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Record W4401173024 · doi:10.55016/ojs/ajer.v70i2.78831

Perceptions and Difficulties of Distance Learning Among Beginning Teachers and Kindergarteners During the Covid-19 Pandemic Period

2024· article· en· W4401173024 on OpenAlexvenueno aff
Raed Zedan

Bibliographic record

VenueAlberta Journal of Educational Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationContext (archaeology)PsychologyPerceptionCoronavirus disease 2019 (COVID-19)PedagogyRealmPandemicMathematics educationPolitical science

Abstract

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Exploring the perceptions and challenges faced by beginning teachers and kindergarten teachers in the realm of distance learning reveals critical insights into the evolving landscape of early education in a digital age. The present study explored the challenges and difficulties experienced by beginning teachers and kindergarteners in the context of distance learning. Qualitative research was conducted through semi-structured interviews involving 254 novice teachers and kindergarten educators. The findings revealed a range of difficulties new teachers face in the distance learning environment. These included challenges in conveying educational content, issues with Wi-Fi and internet connectivity, a lack of resources and technological tools, difficulties in engaging students in Zoom meetings, poor communication between teachers and students, insufficient preparation and training for distance teaching, inadequate digital skills, and a lack of experience among parents with the new method of learning. Understanding the challenges and difficulties that novice teachers encounter, particularly during their initial professional years and in exceptional circumstances like the COVID-19 pandemic, is crucial. This understanding not only aids in addressing and surmounting these challenges but also contributes to the enhancement and development of current distance teaching models. Such improvements should consider the specific needs and challenges of new teachers. L'étude des perceptions et des difficultés rencontrées par les enseignants débutants et les enseignants de maternelle dans le domaine de l'enseignement à distance révèle des informations essentielles sur l'évolution du paysage de l'éducation préscolaire à l'ère numérique. La présente étude a exploré les défis et les difficultés rencontrés par les enseignants débutants et les enseignants de maternelle dans le contexte de l'enseignement à distance. La recherche qualitative a été menée par le biais d'entrevues semi-structurées auxquelles ont participé 254 enseignants débutants et éducateurs de maternelle. Les résultats ont révélé une série de difficultés auxquelles les nouveaux enseignants sont confrontés dans le milieu de l'enseignement à distance. Il s'agit notamment des difficultés à transmettre le contenu éducatif, des problèmes de connexion Wi-Fi et Internet, du manque de ressources et d'outils technologiques, des difficultés à faire participer les élèves aux réunions Zoom, de la mauvaise communication entre les enseignants et les élèves, du manque de préparation et de formation à l'enseignement à distance, des compétences numériques inadéquates et du manque d'expérience des parents en ce qui concerne cette nouvelle méthode d'apprentissage. Il est essentiel de comprendre les défis et les difficultés que rencontrent les enseignants débutants, en particulier au cours de leurs premières années professionnelles et dans des circonstances exceptionnelles telles que la pandémie de COVID-19. Cette compréhension permet non seulement d'aborder et de surmonter ces défis, mais aussi de contribuer à l'amélioration et au développement des modèles actuels d'enseignement à distance. Ces améliorations devraient prendre en compte les besoins et les défis spécifiques des nouveaux enseignants.

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)0.007
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 score0.104
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.060
GPT teacher head0.402
Teacher spread0.342 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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