Measuring achievement goal motivation, mindsets and cognitive load: validation of three instruments’ scores

Cook, David A.; Castillo, R.; Gas, Becca L.; Artino, Anthony R.

doi:10.1111/medu.13405

Cited by 53 publications

(33 citation statements)

References 70 publications

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“…There is only one scale that is widely used to measure mindset, the Implicit Theories of Intelligence Scale, which we will refer to as the "mindset scale" (Dweck, 1999;Sisk et al, 2018). The mindset scale is an eight-item Likert-scale instrument in which four items measure growth mindset and four items measure fixed mindset, which are distinct but strongly correlated constructs (Dweck, 1999;Cook et al, 2017). All items on the mindset scale use the term "intelligence" (e.g., "Your intelligence is something about you that you can't change very much").…”

Section: Introductionmentioning

confidence: 99%

Knowledge or Abilities? How Undergraduates Define Intelligence

Limeri

Choe

Harper

et al. 2020

LSE

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Whether students view intelligence as a fixed or malleable trait (i.e., their “mindset”) has significant implications for their responses to failure and academic outcomes. Despite a long history of research on mindset and its growing popularity, recent meta-analyses suggest that mindset does a poor job of predicting academic outcomes for undergraduate populations. Here, we present evidence that these mixed results could be due to ambiguous language on the mindset scale. Specifically, the term “intelligence” is a referent in every item of the mindset scale but is never defined, which could result in differing interpretations and measurement error. Therefore, we conducted an exploratory, qualitative study to characterize how undergraduate students define intelligence and how their definitions may influence how they respond to the mindset scale. We uncovered two distinct ways that undergraduates define intelligence: knowledge and abilities (e.g., ability to learn, solve problems). Additionally, we found that students’ definitions of intelligence can vary across contexts. Finally, we present evidence that students who define intelligence differently also interpret and respond to the items on the mindset scale differently. We discuss implications of these results for the use and interpretation of the mindset scale with undergraduate students.

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Section: Introductionmentioning

confidence: 99%

Knowledge or Abilities? How Undergraduates Define Intelligence

Limeri

Choe

Harper

et al. 2020

LSE

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“…Mayers and Clark base their instructions on the cognitive load theory, which provides design guidelines based on a model of human cognitive architecture. Cook et al validated a cognitive load index in 2017 [77]. The last theory from our search is from Kern’s curriculum development for medical education: a 6-step approach.…”

Section: Discussionmentioning

confidence: 99%

How We Evaluate Postgraduate Medical E-Learning: Systematic Review

Leeuw¹,

Soet²,

Horst³

et al. 2019

JMIR Med Educ

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Background Electronic learning (e-learning) in postgraduate medical education has seen a rapid evolution; however, we tend to evaluate it only on its primary outcome or learning aim, whereas its effectiveness also depends on its instructional design. We believe it is important to have an overview of all the methods currently used to evaluate e-learning design so that the preferred method may be identified and the next steps needed to continue to evaluate postgraduate medical e-learning may be outlined. Objective This study aimed to identify and compare the outcomes and methods used to evaluate postgraduate medical e-learning. Methods We performed a systematic literature review using the Web of Science, PubMed, Education Resources Information Center, and Cumulative Index of Nursing and Allied Health Literature databases. Studies that used postgraduates as participants and evaluated any form of e-learning were included. Studies without any evaluation outcome (eg, just a description of e-learning) were excluded. Results The initial search identified 5973 articles, of which we used 418 for our analysis. The types of studies were trials, prospective cohorts, case reports, and reviews. The primary outcomes of the included studies were knowledge, skills, and attitude. A total of 12 instruments were used to evaluate a specific primary outcome, such as laparoscopic skills or stress related to training. The secondary outcomes mainly evaluated satisfaction, motivation, efficiency, and usefulness. We found 13 e-learning design methods across 19 studies (4% 19/418). The methods evaluated usability, motivational characteristics, and the use of learning styles or were based on instructional design theories, such as Gagne’s instructional design, the Heidelberg inventory, Kern’s curriculum development steps, and a scale based on the cognitive load theory. Finally, 2 instruments attempted to evaluate several aspects of a design, based on the experience of creating e-learning. Conclusions Evaluating the effect of e-learning design is complicated. Given the diversity of e-learning methods, there are many ways to carry out such an evaluation, and probably, many ways to do so correctly. However, the current literature shows us that we have yet to reach any form of consensus about which indicators to evaluate. There is a great need for an evaluation tool that is properly constructed, validated, and tested. This could be a more homogeneous way to compare the effects of e-learning and for the authors of e-learning to continue to improve their product.

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“…Future studies may include additional outcome measures, e.g. motivation score tools [ 16 ] enabling a more comprehensive evaluation of the teaching concept. Taking into account all the arguments raised before and in spite of the unverifiable impact of the applied intervention on learning success, education in biostatistics should perpetuate implementing practical examples in order to give students an understanding of the subject’s relevance.…”

Section: Discussionmentioning

confidence: 99%