Transitions through lifelong learning: Implications for learning analytics
The ability to develop new skills and competencies is a central concept of lifelong learning. Research to date has largely focused on the processes and support individuals require to engage in upskilling, re-learning or training. However, there has been limited attention examining the types of suppo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X21000333 |
| _version_ | 1819319185536188416 |
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| author | Oleksandra Poquet Kirsty Kitto Jelena Jovanovic Shane Dawson George Siemens Lina Markauskaite |
| author_facet | Oleksandra Poquet Kirsty Kitto Jelena Jovanovic Shane Dawson George Siemens Lina Markauskaite |
| author_sort | Oleksandra Poquet |
| collection | DOAJ |
| description | The ability to develop new skills and competencies is a central concept of lifelong learning. Research to date has largely focused on the processes and support individuals require to engage in upskilling, re-learning or training. However, there has been limited attention examining the types of support that are necessary to assist a learner's transition from “old” workplace contexts to “new”. Professionals often undergo significant restructuring of their knowledge, skills, and identities as they transition between career roles, industries, and sectors. Domains such as learning analytics (LA) have the potential to support learners as they use the analysis of fine-grained data collected from education technologies. However, we argue that to support transitions throughout lifelong learning, LA needs fundamentally new analytical and methodological approaches. To enable insights, research needs to capture and explain variability, dynamics, and causal interactions between different levels of individual development, at varying time scales. Scholarly conceptions of the context in which transitions occur are also required. Our interdisciplinary argument builds on the synthesis of literature about transitions in the range of disciplinary and thematic domains such as conceptual change, shifts between educational systems, and changing roles during life course. We highlight specific areas in research designs and current analytical methods that hinder insight into transformational changes during transitions. The paper concludes with starting points and frameworks that can advance research in this area. |
| first_indexed | 2024-12-24T10:59:40Z |
| format | Article |
| id | doaj.art-22d27791a0d3430e8089f7f0ebcf124c |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2024-12-24T10:59:40Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-22d27791a0d3430e8089f7f0ebcf124c2022-12-21T16:58:44ZengElsevierComputers and Education: Artificial Intelligence2666-920X2021-01-012100039Transitions through lifelong learning: Implications for learning analyticsOleksandra Poquet0Kirsty Kitto1Jelena Jovanovic2Shane Dawson3George Siemens4Lina Markauskaite5University of South Australia, Australia; INSERM U1284, Université de Paris, France; Corresponding author. Present address: Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), F-75004 Paris, France.University of Technology Sydney, AustraliaUniversity of Belgrade, SerbiaUniversity of South Australia, AustraliaUniversity of South Australia, AustraliaThe University of Sydney, AustraliaThe ability to develop new skills and competencies is a central concept of lifelong learning. Research to date has largely focused on the processes and support individuals require to engage in upskilling, re-learning or training. However, there has been limited attention examining the types of support that are necessary to assist a learner's transition from “old” workplace contexts to “new”. Professionals often undergo significant restructuring of their knowledge, skills, and identities as they transition between career roles, industries, and sectors. Domains such as learning analytics (LA) have the potential to support learners as they use the analysis of fine-grained data collected from education technologies. However, we argue that to support transitions throughout lifelong learning, LA needs fundamentally new analytical and methodological approaches. To enable insights, research needs to capture and explain variability, dynamics, and causal interactions between different levels of individual development, at varying time scales. Scholarly conceptions of the context in which transitions occur are also required. Our interdisciplinary argument builds on the synthesis of literature about transitions in the range of disciplinary and thematic domains such as conceptual change, shifts between educational systems, and changing roles during life course. We highlight specific areas in research designs and current analytical methods that hinder insight into transformational changes during transitions. The paper concludes with starting points and frameworks that can advance research in this area.http://www.sciencedirect.com/science/article/pii/S2666920X21000333TransitionsLifelong learningHuman developmentLearning analyticsComplex dynamic systemsCausality |
| spellingShingle | Oleksandra Poquet Kirsty Kitto Jelena Jovanovic Shane Dawson George Siemens Lina Markauskaite Transitions through lifelong learning: Implications for learning analytics Computers and Education: Artificial Intelligence Transitions Lifelong learning Human development Learning analytics Complex dynamic systems Causality |
| title | Transitions through lifelong learning: Implications for learning analytics |
| title_full | Transitions through lifelong learning: Implications for learning analytics |
| title_fullStr | Transitions through lifelong learning: Implications for learning analytics |
| title_full_unstemmed | Transitions through lifelong learning: Implications for learning analytics |
| title_short | Transitions through lifelong learning: Implications for learning analytics |
| title_sort | transitions through lifelong learning implications for learning analytics |
| topic | Transitions Lifelong learning Human development Learning analytics Complex dynamic systems Causality |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X21000333 |
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