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The Power of Metaphor in Bringing Clarity for Learners in Learner-Centered Design

2016· book-chapter· en· W2514032262 on OpenAlexaff
John A. Ewing, Doug Reid

Bibliographic record

VenueAdvances in higher education and professional development book series · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsCLARITYMetaphorSocial constructivismConstructivism (international relations)Constructivist teaching methodsPedagogyPsychologyMathematics educationFace (sociological concept)Teaching methodSociology

Abstract

fetched live from OpenAlex

The study focuses on guiding students through an exploration of social constructivism model as it relates to the roles of instructor and learner. It explores the use of a metaphor, the dot, to demonstrate that metaphors can support deeper understanding of difficult concepts inherent in learner-centered and constructivist pedagogies. This research was conducted to ascertain whether metaphors provide common reference points for learners that can be used to build and test new assumptions of knowledge. Additionally, the study highlights challenges that learner-centered pedagogy face when identifying preconceived constructs and moving towards the adoption of new thoughts, perspectives, and reasoning. In theory, this study identified the continuing role that metaphors play in the learning theory and how the literature can be explored further. In practice, the study identified student-centered activities, which include the learner as a contributor to knowledge, learning in a community of learners, and empowering the learner to change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.877
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.364
Teacher spread0.309 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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