Mobile Teacher Professional Development (MTPD): Delving into English Teachers’ Beliefs in Indonesia
Bibliographic record
Abstract
In recent years, mobile phones have been used for teacher professional development (TPD). However, the potential use of smartphones, a current-generation of mobile phones, to develop teachers’ pedagogical, social, personal, and professional competences remains underexplored. This mixed methods study, examining the potential use of smartphones for TPD by delving into English teachers’ beliefs, employed a sequential explanatory approach. A quantitative survey was completed by 81 participants, followed by qualitative interviews with 8 selected participants. All the respondents were English teachers in elementary, junior, and senior high schools in 11 provinces in Indonesia. The survey was tested for validity and reliability, and analysed using the descriptive statistics method, while the semi-structured interview was analysed using the content analysis method. Almost all teachers had very favourable and favourable beliefs about the use of smartphones for TPD, perceiving that a smartphone could facilitate the enhancement of their pedagogical knowledge, communication skills, positive characters and English proficiency. Very few teachers had unfavourable beliefs, but among those who did, they believed traditional face-to-face TPD was more beneficial and that smartphones would only lead to addiction. This study recommends that smartphones be optimally applied by English teachers for TPD activities and that governments facilitate such implementation by constructing smartphone TPD models and applications.
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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 | 0.004 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".