Reflection prompts and tutor feedback in a web-based learning environment: effects on students' self-regulated learning competence

Boom, Gerard Van den; Paas, Fred; Merriënboer, Jeroen J. G. van; Gog, Tamara van

doi:10.1016/j.chb.2003.10.001

Cited by 179 publications

(102 citation statements)

References 26 publications

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“…The request to reflect on one's own inquiry as well as the simulation outcomes after conducting computer-simulated experiments had positive effects on students' domain-specific knowledge acquisition. Through reflection, learners were becoming aware of their own learning processes (van den Boom et al 2004) and this might have contributed to more effective knowledge acquisition.…”

Section: Discussionmentioning

confidence: 99%

How effective is instructional support for learning with computer simulations?

et al. 2012

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The study examined the effects of two different instructional interventions as support for scientific discovery learning using computer simulations. In two well-known categories of difficulty, data interpretation and self-regulation, instructional interventions for learning with computer simulations on the topic ''ecosystem water'' were developed and tested using a sample of 124 eighth graders in science classes. The results demonstrate the effectiveness of instructional support for domain-specific factual, conceptual, and procedural knowledge acquisition. Students who received either only instructional support for data interpretation or only for self-regulation achieved the highest learning outcomes. However, a combination of instructional support for data interpretation and self-regulation seemed detrimental for knowledge acquisition. Students who received instructional interventions for both data interpretation and self-regulation also showed the highest values of perceived cognitive load. High cognitive load could be a reason for why a combination of particular instructional interventions does not lead to the expected positive learning outcomes.

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Section: Discussionmentioning

confidence: 99%

How effective is instructional support for learning with computer simulations?

et al. 2012

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“…It is assumed that in electronic learning environments reflection prompts can be used to provoke learners' reflection on the way they performed a task and compare it with other strategies to perform the task (Butler 1998;Seale and Cann 2000;Winne and Stockley 1998). An indication for the practical value of reflection prompts was provided by a study of Van den Boom et al (2004) who found that reflection prompts combined with tutor feedback had positive effects on the development of students' self-regulated learning competence. The behavioral component of this type of interactivity contains that learners have to respond on a specific question or prompt to evaluate their performance.…”

Section: Reflection Promptsmentioning

confidence: 99%

Interactivity in Video-based Models

2007

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In this review we argue that interactivity can be effective in video-based models to engage learners in relevant cognitive processes. We do not treat modeling as an isolated instructional method but adopted the social cognitive model of sequential skill acquisition in which learners start with observation and finish with independent, self-regulated performance. Moreover, we concur with the notion that interactivity should emphasize the cognitive processes that learners engage in when they interact with the learning environment. The four-component instructional design (4C/ID) model is used to define a set of cognitive processes: Elaboration and induction enable learners to construct schemas, whereas compilation and strengthening enable learners to automate these schemas. Pacing, cues, control over appearance, prediction, working in dyads, personalized task selection, and reflection prompts are identified as guidelines that might support learners to interactively construct schemas. Personalized task selection with part-task practice helps learners to interactively automate schemas.Keywords Modeling . Interactivity . Cognitive processes Learning motor skills by observing a model that performs the desired actions and behavior has been a successful and well researched instructional technique for the last 30 years (McCullagh et al. 1989;Wetzel et al. 1994;Wulf and Shea 2002). The current focus on flexibility in task performance and the mastering of complex cognitive skills (Jonassen 1999) has made modeling (i.e., observational learning) relevant to modern learning environments as well. At the same time, rapid developments in computer and software

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“…However, there is little evidence for both assumptions. First of all, research has shown that students do not apply and acquire self-regulation skills merely by engaging in self-regulated learning, but rather need additional training or instructional support such as prompts or tutoring (e.g., Aleven & Koedinger, 2002;Azevedo & Cromley, 2004; Van den Boom, Paas, & Van Merriënboer, 2007;Van den Boom, Paas, Van Merriënboer, & Van Gog, 2004). Secondly, although the assumption seems to be correct that personalized instruction can foster learning more compared to non-personalized instruction (e.g., Anderson, Corbett, Koedinger, & Pelletier, 1995;Camp, Paas, Rikers, & Van Merriënboer, 2001;Koedinger, Anderson, Hadley, & Mark, 1997;Salden, Paas, Broers, & Van Merriënboer, 2004), it is questionable whether self-regulated learning actually results in the adaptivity to students' needs that is required for effective personalized instruction.…”

Section: Introductionmentioning

confidence: 99%

Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning

Kostons

Gog

Paas

2012

Learning and Instruction

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215

201

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a b s t r a c tFor self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task selection, which may explain the poor learning outcomes often found with self-regulated learning. In experiment 1, the hypothesis was investigated and confirmed that observing a human model engaging in self-assessment, task selection, or both could be effective for secondary education students' (N ¼ 80) acquisition of selfassessment and task-selection skills. Experiment 2 investigated and confirmed the hypothesis that secondary education students' (N ¼ 90) acquisition of self-assessment and task-selection skills, either through examples or through practice, would enhance the effectiveness of self-regulated learning. It can be concluded that self-assessment and task-selection skills indeed play an important role in selfregulated learning and that training these skills can significantly increase the amount of knowledge students can gain from self-regulated learning in which they choose their own learning tasks.

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Reflection prompts and tutor feedback in a web-based learning environment: effects on students' self-regulated learning competence

Cited by 179 publications

References 26 publications

How effective is instructional support for learning with computer simulations?

How effective is instructional support for learning with computer simulations?

Interactivity in Video-based Models

Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning

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