Exploring the Use of #Hashtags as an Easy Entry Solution to Enhance Online Discussions
Bibliographic record
Abstract
Abstract: Growing interest in online learning has instructors looking for new ways to engage learners in asynchronous discussions. Building on experiences with hashtag use to connect on social media platforms, the purpose of this study is to investigate the contexts of hashtag use and its associated impact on learner engagement in asynchronous online discussions. In more detail, this mixed-methods case study will answer the following questions: 1) How and in what context do students use hashtags in online discussions? and 2) In what ways, if any, does the use of hashtags promote engagement in an online community? Findings suggest that while classifying and organizing course information was a strong motivator for tagging posts, hashtags were also used to connect to others in the learning community, express opinions, and encourage knowledge building. The results from our study contribute to a better understanding of engagement with and through hashtag use. Keywords: engagement, social media, online learning, tagging Résumé: L'intérêt croissant pour l'apprentissage en ligne incite les instructeurs à rechercher de nouvelles façons d'engager les apprenants dans des discussions asynchrones. S'appuyant sur les expériences d'utilisation du hashtag pour se connecter sur les plateformes de médias sociaux, le but de cette étude est d'étudier les contextes d'utilisation du hashtag et son impact sur l'engagement des apprenants dans les discussions en ligne asynchrones. Plus en détail, cette étude de cas à méthodes mixtes répondra aux questions suivantes: (1) Comment et dans quel contexte les élèves utilisent-ils les hashtags dans les discussions en ligne? (2) De quelle manière, le cas échéant, l'utilisation de hashtags favorise-t-elle l'engagement dans une communauté en ligne? Les résultats suggèrent que si la classification et l'organisation des informations sur les cours ont été un puissant facteur de motivation pour marquer les publications, les hashtags ont également été utilisés pour se connecter à d'autres membres de la communauté d'apprentissage, exprimer des opinions et encourager le renforcement des connaissances. Les résultats de notre étude contribuent à une meilleure compréhension de l'engagement avec et via l'utilisation du hashtag. Mots-clés: engagement, médias sociaux, apprentissage en ligne, marquage
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".