Learner-Control Effects: A Review of Reviews and a Meta-Analysis

Niemiec, Richard P.; Sikorski, Christian W.; Walberg, Herbert J.

doi:10.2190/jv1u-eq5p-x2pb-pdba

Cited by 105 publications

(93 citation statements)

References 30 publications

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“…Dependent on the specific cognitive consequences, interactivity might help but sometimes even hinder learning. This may be one of the main reasons why research on the effectiveness of learner control in computer-based instructions has resulted in such mixed findings (for reviews see Kay 2001;Lin and Hsieh 2001;Niemiec et al 1996;Williams 1996). Kennedy (2004) has proposed a model of interactivity that is centered around instructional events.…”

Section: Interactivity In Electronic Learning Environmentsmentioning

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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Section: Interactivity In Electronic Learning Environmentsmentioning

confidence: 99%

Interactivity in Video-based Models

2007

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“…This article focuses on self-regulated learning in which learners can choose their own learning tasks. Research has shown that having control over what information to study or what tasks to work on is not effective for novices' self-regulated learning (Azevedo et al, 2008;Goforth, 1994;Lawless & Brown, 1997;Niemiec, Sikorski, & Walberg, 1996;Williams, 1996). We assume that this may be due to novices' lack of self-assessment and task-selection skills, which play a crucial role in this kind of self-regulated learning.…”

Section: Introductionmentioning

confidence: 99%

“…Rather than having all learners follow the same instruction or practice schedule, which is often targeted at the average learner, personalized instruction allows learners who have difficulty with a task or topic to start at a lower level of complexity or obtain more support, while learners who find the new material easy can quickly move on to more complex materials. Such personalized instruction is expected to enhance students' motivation and learning outcomes more than non-personalized instruction that is the same for all students (Niemiec et al, 1996;Pintrich, 2004;Schnackenberg & Sullivan, 2000). However, there is little evidence for both assumptions.…”

Section: Introductionmentioning

confidence: 99%

“…Support for this assumption comes from studies that have shown that providing novice learners with control over their learning process may have beneficial effects on their motivation or involvement (e.g., Corbalan et al, 2008;Schnackenberg & Sullivan, 2000), but has detrimental effects on learning outcomes when compared to teacher or computer controlled fixed or personalized instruction (see e.g., Azevedo et al, 2008;Lawless & Brown, 1997;Niemiec et al, 1996;Williams, 1996). Beneficial effects on learning outcomes attained through selfregulated learning have been found mainly for learners with higher levels of prior knowledge (Lawless & Brown, 1997;Moos & Azevedo, 2008b, 2008cNiemiec et al, 1996;Scheiter & Gerjets, 2007;Schnackenberg & Sullivan, 2000;Steinberg, 1989), who, as mentioned above, are better able to monitor and assess their own performance than novices. In addition, Kostons et al (2010) investigated whether secondary education students who differed in the amount of knowledge gained from studying in a selfregulated learning environment, also differed in self-assessment and task-selection skills.…”

Section: Introductionmentioning

confidence: 99%

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

Kostons

Gog

Paas

2012

Learning and Instruction

212

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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“…More experienced learners, however, do possess adequate cognitive schemata and therefore are less apt to make ineffective instructional decisions and better able to control their own learning. Because of this, it is believed that as levels of expertise increase through experience, instructional-agent control should diminish in favour of learner control (see for a review, Niemiec, Sikorski, & Walberg, 1996). So, what support can be offered to help learners self-regulate their learning and optimize the positive effects of learner control in powerful electronic learning environments?…”

Section: Designing Supportmentioning

confidence: 99%