Using Social Learning Networks (SLNs) in Higher Education: Edmodo Through the Lenses of Academics
Bibliographic record
Abstract
With its total number of users (around 62 million) throughout the world, it is important to determine the views of academics who use Edmodo (the leading SLN. In this respect in the first part of this two-part research, the purpose was to examine academics’ (n=50) use of technology and social networks. As for the purpose of the second part, it was to determine the views of 12 academics—selected from the academics participating in the first part—who had experience in Edmodo about the basic features of Edmodo and about its use in education. In the study carried out with the mixed method, the qualitative and quantitative data were collected with an online questionnaire. The findings obtained were interpreted within the framework of cooperative learning and the theories of “Diffusion of Innovations” and “Uses and Gratifications,” and the related themes were formed. As a result, the academics with experience in Edmodo reported their views about the benefits of use of the Edmodo in education. Regarding the differences between Edmodo and social networks, the results suggested that the former was used completely for educational purposes and that it did not involve any unnecessary components.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".