Twitter-Mediated Knowledge Brokering in STEM Education Reform: A Social Network Analysis of Key Knowledge Actors in a Mid-Sized Ontario School District During the First Year of Reform
Notice bibliographique
Résumé
This thesis examines the influence of knowledge actors in a mid-sized Ontario school district's Twitter network, with a focus on STEM-related discourse during 2019 - the year of a major Ministry reform announcement. Using social network analysis (SNA), this research investigates how different types of actors - policy, research, and practice - facilitate knowledge exchange through three analytical lenses: (1) centrality measures to identify the most influential actors, (2) strength of ties to assess cohesion within and between subgroups, and (3) structural holes to explore how brokers bridge disconnected parts of the network. Centrality measures were used to determine the top ten ranked actors, highlighting who the main categories of knowledge actors were and what role they held inside or outside the district. The combination of centrality measures allowed for the exploration of the multifaceted ways in which they exerted influence within the district's Twitter network. Two knowledge actors, a STEM teacher and a science-focused school, with high outdegree and betweenness centrality played key roles in both disseminating STEM knowledge and brokering connections across otherwise disconnected groups. These findings highlight the multifaceted influence of certain individuals, such as teachers, schools, and consultants, who acted as both visible communicators and strategic connectors within the district's Twitter network. These findings indicate that the practice subgroup had the strongest ties, facilitating a higher volume of knowledge exchange within their group. In contrast, policy actors shared a significantly lower number of tweets or mentions amongst themselves, recording only eight interactions throughout the year. The strength of ties within the science subgroup facilitated more frequent knowledge exchange about science than in any other subject matter group during the first year of the reform. In contrast, the engineering subgroup had the weakest ties, sharing the least frequently, including only one exchange with the mathematics subgroup. The analysis of structural holes revealed that a small number of knowledge brokers played a key role in bridging otherwise disconnected clusters of knowledge actors about STEM, enabling information to span boundaries across the network. This thesis contributes to understanding online educational networks at a local level, providing insight into the influence dynamics within educational policy discourse on Twitter.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».