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Record W3134140152 · doi:10.5539/ass.v17n3p1

Level of Satisfaction of the Practical Studies Teachers with the Distance Education Experience in Kuwait in Light of the CoronaVirus COVID-19

2021· article· en· W3134140152 on OpenAlexvenueno aff
Adnan Said Ahmad AL-Husaini, Abduallah Salem Azou’bi

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationCoronavirus disease 2019 (COVID-19)PsychologyPandemicReliability (semiconductor)Sample (material)Medical education2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ValidityCoronavirusMathematics educationMedicineClinical psychologyPsychometricsInternal medicine

Abstract

fetched live from OpenAlex

The study aimed to identify the level of satisfaction of the practical studies’ teachers with the distance education experience in Kuwait in light of the coronavirus covid-19 pandemic. To achieve that, the researchers used the survey method. It was applied on a sample of (120) male and female practical studies’ teachers in Mubarak Al-Kabeer governorate in the first semester of the academic year 2020/2021. The researchers built a questionnaire consisting of (20) items to measure the level of their satisfaction with the distance learning experience, and verifying its validity and reliability. The results of the study showed that the level of satisfaction of the practical studies’ teachers with the distance education experience in Kuwait was moderate. The results also revealed that there are no statistically significant differences at (α = 0.05) in the level of satisfaction of the practical studies’ teachers due to the gender variable. While there are statistically significant differences at (α = 0.05) in the level of satisfaction due to the variable of educational experience and in favor of who hold an experience with less than (5) years.

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)0.001
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 score0.350
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.100
GPT teacher head0.382
Teacher spread0.282 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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