A case study of online collaborative learning for union staff in developing countries
Bibliographic record
Abstract
Online collaborative learning (OCL) is used by many universities to provide education to geographically-dispersed groups of students who can participate at times and locations of their choosing.However, despite its potential for expanding labour education and building new knowledge, OCL is not being used by the international labour movement.This dissertation investigates the use of OCL by the staff of unions in developing countries.The focus is on exploring if OCL can be effective and viable for these staff members.Effectiveness is related to evidence of learning, perception of learning, and sense of community.Viability is related to the technological and financial capability to participate in online collaborative learning.Previous investigations concluded that online learning for unions needs to be collaborative, promote community, be based on constructivist learning principles, provide links to leaming in the workplace, and possibly grant a certificate recognized as valuable by the participants.Investigators who studied early online labour education projects emphasized the need to determine the process by which groups of unionists learn online.A case study was conducted.An online course involving 33 union staff members based in 24 developing countries was studied using a mixed-mode research strategy.Both quantitative and qualitative research methods were used.A theory and research methods related to online collaborative leaming were used to analyse the process of learning in the course.A questionnaire on the development of community amongst the participants was applied.Transcript analysis of messages in the course's computer conferences was conducted.Findings include: OCL can be successfully employed for the education of union staff in developing countries; the collaborative creation of a knowledge artefact, such as a document, which has a public life outside of the online course provides a crucial link to activities in the workplace; the task which is set for the group significantly affects the collaborative discourse process; online collaborative learning can build a strong sense of community amongst participants; and a credential, even if it is not accredited by a university or college, is a significant motivator.The international labour movement could use online collaborative learning to provide educational opportunities globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".