The Effect of Technology Based Instruction Lesson Plan on EFL Pre-Service Teachers’ TPACK Self-Efficacy
Bibliographic record
Abstract
This study aims at delving into the effect of applying TbI (Technology based Instruction) lesson plan to pre-service teachers’ (PSTs) Technological Pedagogical Content Knowledge (TPACK) self-efficacy in a microteaching course. TbI lesson plan was developed following Niess’s technology integration framework. The experimental research with pre-post measurements design was conducted in an English Department in a public university in West Nusa Tenggara, Indonesia. The data-gathering process was done by administering the TPACK self-efficacy questionnaire and conducting a semi-structured interview. The participants were 23 PSTs who joined a microteaching course where the researcher became the teacher. The quantitative data were analyzed statistically through SPSS 24 version and supported by the PSTs responses which were thematically analyzed. The results showed that the treatment affected positively to PSTs' self-efficacy which was later recommended to inhibit technological knowledge prior to other TPACK divisions to PSTs. This favorable effect was confirmed by six participants' post-interview comments, in which they claimed all of the benefits of using a TbI lesson plan to improve their microteaching performance and confidence to use technology for EFL teaching. Henceforth, this study implies the urgency to apply similar instruction to mediate challenges of technology integration in EFL teaching.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".