Slack: Adopting Social-Networking Platforms for Active Learning
Bibliographic record
Abstract
ABSTRACT Online learning in postsecondary institutions has increased dramatically across the United States and Canada. Although research demonstrates the benefits of online learning for student success, instructors face challenges in facilitating communication, delivering course content, and navigating outdated and cumbersome technologies. The authors examine the use of a free third-party platform called Slack as a tool to facilitate better communication among students and faculty, enable the delivery of diverse and dynamic course content, and reach students in an online course that supports both independent and collaborative learning. The authors present a case study of Slack’s use in an online second-year environmental politics course taught at a large Canadian public university. There is a significant and growing literature on how to best engage students in online learning, including active and social learning models as promising approaches to digital teaching. The authors argue that using collaborative social technologies such as Slack—which both replicates and integrates the online and social-media environments that students already inhabit—can assist faculty in meeting their pedagogical goals online. The article documents the instructors’ experience in managing discussion and involving students in their online learning through active learning exercises. Best practices are examined.
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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 | 0.003 | 0.005 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".