Lurking and L2 Learners on a Facebook Group: The Voices of the Invisibles
Bibliographic record
Abstract
This qualitative research investigates the practice of lurking among English as a second language (ESL) learners in a Facebook group discussion. Lurking is a term used to describe the activity of following and observing any online discussions or activities without contributing to the discussions. Lurkers are often accused of being invisible and passive participants. Facebook groups with international members usually uses English as the medium of communication in their group discussions. It is a norm for L2 learners to lurk in the group. These L2 learners often do not have the required English language proficiency and the confidence to participate actively in the group they join. This study explores lurking behaviours of three participants in learning English in informal Facebook contexts. This study uses a qualitative case study. The study involved three L2 learners who were university students and members of a Facebook group. The study involved online observation and semi-structured interviews with these three participants. Their Facebook accounts and a common Facebook group were observed for 14 weeks, and after the online observation, the participants were interviewed. Data collected from online observation and semi-structured interviewed were analysed and managed using Atlas.ti 7. The study reveals five emerging themes such as that lurkers have poor online communication skills, lack of confidence, learning by lurking, lack of a sense of belonging and lurking is the norm of Facebook groups.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".