Integrating Digital Reading Module to Enhance Computational Thinking Skills via Lesson Study for EFL Beginners: Case Study of a Public Senior High School in Indonesia
Bibliographic record
Abstract
Research on the integration of computational thinking skills into English language teaching, especially as a digital module, in the EFL context is limited. Moreover, the adoption of locally based cultural materials in this integration is extremely rare. To address the gaps, this mixed methods sequential explanatory study explores the effectiveness of a digital reading module focused on local female heroes in enhancing computational thinking skills among EFL students in Indonesia. Quantitative data were obtained from a pretest and a posttest administered to 30 secondary school students, determined through power analysis for sample size and recruited using random sampling. The results indicate a significant improvement in students' computational thinking, with an N-Gain score of 71.87% (High). ll participants were interviewed to gain a deeper understanding of the results through a case study analysis The qualitative phase, which utilized thematic analysis, reveals that the digital reading module not only enhanced students' problem-solving abilities and logical reasoning. Furthermore, it demonstrated its effectiveness in helping students apply computational concepts to real-world scenarios. Participants also reported positive experiences with the lesson study approach, highlighting its role in refining the module and aligning it with educational objectives. Qualitative findings thus support quantitative results. This suggests that culturally relevant digital modules, combined with collaborative professional development strategies like lesson study, can effectively improve computational thinking skills in EFL contexts. This study contributes to the broader understanding of how digital tools and pedagogical frameworks can be adapted to meet the needs of diverse educational environments
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".