Tensions in Learner Support and Tutor Support in Tertiary Web-based English Education in China
Bibliographic record
Abstract
Based on the findings of a national survey conducted in 2004 designed to examine the support systems for both learners and tutors engaged in tertiary-level Web-based English education in mainland China, this paper reports the findings of secondary analysis by identifying the tensions in the current learner and tutor support systems. For learner support, four tensions were analyzed: (1) vigorous institutional learner support efforts versus learner utilization of the provisions; (2) learner qualities development versus academic support; (3) learner technical competence versus learner participation in online services; and (4) the relationship of face-to-face components and online components in learner support system design. For tutor support, four tensions were identified: (1) institutional conceptual understanding versus the actual practices; (2) tutor' enthusiasm versus tutor’s perception of online education; (3) tutor responsibilities versus tutor commitment, and (4) current tutor support service repertoire versus tutor improvement areas. The paper analyzes possible causes for the tensions and proposes some solutions to address these tensions. Keywords: learner support, tutor support, China, Web-based, tertiary, English language education, tension
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".