Examining the Use of Twitter in Online Classes: Can Twitter Improve Interaction and Engagement?
Bibliographic record
Abstract
Student engagement promotes communication and knowledge acquisition, a concept that is challenged in the online environment as few opportunities exist to physically connect instructors and learners. Limited research suggests that social media is a tool that can positively impact student engagement in the online classroom, which is especially relevant in the case of the COVID-19 pandemic and learning formats transitioning online. Specifically, Twitter, a favoured format for sharing news, entertainment, and professional networking, may provide a platform and an opportunity for engagement between students and the instructor outside of the traditional, formal classroom setting. This research explores how postsecondary students enrolled in two introductory online self-directed asynchronous courses used social media tools for personal, professional, and academic purposes and how Twitter, as a course evaluation requirement, contributed to interaction and engagement. Relying on 104 pre- and 34 post-semester surveys, our analysis revealed that while Twitter was not used as widely as other social media platforms, a notable proportion of students shared positive perceptions about Twitter’s use. Further analysis revealed some polarizing results with recommendations for successfully implementing Twitter in online learning.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".