Student use of technology today: helping plan the school of tomorrow
Bibliographic record
Abstract
The purpose of this study was to determine how secondary students attending F.H. Collins Secondary School (FHC) inWhitehorse, Yukon in spring, 2011 viewed and used technology to complete their studies at school and at home.FHC is scheduled for imminent replacement and the decision about what educational technology to install in the new school must be made by the Yukon Department of Education, based on the advice of the new school's Building Advisory Committee (BAC)-my recommendations are directed to the BAC.Over 50% ofFHC students responded to a voluntary and anonymous quantitative survey conducted by FHC staff.I concluded that respondents felt powered down (Evans, as cited in Nagel, 2009) while attending FHC because theeducational technology students use most often and believe to be most important when completing schoolwork was more accessible at home than at school.Of great concern is that Yukon First Nations were more likely to feel powered down their Non First Nations counterparts at FHC.The greatest limitation of the project was the choice to survey students of today when trying to conceptualize the school oftomorrow.I recommend that all students at FHC have access to school-provided wireless Internet and be encouraged to take personal technological devices like laptops to school, and that the school ' s Internet site restriction policy be reviewed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".