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Record W4379798790 · doi:10.38140/pie.v41i1.6176

Perceptions of pre-service teachers on breakout room micro-teaching with Zoom

2023· article· en· W4379798790 on OpenAlexaff
Timothy Buttler, Jacob Scheurer

Bibliographic record

VenuePerspectives in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsBurman University
Fundersnot available
KeywordsMicroteachingBreakoutZoomMathematics educationPsychologyTeacher educationMedical educationPedagogyEngineeringMedicine

Abstract

fetched live from OpenAlex

Due to the emergence of Covid-19, many educators moved from a face-to-face teaching environment to an online microteaching setting using Zoom. This study explores pre-service teachers’ perspectives on microteaching within Zoom’s breakout rooms. The authors approached this study from a positivist-postpositivist perspective employing a mixed-methods methodology. The exploratory sequential mixed-method design employed here combines qualitative and quantitative data. Analysis entailed open coding of data from Zoom recordings and statistical analysis of a post-course survey. Convenience sampling of pre-service teachers (PSTs) from a teacher education teaching method course provided the data sets. Findings indicate that microteaching activities within breakout rooms facilitated an environment where pre-service teachers engaged and conversed with peers while developing teaching skills. PSTs valued breakout room interactions, though males and females valued different aspects. Finally, although the findings suggest that microteaching in Zoom’s breakout rooms is effective, the findings indicate that the pre-service teachers desired a return to the classroom. This research extends previous research on online microteaching student experiences by providing recommendations regarding microteaching via video conferences.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.369
Teacher spread0.353 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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