Virtual Communities in an Online English Language Learning Forum
Bibliographic record
Abstract
This study aims to shed light on the prospect of MyLinE (Malaysia Online Resources for Learning in English) as a platform for development of virtual communities of practice. Students discussion threads (DTs) in Lounge were explored to answer the following questions: (1) Are the discussion threads task-oriented? What are the tasks?; (2) What social structure patterns can be found from the discussion threads?; and (3) What are the shared resources that emerge from the interaction between participants? This study adopted a descriptive approach of document analysis whereby its main goal is to provide a detailed description of the patterns that emerged from the data. Specifically, interactional analysis was conducted to provide answers on the emergence of community. In addition, depth thread measure of 6-levels was also adopted to determine the quality of interaction. The findings of this study accentuated three features: (1) task-orientedness, (2) social structure patterns and (3) shared resources. Based on the interactional analysis done on DTs, six speech acts were also identified which indicated conversational exchanges between the participants. From the data, two types of discussion patterns were identified. The first was an intensive discussion that took place in a short period, and the second pattern identified was a discussion that stretched over a long period with long gaps between posts. From the data, participants were found to share three resources, which are (1) a shared idea of politeness, (2) a shared manner of expressing opinion and (3) shared manner of supporting opinion by using personal experience.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".