Does Size Matter? Instructors’ and Students’ Perceptions of Students’ Use of Technology in the Classroom
Bibliographic record
Abstract
Aim/Purpose: The aim of this study was to explore whether students’ self-initiated personal technology use differ by class size as well as to explore students and instructors’ perspectives on whether students’ technology use in class is a problem. Background: Because class size influences several aspects of student engagement, it is plausible that class size would affect students’ technology behaviours, but, to our knowledge, no study has directly examined class size as a factor in students’ on- and off-task technology use. There is also a paucity of research on how the use of off-task technology affects instructors. Methodology: We surveyed all undergraduate students and faculty in one Faculty at a Canadian university in Fall 2016. A total of 478 students and 36 instructors completed the survey. The survey contained questions about students’ and instructors’ behaviours and attitudes towards the use of technology in class. Quantitative data were analyzed in SPSS and Excel and qualitative excerpts from short-answer questions on the survey were analyzed in NVIVO 8. Contribution: This paper demonstrates that students’ on- and off-task technology use in class is influenced by class-size. It also informs us on the impact students’ technology use has on instructors in the academic classroom. Findings: Student-initiated technology use increased significantly as class size increased. Students and instructors expressed little concern about the impact of class-related technology use on learning and views did not differ significantly between these two groups. Although both students and instructors believed off-task technology use hinders learning, their views differed significantly, with more instructors than students feeling strongly that students’ use of technology in class is a problem. Recommendations for Practitioners: We need to develop guidelines on how to address off-task technology use in class. Recommendation for Researchers: More research is needed to explore how the use of technology in class affects instructors. Impact on Society: Higher education industry needs to consider how to manage the use of off-task technology in class. Future Research: We need to explore further how to mitigate the factors contributing to the off-task technology use in academic classroom.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".