Online conferencing: participant preferences for networking and collaboration
Notice bibliographique
Résumé
Conferences and training events have, for many years, been perceived as a primary tool for improving professional knowledge and networking, resulting in improved competence and performance in practice. With the increasing economic and environmental costs associated with long-distance travel, many organisations have implemented environmental policies to limit meetings that involve travel and professionals are required to be more restrained with the number and range of professional development opportunities they engage in. Online professional learning conferences or events have the potential to combine the e-learning models developed for online tertiary education with the needs of participants prevented from attending conferences as a result of time or travel restrictions. \n \nWeb conferencing software enables synchronous, internet-based collaboration and communication and is therefore ideally suited to enabling the interaction between facilitators and participants that is so valued in traditional face-to-face training or conference proceedings. The increasing use of social media platforms and the availability of interactive spaces has also increased opportunities for dispersed participants to collaborate, share and network long after completion of the event. \nThis study was aimed at identifying participant perceptions towards social networking and trends in the use of online and social networking tools provided for use during an online conference, such as Twitter and Facebook. \n \nThree primary sources of data were collected during a recent online conference to achieve these aims. The conference was delivered through the web conferencing system, Blackboard Collaborate, and ran non-stop for 48 hours, with consecutive handovers between partners in Australia, the United Kingdom and Canada. The non-stop nature of the event aimed to mirror a 24-hour digital society and the 21st century learner who wants to be engaged with other learners around the world at all the times. \n \nThe first data source was the recordings from the online conference technology, 'Blackboard Analytics', which included such information as drop-out rates and active participation in live sessions, such as whether or not the participant used the chat function. The second data source was the actual content of the online chat boxes during each session and the frequency and content of any discussions posted via the conference social media environments. Content analysis was used to assess the themes and types of discussions generated in these environments. The final data source was a summative online survey that requested information about participation trends and use of social media during the conference and in general. The data from these three resources, as well as recommendations for encouraging collaboration during online conferences, are presented visually using an info-graphic presentation style.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».