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Record W3081278380 · doi:10.5430/ijhe.v9n6p44

The Impact of E-Learning Strategy on Students' Academic Achievement. Case Study: Al- Quds Open University.

2020· article· en· W3081278380 on OpenAlexvenueno aff
Yousef Abdel Jawad, Basem Mohammad Shalash

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsArabicMathematics educationStatistical analysisAcademic yearAcademic achievementOpen universityPsychologyMedical educationDistance educationMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

The study examines the effect of e-learning on the students’ academic performance at Al-Quds Open University. 382 students have been randomly selected from the university's official records where a statistical analysis of the study variables was performed using the Statistical Package of Social Sciences program (SPSS.20). The study hypotheses were tested by Paired Samples T-tests.The study has revealed that there are statistically significant differences in the students’ academic performance over the implementation of the e-learning strategy with regard to the level of the vast majority of students, gender, educational program, as well as their academic level.The study recommends the necessity of enhancing the e-learning strategy in the higher education institutions to foster the students’ academic performance taking into account the specificity of some learning programs such as the Arabic language programs and the social studies.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.032
GPT teacher head0.376
Teacher spread0.344 · 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 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

Citations36
Published2020
Admission routes1
Has abstractyes

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