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Martine Baars Sandra Visser Tamara van Gog Anique de Bruin Fred Paas 《Contemporary educational psychology》2013
Students’ Judgments of Learning (JOLs) are often inaccurate: students often overestimate their future test performance. Because of the consequences that JOL inaccuracy can have for regulating study activities, an important question is how JOL accuracy can be improved. When learning texts, JOL accuracy has been shown to improve through ‘generation strategies’, such as generating keywords, summaries, or concept maps. This study investigated whether JOL accuracy can also be improved by means of a generation strategy (i.e., completing blank steps in the examples) when learning to solve problems through worked example study. Secondary education students of 14–15 years old (cf. USA 9th grade) either studied worked examples or completed partially worked examples and gave JOLs. It was found that completion of worked examples resulted in underestimation of future test performance. It seems that completing partially worked-out examples made students less confident about future performance than studying fully worked examples. However, this did not lead to better regulation of study. 相似文献
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Monitoring accuracy, measured by judgements of learning (JOLs), has generally been found to be low to moderate, with students often displaying overconfidence, and JOLs of problem solving are no exception. Recently, primary school children’s overconfidence was shown to diminish when they practised problem solving after studying worked examples. The current study aimed to extend this research by investigating whether practising problem solving after worked example study would also improve JOL accuracy in secondary education. Adolescents of 14–15 years old (N = 143) were randomly assigned to one of five conditions that differed in timing of JOLs, whether practice problems were provided, and timing of the practice problems provided: (1) worked examples – JOL, (2) worked examples – delay – JOL, (3) worked examples – practice problems – JOL, (4) worked examples – practice problems – delay – JOL or (5) worked examples – delay – practice problems – JOLs. Results showed that practice problems improved absolute accuracy of JOLs as well as regulation accuracy. No differences in final test performance were found. 相似文献
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Instructional Science - In the current study, we examine the role of situation-specific motivational profiles in the effectiveness of video modeling examples for learning problem-solving and... 相似文献
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Raaijmakers Steven F. Baars Martine Paas Fred van Merriënboer Jeroen J. G. van Gog Tamara 《Metacognition and Learning》2019,14(1):21-42
Metacognition and Learning - Effective self-regulated learning in settings in which students can decide what tasks to work on, requires accurate self-assessment (i.e., a judgment of own level of... 相似文献
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Steven F. Raaijmakers Martine Baars Lydia Schaap Fred Paas Jeroen van Merriënboer Tamara van Gog 《Instructional Science》2018,46(2):273-290
Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and task-selection, is effective for improving adolescents’ problem-solving posttest performance after self-regulated learning. In these examples, the models used a specific task-selection algorithm in which perceived mental effort and self-assessed performance scores were combined to determine the complexity and support level of the next task, selected from a task database. In the present study we aimed to replicate prior findings and to investigate whether transfer of task-selection skills would be facilitated even more by a more general, heuristic task-selection training than the task-specific algorithm. Transfer of task-selection skills was assessed by having students select a new task in another domain for a fictitious peer student. Results showed that both heuristic and algorithmic training of self-assessment and task-selection skills improved problem-solving posttest performance after a self-regulated learning phase, as well as transfer of task-selection skills. Heuristic training was not more effective for transfer than algorithmic training. These findings show that example-based self-assessment and task-selection training can be an effective and relatively easy to implement method for improving students’ self-regulated learning outcomes. Importantly, our data suggest that the effect on task-selection skills may transfer beyond the trained tasks, although future research should establish whether this also applies when trained students perform novel tasks themselves. 相似文献
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