Twitter as a Potential Lifelong Learning Environment in Higher Education from Saudi Students’ Practices and Perceptions: A Case Study
Bibliographic record
Abstract
One of higher education’s commitments is to provide students with the skills and knowledge needed for continued learning. Blending formal and informal learning approaches is the suggested approach to close the gap between learning in the real world and in classrooms. Social media is viewed as a bridge that can make learning seamless. This qualitative study aims to examine the use of Twitter as an educational environment in which to expand students’ informal lifelong learning. The case study itself discusses female Saudi master’s degree students engaged in learning activities on Twitter for a course at a university in Saudi Arabia. The study aimed to understand their perceptions of Twitter’s integration with course activities and investigated whether the integration of Twitter motivated students toward lifelong learning. Three students from the course participated in in-depth interviews and a content analysis of their tweets. The results indicate that students receive benefits from the integration, such as self-confidence, but also drawbacks, such as lack of information literacy skills. Key results from the content analysis indicated that participants engaged with the course’s Twitter account after the course formally finished. Formal learning hashtags in Twitter led some students to engage in a broader community. Suggestions for pedagogies were to be supported with necessary skills for lifelong learners and for the teacher to continue to engage with learners’ communities informally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".