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Record W3184897174 · doi:10.5430/ijhe.v10n7p62

Engaging Student Teachers in An Online Teaching Pedagogies Module during COVID-19

2021· article· en· W3184897174 on OpenAlexvenueno aff
Peter Tiernan, Jane O’Kelly, Justin Rami

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Mathematics educationOnline learningOnline teachingPsychologyProcess (computing)Teaching methodComputer scienceMedical educationPedagogyMultimediaMedicine

Abstract

fetched live from OpenAlex

COVID 19 represented a major challenge for many educators, with teachers needing to pivot rapidly to using online learning tools in order to stay connected with their students. This was particularly relevant for teachers in the process of completing their Initial Teacher Education (ITE), whose programmes of study did not include online teaching components. The objective of this study was to develop and evaluate a module for teaching in online and blended learning environments for 244 post-primary teachers in ITE. This study begins by examining the impact of COVID-19 and the resulting pivot to online learning, this includes an overview of the challenges associated with online teaching. Next, the authors explain the module in detail, outlining the tools, strategies and activities provided for student teachers. This included peer-evaluated online micro-teaching components - which formed a major part of the learning. Data collection involved a questionnaire which gathered student teachers’ perceptions of the module and its approaches, the impact it had on their ability to teach during the COVID-19 pandemic, and the knowledge and skills useful for future practice. Findings suggested that the implementation of the developed module was successful in preparing student teachers to teach online, providing them with the tools and confidence necessary for success. Improvements suggested the development of differentiated pathways for student teachers who are more experienced with online teaching.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.047
GPT teacher head0.423
Teacher spread0.376 · 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 designObservational
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

Citations6
Published2021
Admission routes1
Has abstractyes

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