Virtual Schooling through the Eyes of an At-Risk Student: A Case Study.
Bibliographic record
Abstract
While much of the growth in the popularity of virtual schooling has involved at-risk students, little research exists on the experiences of these students in this largely independent setting. This paper describes a case study of an at-risk student in a rural school in the province of Newfoundland and Labrador who was enrolled in an online course as a means to graduate on time. Data from interviews and video observations were analyzed to reveal several themes. The student was good at prioritizing and understood what students needed to do to succeed in an online environment, yet he often did only the minimum needed to pass the course, and his productivity during synchronous and asynchronous sessions declined as the hour progressed. We also found that the student was limited by the lack of proper technology at home. Based on a single case, we are unable to generalize beyond this one student. However, since the attitude of taking the path of least resistance may have taken hold in earlier grades for this particular student, research into improving virtual schooling for at-risk students may be ineffective or counterproductive by reinforcing rather than reducing those attributes; at least in this instance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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".