Gen Z and Digital Distractions in the Classroom:Student Classroom Use of Digital Devices for Non-Class Related Purposes
Bibliographic record
Abstract
A 2019 survey of college students examined classroom-learning distractions caused by their use of digital devices for non-class purposes. The purpose of the survey, part of an on-going study, was to learn more about students’ behaviors and perceptions regarding their classroom uses of digital devices for non-class purposes. The survey included 986 respondents in 37 U.S. states and 47 respondents in Alberta, Canada. A significant feature of the study was its measurement of frequency and duration of students’ classroom digital distractions as well as respondents’ motivations for engaging in the distracting behavior. Respondents averaged 19.4% of class time using a digital device for non-class purposes. The average respondent used a digital device 9.06 times during a typical school day in the 2019 survey for non-class purposes. On a weighted average, survey respondents indicated they would turn-off all non-class digital distractions if their instructor gave them 7.8% extra credit on their final class grade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".