Research on the Ecological Niche of Students' English Language Learning in Foreign Language Online Teaching
Bibliographic record
Abstract
Since the development of new technology, multi terminal online teaching has become the mainstream teaching mode. The classroom space environment of college English for non-English majors has changed from classroom to home, and the teaching mode has also changed from offline to online. Students' online classes at home have formed a new student English Ecological niche. Can students establish a stable and balanced Ecological niche under the online teaching environment of college English in the new era? In this study, qualitative and quantitative research methods are used. In the form of questionnaires, the main factors affecting students' English learning and the student Ecological niche scale in online teaching are designed. Data about external factors such as teachers and teaching environment, students' language learning concepts, language learning strategies, and the definition of teachers' roles are collected, and the research results are analyzed and discussed from the change trend of average and standard deviation, Finally, it was found that the students' Ecological niche had a misplaced or absent balance change phenomenon, that is, the ecological imbalance of learning in online teaching. Therefore, this study aims to arouse teachers' attention to the students' learning ecological environment, correct the misplaced learning ecology in time, and rebuild a balanced and stable student Ecological niche.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".