Effects of performance feedback valence on perceptions of invested mental effort

Raaijmakers, Steven; Baars, Martine; Schaap, Lydia; Paas, Fred; Gog, Tamara van

doi:10.1016/j.learninstruc.2016.12.002

Cited by 42 publications

(29 citation statements)

References 42 publications

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“…Another way of assessing the accuracy of subjective effort appraisals is to investigate whether they are affected by experimental manipulations that are unrelated to the effort invested. Raaijmakers et al (2017) had students work on problem-solving tasks. Before they rated their effort at the end of the task sequence, in some conditions, they were given (fake) feedback regarding their task performance, which was either positive or negative.…”

Section: Heuristic Cues Underlying Effort Appraisalsmentioning

confidence: 99%

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

2020

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A central factor in research guided by the Cognitive Load Theory (CLT) is the mental effort people invest in performing a task. Mental effort is commonly assessed by asking people to report their effort throughout performing, learning, or problem-solving tasks. Although this measurement is considered reliable and valid in CLT research, metacognitive research provides robust evidence that self-appraisals of performance are often biased. In this review, we consider the possibility that mental effort appraisals may also be biased. In particular, we review signs for covariations and mismatches between subjective and objective measures of effort. Our review suggests that subjective and most objective effort measures appear reliable and valid when evaluated in isolation, because they discriminate among tasks of varying complexity. However, not much is known about their mutual correspondence—that is, whether subjective measures covariate with objective measures. Moreover, there is evidence that people utilize heuristic cues when appraising their effort, similar to utilization of heuristic cues underlying metacognitive appraisals of performance. These cues are identified by exposing biases—mismatch in effects of cue variations on appraisals and performance. The review concludes with a research agenda in which we suggest applying the well-established methodologies for studying biases in self-appraisals of performance in metacognitive research to investigating effort appraisals. One promising method could be to determine the covariation of effort appraisals and objective effort measures as an indicator of the resolution of effort appraisals.

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Section: Heuristic Cues Underlying Effort Appraisalsmentioning

confidence: 99%

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

2020

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“…Fundamental research could examine what cues students typically use or are persuaded to use through contextual influences on selfjudgments during prototypical learning tasks, whereas applied research could study how this varies in more ecologically valid settings. The studies in the papers by Raaijmakers et al, 2017;Van Loon et al, 2017 andSchleinschok et al (2017) provide examples of research into the basis of monitoring judgments and mental effort ratings. Further steps would be to use the cue diagnosticity and cue utilization framework (Fig.…”

Section: Towards a Joint Research Agendamentioning

confidence: 99%

Bridging Cognitive Load and Self-Regulated Learning Research: A complementary approach to contemporary issues in educational research

Bruin

Merriënboer

2017

Learning and Instruction

100

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People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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“…Former studies investigated cognitive load by using subjective measurements such as self-reported questionnaires (Boekaerts, 2017;Zheng & Cook, 2012). Those self-reported questionnaires have some important disadvantages (e.g., subjective measures, assumption of constant workload capacity, see section 2.2 ; DeLeeuw & Mayer, 2008;Raaijmakers, Baars, Schaap, Paas & van Gog, 2017;Spanjers, van Gog & van Merriënboer, 2012). As a result, more researchers show interest in using objective, real-time measures.…”

Section: Introductionmentioning

confidence: 99%

Combining physiological data and subjective measurements to investigate cognitive load during complex learning

Larmuseau

Vanneste

Cornelis³

et al. 2019

FLR

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Cognitive load theory is one of the most influential theoretical explanations of cognitive processing during learning. Despite its success, attempts to assess cognitive load during learning have proven difficult. Therefore, in the current study, students' self-reported cognitive load after the problem-solving process has been combined with measures of physiological data, namely, electrodermal activity (EDA) and skin temperature (ST) during the problem-solving process. Data was collected from 15 students during a high and low complex task about learning and teaching geometry. This study first investigated the differences between subjective and physiological data during the problem-solving process of a high and low complex task. Additionally, correlations between subjective and physiological data were examined. Finally, learning behavior that is retrieved from log-data, was related with EDA. Results reveal that the manipulation of task complexity was not reflected by physiological data. Nevertheless, when investigating individual differences, EDA seems to be related to mental effort.

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Effects of performance feedback valence on perceptions of invested mental effort

Cited by 42 publications

References 42 publications

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

Bridging Cognitive Load and Self-Regulated Learning Research: A complementary approach to contemporary issues in educational research

Combining physiological data and subjective measurements to investigate cognitive load during complex learning

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