From Pedagogy to Andragogy in Post Covid-19 ESP Courses: A Customized Blended Learning Model for English in Medicine at a Saudi University
Bibliographic record
Abstract
Teaching an ESP course is significantly challenging due to its practical, contextual, and communicative nature as a “language in context” and a prerequisite for acquiring professional skills and job-related functions that require real life learning situations to imitate specific professional settings and accentuates practicing the essential English language skills that students would primarily employ in their future fields. This paper presents an andragogy-based customized blended learning model for English in Medicine course introduced to students of the preparatory year program during COVID-19 pandemic. Integrating andragogical principles, the course identifies students’ access to multimodal tools for required English Language skills and medical vocabulary, including a virtual listening lab for augmenting listening skills, a virtual medical library for boosting online medical reading, a virtual hospital of different doctor-patient interaction scenarios for practicing the use of language in context, course community blogs and audio discussion forums for enhancing writing and speaking skills, an online medical dictionary for understanding and translating medical terminologies and integrated Kahoot games for testing field related knowledge. A mixed methodology of pre-test and post-test research design along with a learning satisfaction survey were used to evaluate the change in variables which were students’ English Language proficiency, cognitive competence, learning motivation, and learning styles. The study findings establish the effectivity of the shift from pedagogic to andragogic strategies in the course. It supports integrating pedagogic constructive theories along with adult learning theories and blended education theories to ensure productive teaching and learning of ESP courses in accordance with the constrictions of quality modern education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".