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Record W2787228099 · doi:10.21083/nrsc.v0i11.4016

Flipped Classroom, Flipped Teaching and Flipped Learning in the Foreign/Second Language Post–Secondary Classroom

2018· article· en· W2787228099 on OpenAlexaffvenue
Denise Mohan

Bibliographic record

VenueNouvelle Revue Synergies Canada · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFlipped classroomFlipped learningBlended learningTerminologyContext (archaeology)Mathematics educationForeign languageActive learning (machine learning)PedagogyPsychologyEducational technologyComputer scienceLinguisticsArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

The term “flipped classroom”, coined in 2007, represents a pedagogy aligned with long-established principles of student-centered learning. Over the last two decades, the flipped classroom has been adopted by instructors across a range of disciplines, from primary to post-secondary settings. During its development, the characteristics of the flipped classroom have evolved, as have the terminology used to reference it, leading practitioners and researchers to now address it as flipped classroom, flipped teaching or flipped learning. In this article, these terms will be used interchangeably. The article will examine the foundations of flipped learning, discuss its roots in learner-centered pedagogy, trace its development over the last two decades, profile its characteristics, and examine the feedback on its effectiveness and challenges as provided by flipped learning instructors and researchers. An attempt will be made to answer the following four questions. Where does flipped learning fit on the continuum of learner-centered pedagogies? How have educators responded, both positively and negatively, to the flipped learning/teaching approach? How has flipped learning been implemented in the foreign/second language (FL/L2) classroom? What are some considerations and recommendations for FL/L2 instructors contemplating using this approach in the FL/L2 post-secondary context? Finally, some suggestions will be made regarding next steps in research on flipped learning.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.013
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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 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

Citations27
Published2018
Admission routes2
Has abstractyes

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Same venueNouvelle Revue Synergies CanadaSame topicSecond Language Learning and TeachingFrench-language works237,207