Who’s in Charge?—Dealing with the Self-regulation Dilemma in Digital Learning Environments

Bergamin, Per; Hirt, Franziska S.

doi:10.1007/978-3-319-73546-7_14

Cited by 11 publications

(6 citation statements)

References 58 publications

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“…Most importantly, the perceived obstacle of low self-regulation skills led to a less frequent implementation of digital learning in special education. This is in line with previous findings regarding the role of self-regulation in digital learning (Song, Kalet, and Plass 2016), indicating that higher self-regulation leads to improved learning outcomes (Bergamin and Hirt 2018;Hromalik and Koszalka 2018). On the other end of the scales, low self-regulation skills might lead to less effective digital learning processes.…”

Section: Discussionsupporting

confidence: 91%

“…Previous studies described comparatively low self-regulation skills of students with learning and/or behavioural difficulties (Grigorenko et al 2020;Nelson et al 2004). It may be expected that a (mis)match between self-regulatory skills and the prerequisites of digital learning would either promote, or hinder, digital learning (Bergamin and Hirt 2018;Hromalik and Koszalka 2018).…”

Section: Explaining the Intention To Implement Digital Learning For Students With Learning And/or Behavioural Difficultiesmentioning

confidence: 99%

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What predicts teachers’ use of digital learning in Germany? Examining the obstacles and conditions of digital learning in special education

Börnert-Ringleb

Casale

Hillenbrand

2021

European Journal of Special Needs Education

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The school closures during the COVID-19 outbreak triggered ashift towards digital learning. Whilst this is amajor challenge for mainstream education, the implementation of digital learning poses an even bigger challenge for special education teachers, as they are confronted with different learning requirements which might hinder digital learning processes. Previous research identified obstacles to digital learning in mainstream education, but conditions of digital learning in special education remain unclear. This article aims to provide insights on conditions of digital learning in special education at teacher-, school-, and student-levels. We examined whether the intention to use digital learning in special education is predicted by (1) teachers' self-efficacy, attitudes and their perceived usability of digital learning, (2) perceived organisational support and (3) perceived obstacles on student-level. Data of N = 722 special education teachers were collected during the pandemic. Results of mixed models indicate that specific self-efficacy in digital learning and perceived support were the strongest predictors for the intention to use digital learning, while alack of students' self-regulation and parental support represented major obstacles. The results underline the need for specific training in digital learning for special education teachers, and interventions in digital learning for students with special needs.

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Section: Discussionsupporting

confidence: 91%

Section: Explaining the Intention To Implement Digital Learning For Students With Learning And/or Behavioural Difficultiesmentioning

confidence: 99%

What predicts teachers’ use of digital learning in Germany? Examining the obstacles and conditions of digital learning in special education

Börnert-Ringleb

Casale

Hillenbrand

2021

European Journal of Special Needs Education

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“…Work within the KLI framework has investigated how practices interact and which combination of practices are most appropriate (Koedinger et al, 2013), as well as applying KLI principles to the study of new environments such as massive online open courses (Koedinger et al, 2016). Work using KLI has extended well beyond the original team that developed KLI (e.g., Bergamin & Hirt, 2018;Borracci et al, 2020).…”

Section: Learning Engineering: Past Successesmentioning

confidence: 99%

Learning engineering: A view on where the field is at, where it’s going, and the research needed.

Baker¹,

Boser²,

Snow³

2022

Technology, Mind, and Behavior

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Although the idea of learning engineering dates back to the 1960s, there has been an explosion of interest in the area in the last decade. This interest has been driven by an expansion in the computational methods available both for scaled data analysis and for much faster experimentation and iteration on student learning experiences. This article describes the findings of a virtual convening brought together to discuss the potential of learning engineering and the key opportunities available for learning engineering over the next decades. We focus the many possibilities into ten key opportunities for the field, which in turn group into three broad areas of opportunity. We discuss the state of the current art in these ten opportunities and key points of leverage. In these cases, a relatively modest shift in the field's priorities and work may have an outsized impact.

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“…Students can generate personalized learning plans based on the intelligent teaching system, independently select learning content, arrange learning progress, and carry out group cooperative learning (Fang et al, 2019;Walkington & Bernacki, 2020;Yilmaz, 2018). Individualized learning methods have higher requirements for students' self-regulation and self-management level (Bergamin & Hirt, 2018;Tseng, Yi, & Yeh, 2019), so teachers should also pay attention to the cultivation of students' independent learning ability in the teaching process.…”

Section: Challenges Of Ai In Educationmentioning

confidence: 99%

A Review on Artificial Intelligence in Education

Huang

Saleh

Liu

2021

Acad. J. Interdiscip. Stud.

146

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The emergence of innovative technologies has an impact on the methods of teaching and learning. With the rapid development of artificial intelligence (AI) technology in recent years, using AI in education has become more and more apparent. This article first outlines the application of AI in the field of education, such as adaptive learning, teaching evaluation, virtual classroom, etc. And then analyzes its impact on teaching and learning, which has a positive meaning for improving teachers' teaching level and students' learning quality. Finally, it puts forward the challenges that AI applications may face in education in the future and provides references for AI to promote education reform. Received: 16 January 2021 / Accepted: 24 March 2021 / Published: 10 May 2021

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Who’s in Charge?—Dealing with the Self-regulation Dilemma in Digital Learning Environments

Cited by 11 publications

References 58 publications

What predicts teachers’ use of digital learning in Germany? Examining the obstacles and conditions of digital learning in special education

What predicts teachers’ use of digital learning in Germany? Examining the obstacles and conditions of digital learning in special education

Learning engineering: A view on where the field is at, where it’s going, and the research needed.

A Review on Artificial Intelligence in Education

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