Measuring and Profiling Self-Regulated Learning in the Online Environment

Barnard‐Brak, Lucy; Lan, William Y.; Paton, Valerie Osland

doi:10.4018/978-1-61692-901-5.ch002

Cited by 11 publications

(16 citation statements)

References 23 publications

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“…This study, like much research studying SRL behaviours, has utilised self-report measures. The limitations of self-report are acknowledged, particularly in relation to over-estimation of ability; however, this bias can be considered a measurement error (Barnard-Brak et al, 2011) and should be independent of the factor relationship described here. Approaches which do not rely solely on self-report could be considered.…”

Section: Conclusion and Discussionmentioning

confidence: 94%

Self-regulated learning behaviour in the finance industry

Milligan

Fontana

Littlejohn

et al. 2015

Journal of Workplace Learning

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Purpose As work practices in knowledge intensive domains become more complex, individual workers must take greater responsibility for their ongoing learning and development. This study seeks to explore the role of self-regulatory behaviours in predicting workplace learning. Design/methodology/approach The study was conducted with knowledge workers from the finance industry. 170 participants across a range of work roles completed a questionnaire consisting of three scales derived from validated instruments (measuring learning opportunities, self-regulated learning, and learning undertaken). The relationship between the variables was tested through linear regression analysis. Findings Data analysis confirms a relationship between the learning opportunities provided by a role, and learning undertaken. Regression analysis identifies three key SRL behaviours that appear to mediate this relationship: task interest/value, task strategies, and self-evaluation. Together they provide an insight into the learning processes that occur during intentional informal learning. Research limitations/implications This quantitative study identifies a relationship between specific self-regulated learning behaviours and workplace learning undertaken in one sector. Qualitative studies are needed to understand the precise nature of this relationship. Follow up studies could explore whether the findings are generalizable to other contexts. Practical implications Developing a deeper understanding of how individuals manage their day to day learning can help shape the learning and development support provided to individual knowledge workers. Originality/value Few studies have explored the role of self-regulation in the workplace. This study adds to our understanding of this critical element of professional learning.

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Section: Conclusion and Discussionmentioning

confidence: 94%

Self-regulated learning behaviour in the finance industry

Milligan

Fontana

Littlejohn

et al. 2015

Journal of Workplace Learning

View full text Add to dashboard Cite

show abstract

“…In formal education, self‐report instruments such as the Motivated Strategies for Learning Questionnaire (MSLQ: Pintrich et al ., ), or the Metacognitive Awareness inventory (MAI: Schraw & Dennison, ), have proved useful in providing reliable measures of SRL behaviour in a variety of contexts (Barnard‐Brak et al ., ). Self‐report instruments can be deployed efficiently to large cohorts and facilitate statistical analyses which can highlight associations between SRL ‘scores’ and other behaviours and factors.…”

Section: Self‐regulated Learningmentioning

confidence: 97%

Measuring self‐regulated learning in the workplace

Fontana

Milligan

Littlejohn

et al. 2015

Int J Training Development

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In knowledge-intensive industries, the workplace has become a key locus of learning. To perform effectively, knowledge workers must be able to take responsibility for their own developmental needs, and in particular, to regulate their own learning. This paper describes the construction and validation of an instrument (the Self-Regulated Learning at Work Questionnaire) designed to provide a measure of self-regulated learning (SRL) behaviour in the workplace. The instrument has been validated through a pilot study with a cohort of 170 knowledge workers from the finance industry. Results indicate that the five scales of the instrument are reliable and valid, testing a broad range of sub-processes of SRL. The instrument can be used to identify knowledge workers who demonstrate different levels of SRL in workplace contexts for further exploration through qualitative studies and could also provide the basis of professional development tools designed to explore opportunities for self-regulation of learning in the workplace.

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“…Learners' self-regulatory skills become even more necessary in an autonomous learning environment (Barnard, Lan, To, Paton, & Lai, 2009;Barnard-Brak, Lan, & Paton, 2011;Harris, Reinhard, & Pilia, 2011), as the physical absence of the teacher, the absence of immediate feedback (Banerjee & Duflo, 2014;Hew & Cheung, 2014;Zheng et al, 2015;Kizilcec, Pérez-Sanagustín, & Maldonado, 2017) and support , the absence of consequences from the unsuccessful completion of the program (Nawrot & Doucet, 2014) and the lack of external pressure for progress and continuation of studies, motivation, and interaction with other members of the program (Harris et al, 2011) One such intervention that guided the learners to implement various self-regulatory strategies is the effort of Kizilcec et al (2016). During the pilot implementation of the xMOOC training program they created, they asked the learners who successfully completed it with great performance to record the self-regulatory strategies they followed and write recommendations to future learners, to help them achieve similar performance.…”

Section: Review Of Relevant Research Projectsmentioning

confidence: 99%

The Effect of Self-Regulation in a MOOC Environment on the Completion and Performance of Learners

Giasiranis¹,

Sofos²

2020

JSS

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MOOCs were created to change the way universities provide education. If to some extent they have succeeded, since they enable many people to attend them without prerequisites and conditions, it is observed that a very small percentage of those who participate finally manage to complete them. In the present study, which is part of the first researcher's doctoral research, we examine the extent to which helping learners apply the Mental Contrasting with Implementation Intentions (MCII) self-regulatory strategy in conjunction with a number of other self-regulatory processes in Zimmerman's model, contributed to the increase of self-regulation, performance and completion rates of those who participated in the first MOOC program of the University of the Aegean (Greece) on "Violence and bullying in schools". 1309 people started the program and completed it, 1050. The two research groups into which they were divided, showed statistically significant differences in their self-regulation, but not in the completion rates of the program (control group: 80.1%, experimental group: 80.3%) and their performance (90%-100% scale: control group: 62.5%, experimental group: 66.5%). Nevertheless, a very high percentage managed to complete it (80.2%), achieving at the same time very high performance. This result shows that self-regulation is not the only factor that contributes to the successful completion of programs and high performance. The instructional design of the program, its organization, and the quality of the instructional material play also an important role. These results can be useful in the design of future MOOCs programs.

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Measuring and Profiling Self-Regulated Learning in the Online Environment

Cited by 11 publications

References 23 publications

Self-regulated learning behaviour in the finance industry

Self-regulated learning behaviour in the finance industry

Measuring self‐regulated learning in the workplace

The Effect of Self-Regulation in a MOOC Environment on the Completion and Performance of Learners

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