Phenomenological Exploration of the COVID-Time English Language Education in Universities: A Thematic Review
Bibliographic record
Abstract
Phenomenology is applied when a researcher intends to go deep into the research participants' opinions, feelings, and lived experiences to understand a phenomenon's universal nature. This paper, through an inclusion criterion, selected and explored sixteen articles (published between 2020 and 2023) that adopted phenomenology to discover the pedagogical strategies applied in English language education (ELE) in universities during the COVID emergency (2020 – 2021). It conducted thematic data analysis through the 5W1H reporting framework. It highlighted the types of phenomenology that explored English language teachers' and learners' perspectives about educational technologies (EdTechs) and related techno-pedagogies in universities. The findings categorized into six major themes highlighted that the old legacy of teaching-learning in universities was in question during the COVID time due to many new challenges the teachers and the learners faced, and the lack of infrastructural settings of educational institutes. However, the vacuum was gradually filled with the rise of new techno-pedagogies and this crisis somewhat equipped the educational stakeholders (through both synchronous and asynchronous ELE) to face any future catastrophe. The insights from this paper will help pedagogues, students, curriculum specialists, policymakers and university authorities realize the significance of the New-normal techno-pedagogies in ELE for the upcoming days or in any future emergency.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".