Theoretical Foundation for Developing Instructional Guide for China Pre-Service EFL Teachers to Teach Phonics
Bibliographic record
Abstract
In China, the absence of a phonics instructional guide hinders pre-service EFL teachers’ preparation for phonics instruction. To bridge the gap, the purpose of this article is to lay the theoretical groundwork for developing a phonics instructional guide for Chinese pre-service EFL teachers and then to produce a research methodology for this instructional guide. The Bottom-up Theory of Reading Process; the S-R Theory with Reinforcement, particularly Instrumental Conditioning; and the Trial-and-Error Theory of Learning all have pedagogical implications for implementing explicit and systematic phonics instruction for EFL beginners in China. Additionally, in conjunction with the Andragogy Theory of Adult Learning, these theories provide a theoretical foundation for instructional design. Following the ADDIE sequential framework, a multi-phase mixed methods experimental design was used to collect data from 254 representative samples chosen through a stratified random sampling technique. The findings indicate that within the theoretical framework and with the incorporation of specific design frameworks into the ADDIE sequential framework, the instructional guide was successful, although some refinement is still needed. Furthermore, the findings suggest that additional research could be conducted on advanced evaluation levels.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".