Investigating the Effect and Students' Perceptions of Using Instagram as a Writing Teaching Tool in Saudi EFL Classrooms
Bibliographic record
Abstract
Social media platforms have gained exceptional popularity, especially in the last ten years, and have subsequently become important to current human lifestyle. Instagram (IG) is among the most popular social media platforms, and is used by millions of people every day, especially young adults. Given this significance, social media platforms have been used for educational purposes, too. Thus, this study aims at exploring the perceptions of female high school students about the potential of utilizing IG as a writing teaching tool. Using the Mixed Method Design, thirty-five high school female students learning English as a foreign language were included in the survey with the same students taking part in the experimental part of the study designed to explore the impact of IG on students’ language learning processes. Seven students from the same experimental group were also interviewed for their opinions about IG use. After conducting quantitative and qualitative analyses, the findings show that IG is the most frequently used social media platform among the participants and that they favor it for educational and language learning purposes, especially as a writing teaching tool. Additionally, it was found that IG had a positive impact on the students’ language learning based on their achievement scores. Thus, it is concluded that it can be used to enhance the learning of English supplementary to formal teaching by exposing the students to writing the English language while they are using this platform as part of their everyday practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".