Improving Measurements of Self-Regulated Learning

By Philip H. Winne. 2010

420 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/b047c42eb8ca0f05f7420d2516b844c0d43cb2ec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sanna Järvelä
2. Inge Molenaar
3. Paul A. Kirschner
4. Ruth Wylie
5. Jeroen Janssen
6. Ernesto Panadero
7. J. V. van Merriënboer
8. Adar Ben-Eliyahu

In Computer Science Applications

1. Ernesto Panadero
2. Glenda S. Stump
3. J. A. Tapia
4. J. Huertas
5. Ruth Wylie
6. Liesbeth Kester
7. Lindsay McCardle
8. Jaclyn Broadbent

In Developmental and Educational Psychology

1. Ernesto Panadero
2. Sanna Järvelä
3. Matthew L. Bernacki
4. Jonna Malmberg
5. Adar Ben-Eliyahu
6. J. V. van Merriënboer
7. Paul A. Kirschner
8. Allyson Fiona Hadwin

Citations in the corpus

• The Impact of Metacognitive Instruction on Creative Problem Solving (2015, background): “Recent evidence has shown the weakness of relying upon self-report measures (Winne, 2010), yet little work has examined how prevalent self-report measures such as the Metacognitive Awareness Inventory (Schraw & Dennison, 1994) relate to creativity or how stable such measures themselves are over…”
• Comparing online and blended learner's self-regulated learning strategies and academic performance (2017, background): “That is, students may not consider the diverse strategies they use and how they differ across learning contexts or situations (Winne 2010), e.”
• Granularity matters: comparing different ways of measuring self-regulated learning (2019, methodology): “Due to the enormous increase in available information, SRL has become much more important for students in order to complete their education, but its measurement has been a problematic aspect (Winne 2010; Veenman et al. 2006).”
• Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning (2015, background): “As Winne (2010) alluded to, the field is still experimenting with measurement of SRL.”
• Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations (2015, methodology): “Contingent relations can be examined using conditional probabilities (Winne 2010), but require that fine-grained data be collected in sequence so that the likelihood that one event follows another can be gauged.”
• What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. (2014, background): “Self-regulated learning is a multidimensional construct that emphasizes the active role of the learner (Abar & Loken, 2010; Efklides, 2011; Greene & Azevedo, 2010; Winne, 2010; Zimmerman, 2008).”
• Coding Classroom Interactions for Collective and Individual Engagement (2015, background): “SNA aligns with a recent methodological innovation that collects realtime online trace data, which conceptualizes self-regulation of learning as a contextual event (J€arvel€a & Hadwin, 2013; Winne, 2010).”
• Rubrics and Self-Assessment Scripts Effects on Self-Regulation, Learning and Self-Efficacy in Secondary Education. (2012, background): “This unexpected finding may be due to the fact that each measure assesses different aspects of self-regulation (Winne, 2010).”