Balancing teachers’ needs in times of crisis: investigating how computer science instructional coaches and teachers navigated remote professional development during COVID-19
Bibliographic record
Abstract
This study investigated how Chicago Public Schools (CPS) computer science (CS) teachers and instructional coaches navigated remote professional development (PD) during the pandemic. Analyzing multiple sources of qualitative data, we explored how coaches adapted PD to address teachers’ unique needs and how teachers experienced remote PD. We found that the coaching team designed PD to help teachers translate key instructional strategies into the remote learning environment and increasingly centered their PD design efforts on improving teacher engagement and wellbeing. Teachers primarily valued the relational aspects of PD, including opportunities for collaboration and personalized support from instructional coaches. Leveraging an ecological framework, we found that the pandemic and remote learning contexts amplified preexisting PD challenges experienced by teachers and coaches. Findings suggest that PD researchers and designers should focus on teacher wellbeing and that districts should invest in flexible and adaptable PD structures to meet CS teachers’ varied needs.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".