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Record W4398162129 · doi:10.1007/s44282-024-00053-9

Emergency remote teaching amid global distress: how did teacher educators respond, cope, and plan for recovery?

2024· article· en· W4398162129 on OpenAlexaff
Kenneth Gyamerah, Daniel Asamoah, David Baidoo-Anu, Eric Atta Quainoo, Ernest Yaw Amoateng, Ernest Ofori Sasu

Bibliographic record

VenueDiscover Global Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsConestoga CollegeQueen's University
Fundersnot available
KeywordsPlan (archaeology)DistressLesson planPsychologyMedical educationMathematics educationMedicineHistoryPsychotherapistArchaeology

Abstract

fetched live from OpenAlex

Abstract This study explored the emergency remote teaching experiences of Ghanaian teacher educators during COVID-19. The study employed a basic qualitative interpretive approach and purposively interviewed 25 teacher educators from 15 teacher training institutions. Teacher educators in this study reported that emergency remote teaching (ERT) was a learning opportunity and professional capacity-building experience for them to engage in contemporary pedagogical practices. Teachers also indicated that synchronous and asynchronous remote teaching was helpful for their students, as it provided students with the opportunity to engage in self-paced learning due to their access to learning resources at any time. ERT promoted peer teaching, team teaching, and effective collaboration among teacher educators. However, with little preparation and training for remote teaching and learning, both teachers and students struggled with the remote teaching and learning process. A myriad of challenges were identified including the unsuitability and unfamiliarity of online teaching and learning platforms, a high rate of absenteeism and low student engagement, a lack of parental and school support, and inadequate technological resources. The study revealed that mathematics and science teachers needed advanced technological resources to support student learning. Implications for educational policy and 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 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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.342
Teacher spread0.325 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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