Perceived Learning Outcomes and Interaction Mode Matter: Students’ Experience of Taking Online EFL Courses During COVID-19
Bibliographic record
Abstract
The recent health emergency has changed the teaching mode globally, with traditional classroom teaching shifting to online platforms. This created challenges for both foreign/second language teachers and learners. Some recent studies investigated the challenges brought by online teaching from the teachers’ perspective; however, little is known about how students perceive the effectiveness of online language learning based on their experiences. Therefore, this study surveyed 252 Chinese students who took online EFL courses during the pandemic through an online survey platform. Survey questions include 75 statements regarding EFL learner’s perceived experience in taking online EFL courses, such as learning platforms, teaching and assessment methods, interaction mode, classroom management, and the effectiveness of online courses. Students rated each statement on a 5-point Likert Scale. Open-ended questions further targeted students’ suggestions to improve the quality of online language courses. Results from analysis of variance, factor analysis, and multiple linear regression analysis showed that students perceived the interactional opportunity and learning outcome as the most important factors in their online EFL learning experiences. Students generally showed a positive attitude toward their online language learning experiences and a high level of participation in synchronized online EFL courses. They further suggested a mixture of traditional and online class as an ideal teaching model for EFL learning, especially in the face of public crisis. Findings from this study may shed light on language curriculum design, language teacher education, and educational technology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".