Using cloud-computing applications to support collaborative scientific inquiry: Examining pre-service teachers’ perceived barriers towards integration / Utilisation d'applications infonuagiques pour appuyer la recherche scientifique collaborative
Bibliographic record
Abstract
Technology plays a crucial role in facilitating collaboration within the scientific community. Cloud-computing applications can be used to model such collaboration and support inquiry within the secondary science classroom. Little is known about pre-service teachers’ beliefs related to the envisioned use of this technology in their teaching. These beliefs may influence future integration. This study finds several first-order barriers, such as perceptions that these tools would take too much time to use. Second-order barriers include perceptions that this technology would not promote face-to-face collaboration skills, would create social loafing situations, and beliefs that the technology does not help students understand the nature of science. Suggestions for mitigating these barriers within pre-service education technology courses are discussed. La technologie joue un rôle essentiel pour faciliter la collaboration au sein de la communauté scientifique. Les applications infonuagiques telles que Google Drive peuvent être utilisées pour donner forme à ce type de collaboration et pour appuyer le questionnement dans les cours de sciences du secondaire. On connaît pourtant peu les opinions que se font les futurs enseignants d’une telle utilisation des technologies collaboratives infonuagiques. Or, ces opinions pourraient influencer l’intégration future de ces technologies en salle de classe. Cette étude révèle plusieurs obstacles de premier plan, comme l’idée que l’utilisation de ces outils informatiques prend trop de temps. Parmi les obstacles de second plan, on note les perceptions selon lesquelles cette technologie ne promeut pas les compétences collaboratives de personne à personne, pose des problèmes de gestion de classe et n'aide pas les étudiants à comprendre la nature de la science. Des suggestions sont proposées pour atténuer ces obstacles dans les cours de technologie des programmes d’éducation.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".