Developing an Instructional Innovation Model for Teaching English Tense Structures: A Grounded Theory Study of Teachers’ Classroom Practice and Perceptions
Bibliographic record
Abstract
Thai EFL learners consistently struggle with English verb tense structures due to fundamental linguistic differences between Thai and English grammar systems, compounded by traditional grammar-translation teaching methods that emphasize rote memorization over meaningful application. Teachers in resource-limited schools lack effective pedagogical frameworks and professional development opportunities to address these persistent challenges in tense instruction. This study aimed to accomplish the following goals: a) identify current pedagogical practices and challenges in teaching English tense structures in small Thai schools, b) examine how teachers implement a newly developed instructional innovation, c) explore teachers’ perceptions and experiences of using the innovation, and (d) develop an emergent, replicable teaching model grounded in actual classroom practice. Drawing on constructivist and sociocultural theories, cognitive load theory, and communicative grammar instruction, this study situated instructional innovation as a means to bridge rule-based and meaning-focused approaches in EFL grammar teaching. A grounded theory qualitative design was employed. Four English teachers from resource-limited primary schools participated in pre- and post-training observations, focus groups, and semi-structured interviews. Open, axial, and selective coding of field notes and transcripts generated the instructional model. The study revealed the EPIRIS model—Elicitation of Knowledge; Presentation; Innovation Use; Retention Practice; Interaction; Summary and Assessment—which organizes effective strategies for teaching tense structures with the innovation. Teachers reported increased confidence, student engagement, and memory retention, despite challenges of time management and mixed-ability classes. The model offers a practical roadmap for EFL teachers in similar contexts and informs professional development programs by demonstrating how multimodal, student-centered innovations can be systematically integrated.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".