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 in southern Taiwan to explore the participants’ knowledge of English tense forms by recognizing 12 tense forms and translating Chinese sentences into English with specific tense forms. The results showed that the participants who were taught with the Comprehensive Charts of 24 Tense Forms outperformed their counterparts in the control group in 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 results of this study supported the researcher’s speculation in the previous study, Using Comprehensive Charts of Tense Forms to Teach EFL College Students, that the mastery of tense forms was closely related to the subjects’ English proficiency level and the non-written context. The instruction with the Comprehensive Charts particularly worked for students of the advanced level. A questionnaire designed to explore the participants’ preference of tense form teaching with the Comprehensive Charts indicated that 65.1 percent of the subjects preferred the focus on formS teaching (Doughty & Williams, 1998) and 64.7 percent of them deemed it was the most effective. This present study suggested 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 also appended.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".