The Use of Social Media in E-Learning: A Metasynthesis
Bibliographic record
Abstract
The adoption of social media in e-learning signals the end of distance education as we know it in higher education. However, it appears to have very little impact on the way in which open and distance learning (ODL) institutions are functioning. Earlier research suggests that a significant part of the explanation for the slow uptake of social media in e-learning lies outside of conventional factors attributed to distance learning reforms. This research used the conceptual framework for online collaborative learning (OCL) in higher education. Social media such as blogs, wikis, Skype or Google Hangout, Facebook; and even mobile apps, such as WhatsApp; could facilitate deep learning and the creation of knowledge in e-learning at higher educational institutions. This metasynthesis is an interpretative integration of peer-reviewed qualitative research findings on social media in e-learning. It includes a synthesis of data, research methods, and theories used to investigate social media in e-learning. Seven themes emerged from the data which have been recrafted into a framework for social media in e-learning as the final product. The proposed framework could be useful to instructional designers and academics who are interested in using modern learning theories and want to adopt social media in e-learning in higher education as a deep learning strategy.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".