Quiet Participation: Investigating non-posting activities in online learning
Bibliographic record
Abstract
Despite the growth in online learning offerings in K-12 and higher education, limited research has been undertaken to better understand less visible online learning activities. Reading and rereading are not typically valued as important indicators of learning since number or frequency of entries, words or key phrases are usually visible and easily tracked. This paper addresses reading, writing and revisiting behaviours by cluster groups in eight online courses, and looks for patterns related to rereading. Participant perceptions of the value of rereading entries in online learning are discussed. The findings highlight the importance of a more nuanced understanding of the different roles reading and rereading play in online learning discussions. This research informs our understanding of the importance of non-posting behaviors to student learning. Instructionally, these results may encourage valuing of different “paths” to online learning success beyond the criterion of written entries.Despite the growth in online learning offerings in K-12 and higher education, limited research has been undertaken to better understand less visible online learning activities. Reading and rereading are not typically valued as important indicators of learning since number or frequency of entries, words or key phrases are usually visible and easily tracked. This paper addresses reading, writing and revisiting behaviours by cluster groups in eight online courses, and looks for patterns related to rereading. Participant perceptions of the value of rereading entries in online learning are discussed. The findings highlight the importance of a more nuanced understanding of the different roles reading and rereading play in online learning discussions. This research informs our understanding of the importance of non-posting behaviors to student learning. Instructionally, these results may encourage valuing of different “paths” to online learning success beyond the criterion of written entries.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".