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Record W2521169251 · doi:10.33524/cjar.v17i2.261

AN INQUIRY INTO FLIPPED LEARNING IN FOURTH GRADE MATH INSTRUCTION

2016· article· en· W2521169251 on OpenAlexvenueno aff
Teresa D’addato, Libbi R. Miller

Bibliographic record

VenueThe Canadian Journal of Action Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorMathematics educationFlipped classroomAction researchFlipped learningPsychologyClass (philosophy)DisadvantagedPedagogyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

The objective of this action research project was to better understand the impact of flipped learning on fourth grade math students in a socioeconomically disadvantaged setting. A flipped instructional model was implemented with the group of students enrolled in the researcher’s class. Data was collected in the form of classroom observations, teacher reflections and surveys from both parents and students. Findings indicated that a flipped learning methodology shifted the role of the teacher to that of a classroom facilitator. Flipped learning also created an engaging instructional environment, which afforded students the opportunity to experience a greater sense a responsibility over their learning process. While parents responded to flipped learning in mixed ways, they overall reported positive changes in their children as a result of the shift in instructional methods. Conclusions and implications for practice are discussed.

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)0.013
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0050.004
Open science0.0030.006
Research integrity0.0020.003
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 head0.339
GPT teacher head0.533
Teacher spread0.194 · 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 designQualitative
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

Citations28
Published2016
Admission routes1
Has abstractyes

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