Factors affecting the effectiveness of novice EFL teachers’ transition in Niger
Bibliographic record
Abstract
The aim of this study is to understand the factors that cause obstacles to an effective transition for novice EFL teachers in Niger. It draws from socio-cultural perspectives to demonstrate the relationship between those factors and teachers’ previous education. Participants in this study consisted of 10 novice EFL teachers with a maximum of five years of teaching experience. Using a qualitative framework, semi-structured interview strategies were used to collect verbal data. The recorded data were coded to unravel the factors that negatively affect the transition of the 10 participants. Analyzed data showed that novice EFL teachers face multiple challenges, such as heavy workload, lack of teaching resources, constraints due to large classes, lack of skills or knowledge for managing certain classroom issues, and poor support from host institutions. While some of the factors reflect the economic status of the country, most of the remaining others are indicative of a poor teacher education system. Solutions to these reside in a sustainable reform of the teacher education system by integrating innovative approaches to professional development and by initiating trainees into procedures that foster teacher autonomy. Procedures such as lesson study, action research, and reflective practice could provide avenues to novice EFL teachers in terms of working collaboratively to establish ways for understanding their learners, developing materials that work, and making collaborative decisions on how to tackle classroom issues.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".