Connecting OER With Mandatory Textbooks in an EFL Classroom: A Language Theory–Based Material Adoption
Bibliographic record
Abstract
Systemic functional linguistics (SFL) theory focuses on developing language learners’ meta-linguistic understanding of the interrelation among linguistic form (grammar/vocabulary), meaning, and context. Guided by SFL when using a mandatory textbook and open educational resources, this study investigates how exposure to this blended teaching and learning context may impact English-as-a-foreign-language (EFL) learners’ adjustment to materials used in their learning, as well as their learning practices. By drawing on the written documents of four students written, and on interviews conducted with these students over an academic semester in an EFL writing course, this qualitative study, through content analysis and discourse analysis, shows that the SFL theory-based material adoption did a good job of supporting EFL students in their internalization of language knowledge from both open educational resources and traditional textbooks, while also enabling students to use materials flexibly instead of passively following along with the content in the mandatory textbook. The flexibility of the students participating in the study was particularly reflected by their ability to construct principled knowledge informed by SFL and to independently apply such knowledge to effectively navigate literacy practices (e.g., critical construction and deconstruction of discourses).
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".