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Record W7020147024

Learning and thinking about socio-scientific issues: A multi-study examination of the role of epistemic emotions in epistemic cognition

2019· dissertation· en· W7020147024 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCognitionContext (archaeology)Cognitive biasPerception
DOInot available

Abstract

fetched live from OpenAlex

Complex socio-scientific issues surrounding health or the environment, among others, are becoming increasingly controversial as the urgency for action increases.Learning and thinking critically about these issues requires epistemic cognition, i.e., thoughts and beliefs about the nature of knowledge, who holds knowledge, and how knowledge is justified.A review of the literature on epistemic cognition shows that epistemic cognition is a multifaceted phenomenon, including elements such as epistemic beliefs, epistemic aims, epistemic strategies, and epistemic emotions, yet many of these facets remain underexplored.The review also indicates that using more diverse and sophisticated methodologies is instrumental to advancing our understanding of epistemic cognition, as is the investigation of variables that may mediate relations between epistemic cognition and learning outcomes.Epistemic emotions are identified as one promising mediational mechanism that may explain how epistemic cognition relates to important outcomes.On the basis of this review, two manuscripts are proposed that address these issues.The first manuscript reports on two studies where a think-aloud methodology was employed to assess instances of epistemic cognition, epistemic emotions, and self-regulated learning during complex learning.Verbal data were used to examine the types of appraisals that serve as antecedents to epistemic emotions and to explore the immediate consequences of epistemic emotions for selfregulated learning.A path analysis using self-report and verbal data provided support for a model of epistemic emotions as mediators between epistemic cognition and learning processes and outcomes.The second study tested the generalizability of this model by examining the mediational role of epistemic emotions in the relationship between epistemic cognition and critical thinking.Theoretical contributions, implications, limitations, and future directions are discussed. EPISTEMIC COGNITION AND EPISTEMIC EMOTIONSv Résumé Les problématiques socio-scientifiques complexes, liées par exemple à la santé ou à l'environnement, sont devenues aussi urgentes que controversées.Apprendre et avoir une pensée critique au sujet de ces questions nécessite une pensée épistémique adaptée, c'est-à-dire d'avoir une réflexion et d'adopter des croyances productives au sujet de la nature de la connaissance, à savoir qui la détient et comment elle est justifiée.Une recension des écrits sur la cognition épistémique montre que celle-ci est multiple, incluant des éléments tels que les croyances épistémiques, les objectifs épistémiques, les stratégies épistémiques et les émotions épistémiques, tout en indiquant que plusieurs de ces facettes demeurent empiriquement peu explorées.Les conclusions de cette recension indiquent également que pour améliorer notre compréhension de la cognition épistémique, il est essentiel d'utiliser des méthodologies plus diverses et sophistiquées, ainsi que d'investiguer les variables médiatrices susceptibles d'intervenir dans la relation entre la cognition épistémique et l'apprentissage.Les émotions épistémiques sont identifiées comme un mécanisme médiateur prometteur qui pourrait expliquer l'influence de la cognition épistémique sur des résultats importants.Ces lacunes font l'objet de deux articles présentés ici.Le premier article décrit deux études empiriques où la cognition épistémique, les émotions épistémiques et l'apprentissage autorégulé sont mesurés à l'aide d'un protocole de réflexion à voix haute au cours d'un épisode d'apprentissage complexe.Ces données ont été utilisées pour examiner les types d'évaluations cognitives qui servent d'antécédents aux émotions épistémiques, ainsi qu'afin d'explorer les conséquences immédiates des émotions épistémiques sur l'apprentissage autorégulé.Une analyse de trajectoire appuie un modèle prédictif où les émotions épistémiques constituent un médiateur entre la cognition épistémique et les divers processus et résultats d'apprentissage.Le deuxième article présente une deuxième First and foremost, I would like to extend my sincerest gratitude to my supervisor, Dr. Krista Muis, for her mentorship and encouragement over the last seven years.At each stage of this process, you have guided me with expertise, encouraged me through challenges, and celebrated every achievement.You have introduced me to a network of excellent collaborators, provided financial support and access to key resources, and have invested generous amounts of time and effort into my success.You have helped me grow into the researcher I am today and for that, I am forever grateful.I would also like to extend my thanks to Dr. Alenoush Saroyan for her guidance over the years, from my comprehensive exam to this dissertation.The time and attention you have dedicated is greatly appreciated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.040
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0090.007
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.310
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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