Exploring the Benefits and Challenges of Using Laptop Computers in Higher Education Classrooms: A Formative Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Because of decreased prices, increased convenience, and wireless access, an increasing number of college and university students are using laptop computers in their classrooms. This recent trend has forced instructors to address the educational consequences of using these mobile devices. The purpose of the current study was to analyze and assess beneficial and challenging laptop behaviours in higher education classrooms. Both quantitative and qualitative data were collected from 177 undergraduate university students (89 males, 88 females). Key benefits observed include note-taking activities, in-class laptop-based academic tasks, collaboration, increased focus, improved organization and efficiency, and addressing special needs. Key challenges noted include other student’s distracting laptop behaviours, instant messaging, surfing the web, playing games, watching movies, and decreased focus. Nearly three-quarters of the students claimed that laptops were useful in supporting their academic experience. Twice as many benefits were reported compared to challenges. It is speculated that the integration of meaningful laptop activities is a critical determinant of benefits and challenges experienced in higher education classrooms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it