Integrating E-Learning in Saudi Primary Schools: Insights from English Language Learners and Educators
Bibliographic record
Abstract
This study examines the perceptions of primary school students in Saudi Arabia regarding the use of e-learning in English language education. Employing a mixed-methods approach, including a descriptive survey and interviews, data were collected from 100 students and 15 teachers. The findings reveal that learners had a generally positive perception of e-learning, particularly in terms of enjoyment in learning activities (M=3.97, SD=0.67) and a strong preference for digital learning environments (M=3.94, SD=1.29). However, students also reported challenges associated with e-learning (M=3.95, SD=0.74), particularly due to technological unfamiliarity and inadequate internet infrastructure, which hindered the effectiveness of their learning experience. Teachers, on the other hand, expressed difficulties stemming from insufficient training and limited access to technological resources, which inclined them to favor traditional teaching methods over online approaches. The study also identified gaps in technological readiness and information retrieval skills among learners at this educational stage. These findings emphasize the need for targeted interventions to enhance teacher training in e-learning strategies and to improve students’ adaptability to technological advancements. Addressing these challenges could facilitate the smoother integration of online learning into primary education in Saudi Arabia.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".