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Open Access
Article
Publication date: 12 March 2024

Suné Maré and Ashley Teedzwi Mutezo

This paper aimed to determine the self- and co-regulation influences on the community of inquiry (CoI) for collaborative online learning.

Abstract

Purpose

This paper aimed to determine the self- and co-regulation influences on the community of inquiry (CoI) for collaborative online learning.

Design/methodology/approach

A quantitative survey was used on a sample of (N = 626) enrolled postgraduate students in a South African Open Distance and e-Learning (ODeL) university. The measuring instruments were the CoI and the shared metacognitive surveys. Correlation and multiple regression analyses were used to determine the association and influence of self- and co-regulation on the CoI.

Findings

The results indicated that self- and co-regulation related to the CoI (teaching, cognitive and social) presences. In addition, the results revealed that self- and co-regulation influence the CoI presences. Self-regulation had the highest influence on teaching and cognitive presence, while co-regulation influenced social presence.

Research limitations/implications

The study’s convenience sampling method from a single university limited the applicability of the findings to other online learning environments.

Practical implications

Higher educational teachers who encourage student self- and co-regulation may enhance their online teaching, cognitive and social presence when studying online. The research’s findings may be valuable to teachers to enable them to provide a more collaborative and interactive online learning environment and promote productive online communities.

Originality/value

This study contributes to the body of knowledge about the relationship between teaching, social and cognitive presence and self- and co-regulation within the CoI framework. Furthermore, there has also been limited research focussing on the dynamics of shared metacognition within the CoI framework in an ODeL context.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

Keywords

Open Access
Article
Publication date: 4 August 2022

Julia Kasch, Margien Bootsma, Veronique Schutjens, Frans van Dam, Arjan Kirkels, Frans Prins and Karin Rebel

In this opinion article, the authors share their experiences with and perspectives on course design requirements and barriers when applying challenge-based learning (CBL) in an…

Abstract

In this opinion article, the authors share their experiences with and perspectives on course design requirements and barriers when applying challenge-based learning (CBL) in an online sustainability education setting. CBL is an established learning approach for (higher) sustainability education. It enables teachers to engage students with open, real-life grand challenges through inter-/transdisciplinary student team collaboration. However, empirical research is scarce and mainly based on face-to-face CBL case studies. Thus far, the opportunities to apply CBL in online educational settings are also underinvestigated.

Using the TPACK framework, the authors address technological, pedagogical and content knowledge related to CBL and online sustainability education. The integration of the different components is discussed, providing teachers and course designers insight into design requirements and barriers.

This paper supports the promising future of online CBL for sustainability education, especially in the context of inter-/national inter-university collaboration, yet emphasizes the need for deliberate use of online collaboration and teaching tools.

Open Access
Article
Publication date: 5 December 2023

Ali Zarifhonarvar

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

4962

Abstract

Purpose

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

Design/methodology/approach

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

Findings

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

Research limitations/implications

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

Originality/value

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

Details

Journal of Electronic Business & Digital Economics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2754-4214

Keywords

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