Reusing Comprehensive Charts of Tense Forms to Teach EFL Students in a University of Science and Technology.
Bibliographic record
Abstract
This study investigated 228 English as foreign language freshmen at a university of science and technology insouthern Taiwan to explore the participants’ knowledge of English tense forms by recognizing 12 tense forms andtranslating Chinese sentences into English with specific tense forms. The results showed that the participants whowere taught with the Comprehensive Charts of 24 Tense Forms outperformed their counterparts in the control groupin recognizing the tense patterns and translating the present perfect continuous tense (at a significant level, p＜.05).However, the translation of the other two tense sentences did not reach the statistically significant level. The resultsof this study supported the researcher’s speculation in the previous study, Using Comprehensive Charts of TenseForms to Teach EFL College Students, that the mastery of tense forms was closely related to the subjects’ Englishproficiency level and the non-written context. The instruction with the Comprehensive Charts particularly worked forstudents of the advanced level. A questionnaire designed to explore the participants’ preference of tense formteaching with the Comprehensive Charts indicated that 65.1 percent of the subjects preferred the focus on formSteaching (Doughty & Williams, 1998) and 64.7 percent of them deemed it was the most effective. This present studysuggested storing the grammatical knowledge of tense forms in learners’ memory for future retrieval based on the“Instance Theory.” An Instructional Mode: Using the Comprehensive Charts for Teaching the Tense Forms was alsoappended.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".