Emergency Remote Teaching: The Challenges Associated with a Context of Second Language Instruction
Bibliographic record
Abstract
The pandemic of 2020 frequently necessitated that offshore school teachers continue their instruction of Chinese children in the online format rather than face-to-face back in China; a so-called emergency remote teaching response. A required change in pedagogy accompanied a range of challenges in an effort to offer quality education to English as a Second Language (ESL) students. During the fall 2020-2021 academic year, a sample of 25 teachers and 3 principals provided feedback on those inherent challenges in a mixed method study consisting of surveys, interviews, and focus groups. Factors that impacted the delivery were identified in broad categories of teacher lifestyle, hindrances with technology, teaching practice, and pedagogical support. The findings were unique in that 1) they were nested in a response to a difficult context as opposed to a carefully planned online instruction and 2) second language students constituted a different learning cohort. This work further adds to the literature by suggesting that cognitive load, self-regulation, and attentional literacy deserve careful consideration when contexts of ESL learning with technology are implicated. This work further adds to the literature by suggesting that cognitive load, self-regulation and attentional literacy deserve careful consideration when contexts of ESL learning with technology are implicated.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".