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Record W3160262504 · doi:10.19173/irrodl.v22i2.5154

Parents’ Perceptions of Their Children’s Experiences With Distance Learning During the COVID-19 Pandemic

2021· article· en· W3160262504 on OpenAlexvenueno aff
Diala A. Hamaidi, Yousef M. Arouri, Rana K. Noufal, Islam T. Aldrou’

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCommunication Studies and Media
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicDistance educationPerceptionPsychologyCoronavirus disease 2019 (COVID-19)Sample (material)Descriptive statisticsMedical educationValidityReliability (semiconductor)Descriptive researchMathematics educationMedicineDevelopmental psychologyPsychometricsDiseaseMathematicsStatisticsInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This study aimed to investigate the perceptions of primary and secondary students’ parents in Jordan toward the distance learning process implemented in light of the coronavirus disease (COVID-19) pandemic. To achieve the study objectives, the researchers used the descriptive survey method to collect and analyze data and interpret the results. After developing the study instrument (questionnaire) and ensuring its validity and reliability, it was distributed to a selected sample, consisting of 470 parents, by random cluster method during the second semester of the 2019–2020 academic year. The study results show that primary and secondary students’ parents were moderately satisfied with the distance learning process implemented in light of the COVID-19 pandemic. In addition, the results reveal statistically significant differences in the parents’ perceptions attributed to the variables of the child’s grade, in favor of grades 5–7; teacher’s gender, in favor of female teachers; and school type, in favor of private schools.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.480
Teacher spread0.350 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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