Harnessing the Affordances of Action Researchers to Address the Challenges of the COVID-19 Pandemic: Educational Leaders take Action Research Online
Bibliographic record
Abstract
The unique affordances of Action Research, including flexibility, playfulness, accessibility, and a focus on practical problem solving provided crucial strategies for generating knowledge and developing solutions to the challenges created by the Covid-19 pandemic. The move to online research settings, in particular, required action researchers to find ways to adapt existing research methods and to devise new approaches. This article describes the work of a group of doctoral students in an Educational Leadership program and their instructor in carrying out action research methods in both synchronous and asynchronous online settings. If the months of the pandemic have taught us nothing else, it is that flexibility and willingness to innovate, which are central to action research, are valuable assets in times of uncertainty. The unique affordances of Action Research include creativity, playfulness, accessibility to multiple participants and audiences, transferability of findings, and a focus on the generation of knowledge designed to be pragmatic and problem-focused. These qualities can be harnessed to address the multiple challenges we have encountered during the pandemic including health equity and access, poverty and unemployment, and the interruption of education for vulnerable student populations. They also offer us hope that action research can continue to contribute to addressing the challenges we are sure to face in the future. As students in an educational leadership doctoral program, we focus on examining problems of practice in our schools and districts through action research. As we adapted to online learning in our own schools, we were able to bring these skills to bear in our doctoral studies by developing strategies for conducting these action research methods in both synchronous and asynchronous online settings. This paper describes some of the approaches we developed in the hope that this will enable other action researchers to implement these methods in their own schools, organizations, and communities. The specific action research methods described in this paper are Future Creating Workshops, Citizens’ Juries, World Café, Nominal Group Technique, and Digital Storytelling.
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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 | 0.116 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".