Literacy Clinics During COVID-19: Voices that Envision the Future
Bibliographic record
Abstract
The resiliency of literacy clinics was tested during 2020–2021, as many pivoted from in-person (F2F) to online or 3-way remote learning because of the COVID-19 pandemic. University-based literacy clinics advance teacher education, provide services to K-12 students who may need instructional support, and are a laboratory for research. The purpose of the study was to examine modifications in literacy instruction and assessment as a consequence of the changes in modality. Participants (n = 58) were literacy clinic directors/instructors from multiple states and countries. Data were analyzed in three phases: researchers individually coded; multiple teams cross-checked; a macro team collated across themes. Alterations during the pandemic involved place, time, types of texts, innovative instructional tools, and new ways of operationalizing literacy assessment and instruction. Some clinics used technology to transform instruction and innovate, while for others the goal was to replicate existing practices. Teachers, students in the context of their families, and teacher educators demonstrated resiliency, resourcefulness, and creativity in the face of interruptions and stress. Findings, viewed through the lens of the TPACK framework, can help us understand how transformations in instruction and assessment affect literacy learning not only in the context of clinics, but in school classrooms as well.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".