Bibliographic record
Abstract
My introduction to action research (AR) began in 2004 when I was selected to complete a 6month professional development AR course with my school board.At the time, I was an early career teacher trying to survive one of my Nirst teaching placements.My project title was Nitting: Why Do Junior Students Dislike French?The research process changed my teaching practice.I realized that research-based evidence was critical for guiding educational practice.Over the years as a public school teacher and now as a higher education teacher, I have completed several AR projects; each one has helped me gain more self-autonomy in my professional practice.I continue to pass along my belief and passion that AR is a self-changing process.For the past 4 years, I have taught an introductory research course to teacher candidates where they learn how to conduct their own "mini" AR projects based on problems of practice during their teaching practicum.Last term, my own AR project was to track my students' learning and responses to their AR processes and Nindings.I was pleased to discover that their responses mirrored how I felt in 2004: Many commented that they were pleasantly surprised at the amount they learned and how relevant AR could be to their teaching.Action research is an intentional and systematic investigation process (Stringer, 2014) that can help educators Nind solutions, based on evidence, to everyday issues and problems of practice.This special issue is designed to not only highlight the beneNits of AR but to also encourage educators to use AR to Nind solutions to the challenges created by the COVID-19 global pandemic.As we have all experienced, the pandemic created challenges, chaos, and many unknowns, such as the isolation of online learning, the rigid structures imposed in classrooms, and the extreme hurdles placed on researchers seeking ethical approval, to name a few.Like myself, educators, groups of educators, and educational system leaders were recognizing the powerful autonomy of conducting their own research on issues they were facing during the pandemic and determining links between effective professional practice and learning (Parsons et al., 2013).Out of necessity, partnerships were formed between teacher educators, teacher-mentors, and pre-service and/or novice teachers to Nind solutions to the many challenges created by the pandemic.Action research provided a method for "looking at one's practice or work
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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 | 0.036 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.028 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 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".