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Electronic work-based learning (eWBL): a framework for trainers in companies and higher education.

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Title: Electronic work-based learning (eWBL): a framework for trainers in companies and higher education.
Authors: Perusso, Andre, Wagenaar, Robert
Source: Studies in Higher Education. Nov2024, Vol. 49 Issue 11, p1805-1821. 17p.
Subjects: Electron work function, Higher education, Educational cooperation, Cooperative education, Students
Abstract: Driven by COVID-19, remote work is popularising. Companies and employees are increasingly embracing its benefits of flexibility and convenience, showing reluctance to return to full-time office schedules. Similarly, companies and HEIs started offering remote forms of work-based learning (WBL) – or eWBL. However, remote work presents social and psychological barriers associated with physical isolation that need addressing. In the context of WBL, which aims to provide a real-work environment for students, it is essential to explore these barriers as they might undermine the development of critical competencies associated with WBL, particularly creativity, teamwork, collaboration, networking and situational awareness. While research has started producing alternatives to overcome some of these limitations, we still miss a robust framework for designing and implementing these and other potential new strategies that could further enhance eWBL. To address this gap, a comprehensive framework is developed by integrating literature recommendations with results from 27 case studies derived from the Erasmus + Project eWBL. The framework comprises five primary phases: design, preparation, onboarding, delivery, and assessment. Each phase contains several sub-phases that offer practical implementation steps to WBL trainers in higher education and industry on how to deliver high-quality eWBL. [ABSTRACT FROM AUTHOR]
Copyright of Studies in Higher Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Driven by COVID-19, remote work is popularising. Companies and employees are increasingly embracing its benefits of flexibility and convenience, showing reluctance to return to full-time office schedules. Similarly, companies and HEIs started offering remote forms of work-based learning (WBL) – or eWBL. However, remote work presents social and psychological barriers associated with physical isolation that need addressing. In the context of WBL, which aims to provide a real-work environment for students, it is essential to explore these barriers as they might undermine the development of critical competencies associated with WBL, particularly creativity, teamwork, collaboration, networking and situational awareness. While research has started producing alternatives to overcome some of these limitations, we still miss a robust framework for designing and implementing these and other potential new strategies that could further enhance eWBL. To address this gap, a comprehensive framework is developed by integrating literature recommendations with results from 27 case studies derived from the Erasmus + Project eWBL. The framework comprises five primary phases: design, preparation, onboarding, delivery, and assessment. Each phase contains several sub-phases that offer practical implementation steps to WBL trainers in higher education and industry on how to deliver high-quality eWBL. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Studies in Higher Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2024
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