“Switching to the Online MA TESOL Program”: Experiences and Decision-Making Processes of International Learners
Bibliographic record
Abstract
The COVID-19 pandemic changed the curricula and mode of instruction for many postgraduate taught and research programs. Before the pandemic, many MA TESOL programs included on-campus tutorials as requirements, as the curricula were designed based on on-campus activities. However, as the United Kingdom restricted face-to-face teaching due to social distancing, most of the courses were switched to online learning platforms during the pandemic. Although most courses returned to on-campus teaching during the 2021/2022 academic year, a group of East Asian international students decided to continue their MA TESOL program online as an alternative option. The purpose of this study is to understand the experiences and decision-making processes about this group of MA TESOL students and their decisions to finish their degrees via the online completion option. In line with social cognitive career and motivation theory, the results indicated that flexibility, career development through online learning options, and concerns about job security were the main three themes that arose within this group of students. The outcomes provide suggestions to university leaders and program directors in regard to developing additional online courses and programs to meet the needs of adult and postgraduate students who cannot attend on-campus courses, particularly MA TESOL learners.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".