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Record W2244671687 · doi:10.21810/sfuer.v6i.363

A Critical Reflection of Collaborative Inquiry: To what extent is collaborative learning beneficial in my classroom?

2013· article· en· W2244671687 on OpenAlexvenueno aff
Bianca Lavorata

Bibliographic record

VenueSFU Educational Review · 2013
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationCollaborative learningActive learning (machine learning)PedagogyRhetoricCooperative learningPsychologyTeaching methodValue (mathematics)Computer science

Abstract

fetched live from OpenAlex

Twenty years ago, I was a Grade 6 student in a Grade 6/7 classroom. I remember learning material through direct teacher instruction. The structure was quite clear-cut; the teacher transmitted information and the students listened and absorbed the material. Tests were distributed, graded, and then new content would be introduced. Students were seated in rows, sometimes according to alphabetical order, which inhibited peer intermingling and interaction. This type of traditional teaching still exists in many classrooms despite educational advances that highlight the importance of collaborative student-centred learning rather than a teacher-centred classroom. I strongly maintain that “a major value of collaboration, the reason why it is so praised in our rhetoric, is that we can do more and better work collaboratively than we can alone” (Johnston-Parsons, M., 2010, p. 289). Student centred learning may be referred to as learning that “has student responsibility and activity at its heart, in contrast to the stronger emphasis on teacher-control and the coverage of academic content” found in much traditional teaching classrooms (Cannon, R., Ingleton, C., Kiley, M., & Rogers, T., 2000, pg.3). I further extend this definition and place emphasis on student centred learning as a classroom in which students play an active role in their learning, as oppose to a passive role in a teacher-centered learning environment.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.487
Teacher spread0.417 · 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.

Study designNot applicable
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

Citations0
Published2013
Admission routes1
Has abstractyes

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