Two Years for an English Teacher to Become a Novice Educational Researcher: Author’s Experiences from Writing Opinion Articles to Research Articles
Bibliographic record
Abstract
The current study employs an autobiographical design. An autographical design is a type of qualitative approach affiliated with the social explanatory paradigm of research. The author of this article collected his published work, certificates of achievement, and degrees to present as the results of this study. He also used the memorization of his training, workshops, and seminars to present the findings. The results show that from July 2021, the author has upgraded himself from an English teacher to become a novice educational researcher, and his work has been concerning the English language and educational fields. Additionally, he presented how he became a researcher including the difficulties, challenges, and opportunities he experienced in his life over the last two years. He noted that English for teaching and learning is not the same as English for writing research articles. He also encouraged other teachers to engage in research since it is not as difficult as everyone might have expected, and it is the best way to solve the existing problems in all fields including learning and teaching. Besides, research is not only to solve problems but to improve the outcome of the process of learning and teaching as well as other concerning areas. He finally showed simple and easy ways that everyone could become researchers and how to get their work published.
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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 | 0.030 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".