Students’ Perceptions of the Infrastructural Design and TA-Student Interaction Modes of a College Freshman English Tutorial Program
Bibliographic record
Abstract
In recent years, higher education institutes in Taiwan are implementing tutorial programs and are recruiting student tutors/teaching assistants (TAs) in an effort to facilitate instructional effectiveness of core subjects. Undoubtedly, infrastructural design and TA quality are two vital aspects for achieving success of the tutorial programs. This study employed research methods of surveys and interviews to examine, from students’ perspectives, effectiveness of a college freshman English tutorial program in terms of its implementation and its TA-student interaction. Results of the study show that, in terms of the tutorial implementation, students were generally satisfied with one-on-one tutoring and convenience of time and location. As for preferred TA-student interaction modes, the students favored collaboration with the TAs for diagnosing and verbalizing their English learning problems. In particular, the students appreciated that the TAs listened attentively to their feelings and problems. Based on the findings, implications for program design and TA training are addressed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".