Referee report. For: Tweets from the forest: using Twitter to increase student engagement in an undergraduate field biology course [v1; approved 1, http://f1000r.es/56w]
Bibliographic record
Abstract
Twitter is a cold medium that allows users to deliver content-rich but small packets of information to other users, and provides an opportunity for active and collaborative communication. In an education setting, this social media tool has potential to increase active learning opportunities, and increase student engagement with course content. The effects of Twitter on learning dynamics was tested in a field biology course offered by a large Canadian University: 29 students agreed to take part in the Twitter project and quantitative and qualitative data were collected, including survey data from 18 students. Students published 200% more public Tweets than what was required, and interacted frequently with the instructor and teaching assistant, their peers, and users external to the course. Almost 80% of students stated that Twitter increased opportunities for among-group communication, and 94% of students felt this kind of collaborative communication was beneficial to their learning. Although students did not think they would use Twitter after the course was over, 77% of the students still felt it was a good learning tool, and 67% of students felt Twitter had a positive impact on how they engaged with course content. These results suggest social media tools such as Twitter can help achieve active and collaborative learning in higher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.593 | 0.301 |
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".