The Use of Social Media Viewed Through Some Language Learning Assumptions Lens
Bibliographic record
Abstract
To address whether social media helps the English language learning abilities of the learners, this paper probes into the strengths and weaknesses of social media as a platform. There is rarely complete agreement about the best way or the right way to learn the English language. Consensus usually remains difficult about social media, which is no longer a mere communication tool. If social media is considered to be an effective learning tool suitable for learners, then several assumptions about the view of learning need to be taken into account. Hence, critiquing the learning assumptions of Anderson et al. (1996), this paper aims to draw educators' attention to how they can make learners aware of maximizing the benefits of social media and take recourse to this learning tool. This study explores the perspectives of (N=40) undergraduate EFL students at a public university in Saudi Arabia about using social media to learn the English language. A questionnaire consisting of 26 items was prepared on a 5-point Likert Scale. After collecting the data, it was analyzed using SPSS (Version 20.0). Based on the findings, the paper concludes with some recommendations on how social media can be used to enhance students' performance in learning English.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".