devis and Leonard. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Brass, Tamishka
Kennedy, JohnPaul
Gabriel, Florence
Neill, Bec
Devis, Deborah
Leonard, Simon N.
Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
title Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
title_full Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
title_fullStr Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
title_full_unstemmed Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
title_short Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
title_sort learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248419/
https://www.ncbi.nlm.nih.gov/pubmed/37304524
http://dx.doi.org/10.3389/frai.2023.1173099
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