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Record W2507243356 · doi:10.22329/celt.v9i0.4426

Making Students' Metacognitive Knowledge Visible Through Reflective Writing in a Mathematics-for-Teachers Course

2016· article· en· W2507243356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsMetacognitionMathematics educationPsychologyReflection (computer programming)Knowledge levelPedagogyCognitionComputer science

Abstract

fetched live from OpenAlex

Metacognition directly contributes to learning, performance, and beliefs about the self as a learner. This paper describes the rationale, implementation, and assessment of a weekly online reflection activity based on instructor prompts designed for post-secondary students who aspire to be elementary school teachers. Our study defines four categories of metacognitive knowledge that speak to the specific goals of the course and the characteristics of the students. Using these categories, 71 students’ written responses to four reflection prompts from three course offerings were coded, and their effects were examined in terms of types of metacognitive knowledge demonstrated. Our results not only confirm that students were engaged in metacognition through the reflection activity but also show that students exhibited different categories of metacognitive knowledge in relation to the varying emphases of the prompts.

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.

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)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.079
GPT teacher head0.470
Teacher spread0.392 · 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