Digital support during self-regulated learning can improve metacognitive knowledge and skills in learners. Previous research has predominantly focused on embedding metacognitive support in domain-specific content. In this study, we examine a detached approach where digital metacognitive support is offered in parallel to ongoing domain-specific training via a digital tool. The primary support mechanism was self-explication, where learners are prompted to make, otherwise implicit, metacognition concrete.In a controlled pre-test/post-test quasi-experiment, we compared domain-specific and domain-general support and assessed the effects, use, and learners' perceptions of the tool. The results showed that self-explication is an effective mechanism to support and improve metacognition during self-regulated learning. Furthermore, the results confirm the effectiveness of offering detached metacognitive support. While only domain-specific metacognitive support was found to be effective, quantitative and qualitative analysis warrant further research into domain-general and detached metacognitive support.The results also indicated that, while students with higher metacognition found a lack of relevance of using the tool, students with lower metacognition are less likely to make (structural) use of the available support. A key challenge for future research is thus to adapt metacognitive support to learner needs, and to provide metacognitive support to those who would benefit from it the most. The paper concludes by formulating implications for future research as well as design of digital metacognitive support.
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In the dynamic landscape of higher education, fostering self-regulated learning (SRL) is crucial for empowering students to take control of their educational journey. This contribution explores the innovative approach of Me:Learning, a omprehensive toolset designed to enhance metacognitive knowledge and skills through the gamification of learning experiences.
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For many decades, teacher-structured hands-on simulations have been used in education mainly for developing procedural and technical skills. Stimulating contemporary learning outcomes suggests more constructivist approaches. The aim of this study is to examine how self-regulated learning (SRL), an important constructivist learning environment characteristic, is expressed in hands-on simulations. Via structured observations of teachers’ SRL promoting strategies and students’ SRL strategies in eight hands-on simulations, along the three phases of SRL, this study is the first to expose whether students and teachers use SRL in hands-on simulations, what these strategies look like and what their quality is. The results show that both students and teachers demonstrate SRL behaviour in the forethought, performance and reflection phase to some extent, but that they vary considerably in their occurrences, form and quality and provide opportunities for improvement. For example, teacher strategies ‘modelling’ and ‘scaffolding’ were often used, while ‘giving attribution feedback’ and ‘evaluation’ were lacking. The student strategy ‘proposing methods for task performance’ was used regularly, while ‘goal-setting’ and ‘self-monitoring’ were often absent. An overview shows exemplary teacher and student behaviours in the SRL phases with lower, medium and higher quality in hands-on simulations.
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Morssink-Santing, V. E., van der Zee, S., Klaver, L. T., de Brouwer, J., andamp; Sins, P. H. (2024). The long-term effect of alternative education on self-regulated learning: A comparison between Montessori, Dalton, and traditional education. Studies in Educational Evaluation, 83, 101380. https://doi.org/10.1016/j.stueduc.2024.101380
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The present study aimed to develop a football-specific self-report instrument measuring self-regulated learning in the context of daily practice, which can be used to monitor the extent to which players take responsibility for their own learning. Development of the instrument involved six steps: 1. Literature review based on Zimmerman's (2006) theory of self-regulated learning, 2. Item generation, 3. Item validation, 4. Pilot studies, 5. Exploratory factor analysis (EFA), and 6. Confirmatory factor analysis (CFA). The instrument was tested for reliability and validity among 204 elite youth football players aged 13-16 years (Mage = 14.6; s = 0.60; 123 boys, 81 girls). The EFA indicated that a five-factor model fitted the observed data best (reflection, evaluation, planning, speaking up, and coaching). However, the CFA showed that a three-factor structure including 22 items produced a satisfactory model fit (reflection, evaluation, and planning; non-normed fit index [NNFI] = 0.96, comparative fit index [CFI] = 0.95, root mean square error of approximation [RMSEA] = 0.067). While the self-regulation processes of reflection, evaluation, and planning are strongly related and fit well into one model, other self-regulated learning processes seem to be more individually determined. In conclusion, the questionnaire developed in this study is considered a reliable and valid instrument to measure self-regulated learning among elite football players.
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Higher education is making increasing demands on students’ learner-agency and self-directed learning. What exactly are learner agency and self-directed learning? Why are they important? And what does it take? The aim of the five questions and answers on this poster is to support a common language and to be used as conversation starters when you want to discuss learner-agency and self-directed learning.
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A promising contribution of Learning Analytics is the presentation of a learner's own learning behaviour and achievements via dashboards, often in comparison to peers, with the goal of improving self-regulated learning. However, there is a lack of empirical evidence on the impact of these dashboards and few designs are informed by theory. Many dashboard designs struggle to translate awareness of learning processes into actual self-regulated learning. In this study we investigate a Learning Analytics dashboard based on existing evidence on social comparison to support motivation, metacognition and academic achievement. Motivation plays a key role in whether learners will engage in self-regulated learning in the first place. Social comparison can be a significant driver in increasing motivation. We performed two randomised controlled interventions in different higher-education courses, one of which took place online due to the COVID-19 pandemic. Students were shown their current and predicted performance in a course alongside that of peers with similar goal grades. The sample of peers was selected in a way to elicit slight upward comparison. We found that the dashboard successfully promotes extrinsic motivation and leads to higher academic achievement, indicating an effect of dashboard exposure on learning behaviour, despite an absence of effects on metacognition. These results provide evidence that carefully designed social comparison, rooted in theory and empirical evidence, can be used to boost motivation and performance. Our dashboard is a successful example of how social comparison can be implemented in Learning Analytics Dashboards.
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Using path analysis, the present study focuses on the development of a model describing the impact of four judgments of self-perceived academic competence on higher education students' achievement goals, learning approach, and academic performance. Results demonstrate that academic self-efficacy, self-efficacy for self-regulated learning, academic self-concept, and perceived level of understanding are conceptually and empirically distinct self-appraisals of academic competence which have a different impact on student motivation, learning, and academic performance. Furthermore, the current study suggests that students reflecting high scores on the four measures of self-perceived competence, are more persistent, more likely to adopt mastery and/or performance approach goals, less anxious, process the learning material at a deeper level, and achieve better study results. However, this study also warns that high self-perceived competence (e.g., perceived level of understanding), if not accompanied by a mastery goal orientation, can turn into overconfidence resulting in lower persistence levels and poorer study results.
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