Experiences of Online Closeness in Virtual Learning Environments (VLEs)
Bibliographic record
Abstract
Abstract In virtual learning environments (VLEs) students often find themselves in front of a computer, looking at a bright screen, interacting with classmates and teachers through a keyboard and a mouse, and, in most cases, listening and watching someone who is not physically present. Virtual components (or even an entirely online classroom) are not rare, and growing concern is currently surfacing about students’ potential feeling of isolation, which has been found to increase educational barriers such as lack of motivation or engagement, or poor academic achievement. We may therefore begin to wonder whether VLE allows for the necessary interpersonal involvement required for learning. Using a qualitative phenomenological research methodology called phenomenology of practice, the aim of this study was to understand what it is like to experience a sense of closeness to others in a VLE. Data was gathered by means of in-depth phenomenological interviews with five adult university students recruited via snowball sampling who had previous experience in VLE settings. The findings revealed that students may experience closeness with their classmates and teachers when they suddenly look beyond the superficial technological hurdles and find the humanity in the virtual others, when they share a difficult group experience, or when they create a personal virtual space. This study showed that closeness is indeed essential in education, and that even online we repeatedly find ourselves in a continuum of closeness to others, moving from an experience of togetherness to an experience of loneliness, or vice versa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".