Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations

Ben-Eliyahu, Adar; Bernacki, Matthew L.

doi:10.1007/s11409-015-9134-6

Cited by 158 publications

(87 citation statements)

References 68 publications

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“…Second, these findings contribute to the growing literature on SRL functioning across disciplinary contexts and samples. Despite the need for further investigations of the domain specificity of SRL and strategy use (i.e., both in terms of the kinds of processes and strategies invoked and their predictive validity), our findings support continued study of contingent relations among aspects of SRL and strategy use (Ben‐Eliyahu & Bernacki, ). Finally, our findings suggest a degree of similarity in the predictive validity of monitoring and strategy use that could be used to develop SRL interventions for a variety of contexts and samples.…”

Section: Discussionsupporting

confidence: 57%

“…Prominent models of SRL position metacognitive monitoring and control as central SRL processes that predict learning outcomes individually and in tandem (Winne & Hadwin, ; Zimmerman, ). However, much of the previous empirical research on these processes has focused on their predictive utility in isolation; only recently have researchers begun exploring how monitoring and control processes contingently interact to influence performance (Ben‐Eliyahu & Bernacki, ; Binbasaran Tüysüzoğlu & Greene, ).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring and depth of strategy use in computer‐based learning environments for science and history

Deekens

Greene

Lobczowski

2017

Brit J of Edu Psychol

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Section: Discussionsupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Monitoring and depth of strategy use in computer‐based learning environments for science and history

Deekens

Greene

Lobczowski

2017

Brit J of Edu Psychol

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“…The general predictiveness for both motivational variables is consistent with arguments made elsewhere (e.g., Furrer & Skinner, ; Pomerantz et al, ) that family involvement in children's learning at home may strengthen their feelings of relatedness to the family members, which in turn strengthen their perceptions of being capable and their interest to explore the environment. That is, learning occurs within the learner's social‐cultural environment (Ben‐Eliyahu & Bernacki, ). What is novel to the literature here is that the perceived family support in science appears to exert a greater impact on children's self‐efficacy in science than on interest to learn science.…”

Section: Discussionmentioning

confidence: 99%

Families support their children's success in science learning by influencing interest and self‐efficacy

et al. 2015

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How is a child's successful participation in science learning shaped by their family's support? We focus on the critical time period of early adolescents, testing (i) whether the child's perception of family support is important for both choice preferences to participate in optional learning experiences and engagement during science learning, and (ii) whether the effects on choice preferences and engagement are mediated through effects on child interest and self‐efficacy in science. Structural equation modeling is applied to data from two different contexts, one examining engagement during a science and technology center visit and the other examining engagement and learning during classroom instruction. Models from both datasets suggest that early adolescents' perceived family support for learning is associated with their choices for and engagement in science learning, and that these effects are mediated by effects on child interest and self‐efficacy in science. Further, children's family physical resources (e.g., available learning spaces and materials) predicts their perceived family support, but is not separately connected to either interest or self‐efficacy. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 450–472, 2016

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“…The methodologies used to research self-regulated learning are moving away from reliance on unimodal data sources, such as self-report questionnaires (Pintrich, 2004), think-aloud protocols (Greene, Robertson, & Costa, 2011), or computer logs (Aleven, Roll, McLaren, & Koedinger, 2010) toward multimodal data sources (Ben-Eliyahu & Bernacki, 2015). Recent research has attempted to triangulate multiple sources, such as case studies, interviews, and observations (Lichtinger & Kaplan, 2015), or combined quantitative surveys with qualitative open-response questions (McCardle & Hadwin, 2015).…”

Section: Future Directionsmentioning

confidence: 99%

Know thy student! Combining learning analytics and critical reflections to develop a targeted intervention for promoting self-regulated learning.

et al. 2015

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It is well established that a student's capacity to regulate his or her own learning is a key determinant of academic success, suggesting that interventions targeting improvements in self-regulation will have a positive impact on academic performance. However, to evaluate the success of such interventions, the self-regulatory characteristics of students need to be established. This paper examines the self-regulatory characteristics of a cohort of second-year allied health students, using the evaluation of responses to "meta-learning" assessment tasks supported by access data from the learning management system. Students primarily report using learning strategies from the performance and self-reflection phases. Although few reported using forethought strategies, access to preparatory course materials suggests that these were under-reported. Students who reported reviewing lectures as a learning strategy were more likely to access the online lecture recordings; however, higher access was associated with poorer academic performance. Cluster analysis of all available data showed high academic performance was positively associated with early submission of intra-semester assessment tasks but negatively associated with both use of, and reported of use of lecture recordings by students. These findings suggest that early submission of intra-semester assessment may be useful as a predictor of academic achievement.

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Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations

Cited by 158 publications

References 68 publications

Monitoring and depth of strategy use in computer‐based learning environments for science and history

Monitoring and depth of strategy use in computer‐based learning environments for science and history

Families support their children's success in science learning by influencing interest and self‐efficacy

Know thy student! Combining learning analytics and critical reflections to develop a targeted intervention for promoting self-regulated learning.

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