Learners' Perspectives on what is Missing from Online Learning: Interpretations through the Community of Inquiry Framework
Bibliographic record
Abstract
Despite the success that instructors and learners often enjoy with online university courses, learners have also reported that they miss face-to-face contact when learning online. The purpose of this inquiry was to identify learners’ perceptions of what is missing from online learning and provide recommendations for how we can continue to innovate and improve the online learning experience. The inquiry was qualitative in nature and conducted from a constructivist perspective. Ten learners who had indicated that they missed and/or would have liked more face-to-face contact following their participation in an online course were interviewed to elicit responses that would provide insights into what it is they miss about face-to-face contact when learning online. Five themes emerged: robustness of online dialogue, spontaneity and improvisation, perceiving and being perceived by the other, getting to know others, and learning to be an online learner. Garrison and colleagues’ (Garrison, Anderson, & Archer, 2000) community of inquiry framework was used to interpret the findings.
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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 | 0.051 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| 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".