Online conferencing: participant preferences for networking and collaboration
Bibliographic record
Abstract
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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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".