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Record W2942760597 · doi:10.5539/ies.v12n5p99

Jordanian Students’ Barriers of Utilizing Online Learning: A Survey Study

2019· article· en· W2942760597 on OpenAlexvenueno aff
Yousef Aljaraideh, Khaleel Al Bataineh

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSimple random samplePsychologyReliability (semiconductor)Medical educationSample (material)Mathematics educationOnline learningPerspective (graphical)ValidityApplied psychologyComputer scienceMultimediaPsychometricsEnvironmental healthDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

This study aims at finding out the main barriers preventing students in Jordan from using online learning from their perspective. To achieve this objective, a questionnaire was developed and the validity and reliability of the questionnaire were checked. A simple random sampling was used forming a sample of 400 students. The results indicate that the online learning infrastructure is an immense barrier that obstructs the utilization of online learning in Jerash University. Also, there are statistically significant differences in the barriers that faced students while they are using online learning based on gender and studying year variables in favor of female and students of first year respectively. Moreover, the results of the study revealed that there is an interaction between gender and teaching year variables. Finally, in light of the results, the study recommended that additional efforts from decision makers and teachers should be taken into consideration for the sake of online learning process improvement.

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)0.004
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 score0.462
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.076
GPT teacher head0.472
Teacher spread0.395 · 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

Citations64
Published2019
Admission routes1
Has abstractyes

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