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Record W4285406279 · doi:10.5121/ijite.2022.11201

Breakout with Zoom: Mixed-methods Research Examining Preservice Teachers’ Perceptions of Breakout Room Interactions

2022· article· en· W4285406279 on OpenAlexaff
Tim Buttler, Jacob Scheurer

Bibliographic record

VenueInternational Journal on Integrating Technology in Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsBurman University
Fundersnot available
KeywordsBreakoutVideoconferencingZoomPerspective (graphical)PerceptionPsychologyTeacher educationMathematics educationPedagogyComputer scienceMultimediaEngineering

Abstract

fetched live from OpenAlex

Challenges to teacher education due to COVID-19 are widespread. Preservice teachers, in particular, have faced numerous obstacles as a result. While remote teaching became common in higher education, home-based videoconferencing became a standard means of teaching and learning. Regardless of COVID19, virtual technologies use increases within post-secondary education, progressively impacting educational experiences. Therefore, educators must consider the benefits and drawbacks of virtual online education. From a constructivist perspective, we studied preservice teachers’ interactions and perceptions of Zoom’s videoconferencing platform. Specifically, we identified preservice teachers’ interactions and responses to Zoom’s Breakout Rooms. The findings indicate that students built relationships and valued their online interactions. Additionally, males and females valued different aspects of their online interactions.We conclude with recommendations regarding videoconferences in higher education and suggest future research, including empirical studies.

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)0.015
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 score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.478
Teacher spread0.437 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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