The Correlation among L2MSS, Foreign Language Enjoyment and Boredom in Online Classes: An Exploratory Study of Chinese English Majors
Bibliographic record
Abstract
According to the statistical results of questionnaires issued, this research, standing up for positive psychology (PP), analyzed the relationship between second language motivational self system (L2MSS), foreign language enjoyment (FLE) and foreign language learning boredom (FLLB) of Chinese English language students in the post- pandemic era in an e-learning environment. The results show that: (1) Chinese English majors maintain a medium to high level of L2MSS and FLE in their online classes, and a medium level of foreign language learning boredom; (2) FLE has a negative correlation with FLLB; (3) FLE produces a positive predictive trend for ideal L2MSS and learning experience. However, FLLB does not produce a significant predictive trend for either of these categories. The study integrates multiple theories in second language acquisition (SLA), which not only corroborates the applicability of the undoing hypothesis in the online classroom, but also provides a scientific basis for improving language learning outcomes for English majors in China at a theoretical level.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".