Did they Manage to Meet Students’ Needs? English Language Instructors’ Experiences of Remote Instruction Differentiation
Bibliographic record
Abstract
The COVID-19 pandemic has impacted English language teaching (ELT) in many ways, and it has pushed language educators to the limit. Due to the shift to virtual education, the non-mediated in-person support that many instructors used to acknowledge their students’ needs, is no longer available. A question of significant relevance to this ongoing emergency shift is: How do English language instructors differentiate remote instruction? Differentiated Remote Instruction (DRI) is the pedagogical approach that is needed for successful e-learning and remote teaching. Guided by three main research questions, this study examines the adoption and challenges of differentiated remote instruction (DRI) in online classrooms by English language college instructors during COVID-19 in the Saudi context. The study adopted a mixed-methods approach, and the examination is based on online surveys filled out by 172 English language instructors and a thematic analysis of six semi-structured interviews. Analysis has yielded interesting findings on the differentiation practices and challenges among virtual language instructors. Findings show that there are some factors related to 1) students; 2) instructors; and 3) technological issues that affect and challenge the implementation of DRI in EFL virtual classrooms. Moreover, despite the DRI challenges faced by the EFL instructors, they did attempt to find methods to deal with them. These methods were related to effective teaching through online platforms (LMS), early diagnosis and interventions for problem and weak learners, specific tailoring of lessons and activities, and dedicating one-on-one online sessions for students in need.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".