Faculty Use of Tablet Computers at the University of Ontario Institute of Technology
Bibliographic record
Abstract
This article describes instructor use of tablet computers for personal use, research activities and teaching practices within the Faculties of Science and Engineering at UOIT. The benefits of tablet use were evaluated on the basis of types of usage, personal and professional productivity and the “richness” of the overall computing experience. Major findings include the enhanced ubiquity of computer use by faculty as a result of increased mobility, and the modification of pedagogical practices before, during and after lectures. The article also reports on faculty speculation regarding the effects of tablet use by students as well as suggestions for improving tablet computer design. The article concludes with a number of recommendations for the expanded use of tablet computers within higher education settings. Résumé : Le présent article décrit l’utilisation par l’instructeur d’ordinateurs tablettes à des fins personnelles, pour des activités de recherche et la pratique de l’enseignement au sein des facultés de sciences et génie de l’UOIT. On a évalué les avantages de l’utilisation de la tablette en fonction des types d’utilisation, de la productivité personnelle et professionnelle et de la « richesse » de l’ensemble de l’expérience de traitement. Parmi les conclusions importantes, on trouve l’ubiquité améliorée de l’utilisation de l’ordinateur par la faculté en raison de la mobilité accrue et de la modification des pratiques pédagogiques avant, pendant et après les cours. Le présent article traite aussi des suppositions du corps professoral quant aux effets de l’utilisation des ordinateurs tablettes par les étudiants et de suggestions visant l’amélioration de la conception de ces ordinateurs. L’article termine sur des recommandations pour une utilisation accrue des ordinateurs tablettes en enseignement supérieur.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".