An examination of laptop-based off-task behaviours in secondary school classrooms
Bibliographic record
Abstract
The purpose of this study was to examine factors that affect secondary school students??? off-task behaviours in laptop-based classrooms. Quantitative and qualitative data were collected from 224 secondary school students from four private schools in Canada (156 males, 65 females, 3 no response). The perceived advantages of laptop use in the classroom were access to information online, the use of technology during class, and the use of specific programs and applications during course work. The perceived disadvantages of laptop use in the classroom by students were being distracted by peers and engaged in off-task behaviours. The factors that appeared to influence off-task laptop-based activities were subject area, instructional method, and gender. Gender differences were found in students??? on-task activities and off-task activities. Females reported engaging in on-task activities significantly more than males. Females also engaged significantly more frequently in social media compared to males, whereas males played games significantly more often than females. More in-depth research, perhaps in the form of interviews and discussion groups, needs to be conducted on how subject area and instructional method might influence secondary school students??? off-task behaviours.
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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 | 0.003 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".