Developing a Mentoring-based Booklet for the Professional Development of Indonesian Novice Teachers of English
Bibliographic record
Abstract
development of teacher professionalism (Farrell, 2009), a crucial time that will greatly affect teacher retention (Jin, Li, Meirink, van der Want, & Admiraal, 2019).Therefore, practices of providing support and assistance for novice teachers are commonly found in such countries as the United States, Canada, and the United Kingdom through a program known as teacher induction (Cherubini, 2009).In the context of preparing, professionally mentoring, and then continuously supporting novice teachers of English, programs such as the teacher induction model seem to be non existent in the Indonesian context.Therefore, empirical evidence about novice teachers are quite limited, contrary to the data concerning experienced and professional teachers, which is abundant as has been reviewed by Widiati, Suryati, and Hayati (2017), some of which are, for example, studies by Musthofa (2011), Triyanto (2012), Abdullah (2015), and Irmawati and Widiati (2017).Meanwhile, at the international context, much research on novice teachers has revealed that the initial years of teaching are often marked with confusion, challenges, and tensions (see, e.g.,
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".