Enriching Higher Education with Social Media: Development and Evaluation of a Social Media Toolkit
Bibliographic record
Abstract
<p class="1">While ubiquitous in everyday use, in reality, social media usage within higher education teaching has expanded quite slowly. Analysis of social media usage of students and instructors for teaching, learning, and research purposes across four countries (Russia, Turkey, Germany, and Switzerland) showed that many higher education instructors actively use social media for private purposes. However, although they understand that their students also use it for learning purposes, and instructors sense the potential of social media in teaching, they mostly refrain from doing so due to various barriers. In response, an openly accessible trilingual <em>Social Media Toolkit </em>was developed which analyzes the teaching scenario with several questions, before suggesting, based on an algorithm, the best matching class of social media, complete with advice on how to use it for teaching purposes. This paper explains the rationale behind the toolkit, its development process, and examines instructors’ perceptions towards it.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".