An Action Research Study of Using Group-Based Remote Learning with ESL High School Students in English Language Arts
Bibliographic record
Abstract
At the time of writing, New York City high schools had been working remotely for a year, with educators facing some great challenges. Working remotely marked a significant difference in the way we teach and the way our students learn. Classroom spaces were no longer room numbers on doors; they were virtual meeting room numbers on platforms like Teams or Google Classroom, and they were accessible from anywhere by Wi-Fi. According to the United Nations Educational, Scientific, and Cultural Organization (UNESCO, 2020), school closures caused by the Covid-19 pandemic affected over 1.5 billion students and families. The Covid-19 pandemic presented multiple challenges for teaching students with English as a second language in an online instructional environment, but also opportunities for collaboration, training, and communication for inclusive educators to strive to meet the needs of their students. This article addresses the following teacher-researcher questions: 1) What opportunities do I find most rewarding teaching in an online environment to students for whom English is a second language? 2) What do I find to be most challenging teaching English second language (ESL) students in the online context? 3) How can media play a part in online teaching and learning, and how do students respond to online learning with these mixed media platforms? 4) What recommendations can be offered to other inclusive educators who are teaching online? The researcher discusses the methodological approach used to conduct the research, using methods including surveys and field notes. Further research conducted was based on the researcher’s journal of field notes kept throughout teaching a unit titled “Beowulf.” An analysis of student assessment data is also provided to show progress, where applicable, for one class of English Second Language students.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".