Distance Learners’ Experiences of Silence Online: A Phenomenological Inquiry
Bibliographic record
Abstract
Although learner silence in face-to-face classrooms has been the topic of considerable research interest, relatively little investigation has been done into learners’ experience of silence in distance education. Guided by a phenomenology of practice approach, this study explores the lived experiences of online silence, using interview data gathered from 12 graduate students who were engaged in cohort-based distance learning. Iterative rounds of a whole-part-whole interpretive process were used to identify key themes that emerged regarding the participants’ lived experiences. The findings highlight that silence is a complex, multifaceted phenomenon that was both enacted and received by the participants. Speaking out online was done carefully, sometimes with partial voice and sometimes in fuller voice, sometimes as an obligation and other times with a sense of spontaneity and connection. The six themes that emerged were as follows: (a) learners enact purposeful silence; (b) learners absorb silence from others; (c) learners perceive, and use, silence as demarcation; (d) learners experience silence within voice; (e) learners use deliberate, complex strategies while engaging in online discourse; and (f) learners hear each other in a trusted community. These six themes give new understandings to the experience of online silence. They reflect the multifaceted and nuanced aspects of the phenomenon and have implications for distance education instructors, learners, and curriculum developers.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".