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Record W4391567262 · doi:10.1080/08993408.2024.2313913

Balancing teachers’ needs in times of crisis: investigating how computer science instructional coaches and teachers navigated remote professional development during COVID-19

2024· article· en· W4391567262 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Science Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsLearning Partnership
FundersNational Science Foundation
KeywordsCoronavirus disease 2019 (COVID-19)Professional developmentComputer scienceFaculty development2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mathematics educationInstructional designMedical educationPsychologyMultimediaPedagogyMedicineVirology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.338
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it