Exploring the Experiences of Faculty-led Teams in Conducting Action Research
Bibliographic record
Abstract
Action research has been suggested as a useful way to support university faculty to improve teaching and learning. However, there seems to be little knowledge about how faculty (and those who work with them) experience the process of doing action research. In order to explore team members’ in-depth experience about what they learned and how they experienced conducting action research, this study documented the experiences of two action research project teams supported through an initiative at Simon Fraser University, the Teaching and Learning Development Grants program (TLDG). Using case study methodology, multiple types of data were collected and analyzed through an iterative process. The results showed that all the team members perceived they had developed professional knowledge through participating in the projects. Most team members perceived a positive experience of teamwork as well as satisfaction with the experience of conducting action research. On a suggéré que la recherche-action était un moyen efficace pour aider les professeurs d’université à améliorer l’enseignement et l’apprentissage. Toutefois, on semble ne pas avoir beaucoup de détails sur la manière dont les professeurs (et ceux qui travaillent avec eux) font l’expérience de la recherche-action. Afin d’explorer l’expérience profonde des membres d’un groupe relative à ce qu’ils ont appris et comment ils ont vécu cette recherche-action, cette étude documente les expériences de deux groupes qui ont participé à un projet de recherche-action dans le cadre d’une initiative organisée par le programme des TLDG (Teaching and Learning Development Grants) de l’Université Simon Fraser. Grâce à la méthodologie des études de cas, divers types de données ont été recueillies et analysées au moyen d’un processus itératif. Les résultats ont montré que la participation au projet avait permis à tous les membres des groupes d’acquérir des connaissances professionnelles. La plupart des membres ont indiqué qu’ils avaient vécu une expérience de travail de groupe positive et qu’ils étaient contents d’avoir mené des activités de recherche-action.
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 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 | 0.061 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".