Gender Differences in the use of Laptops in Higher Education: A Formative Analysis
Bibliographic record
Abstract
Over the past 18 years, a number of large scale reviews of the literature have documented that gender differences in computer attitudes, ability, and use tend to favor males. Since the use of laptops in higher education classrooms is increasing, it is important to examine whether this use is disproportionally advantageous to males and disadvantageous to females. The purpose of this study was to explore gender differences in the use of laptops in higher education classrooms. Two key areas were examined: on-task behaviors (note-taking, academic activities, instant messaging) and off-task behaviors (e-mail, instant messaging, games, movies, distractions). With respect to on-task behaviors, females reported significantly more note-taking and participation in academic laptop-based activities. No gender differences were observed with respect to instant messaging for academic purposes. Regarding off-task behaviors, females were more distracted by their peers' use of laptops than males, whereas males reported that they played significantly more games during class. Recommendations for future research include expanding the breadth of off- and on-task behaviors assessed, exploring the role of teaching strategies, and focusing on learning performance.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".