High School Students’ Topic Preferences and Oral Development in an English-only Short-term Intensive Language Program
Bibliographic record
Abstract
Developing the ability to speak English is a daunting task that has long been omitted in a test-driven pedagogy context (Chang, 2011; Li, 2012a, 2012b; Chen & Tsai, 2012; Katchen, 1989, 1995). Since speaking is not tested for school admissions, most students are not motivated to learn it (Chang, 2011; Chen & Tsai, 2012). Now, globalization makes English Lingua Franca; speaking English is definitely bound to be one key capability to connect oneself with the world (Graddol, 2007). Thus, teachers strive to help learenrs learn English by selecting appropriate and interesting topics to motivate them to learn more effectively (Dörnyei & Csizér, 1998; Spratt, Pulverness & Williams, 2011), especially in speaking. However, with only one internationally published research on Taiwanese college students’ topics preference (Chen, 2012) and none on high school students, selecting appropriate topics seems challenging. Consequently, this study intended to investigate the potential topics that motivated learners to practice speaking and their oral performance. The results show that learners preferred topics related to their daily life and their speaking improved in terms of speech unit, clause unit, and words uttered.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".