I Prefer My Own Ways to Acquire My English Speaking Skills: A Grounded Research
Bibliographic record
Abstract
Formal educational practitioners tend to neglect the students’ sense of liking; we labled as Preferent learning, in order to acquire certain skill in the learning foreign language, especially speaking skills. In general, so far, issues of formal learning with the focus on bounded academic rules, cognition, and motivation have been used as the main basis for the learning foreign language and even learning in general. In fact, the individual learning, language community, social change, and sopihisticated technology need to be considered in how students acquire the skills they want based on their preferences. By investigating how the University students in Kolaka learned and improved their English speaking skills, we applied a Grounded study that involved 10 informants who were the students and alumni of the English Language Education Study Program of the University X in Kolaka, Southeast Sulawesi, Indonesia. All data were collected 12 times in 3 stages then were analyzed using three steps of Strauss and Corbin's analysis that applied theoretical sampling and constant comparison in generating the substantive theory. The findings revealed that the informants acquired the English speaking skills because of a sense of liking or preference toward any topic to learn. Further, they prefer to learn in an unpredictable ways without any rules and an informal self-evaluation were applied as a way in sustaining the skills.
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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 | 0.024 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".