The Effects of Mobile Blended Active Language Learning on the English Critical Reading Skills of High School Students in Thailand
Bibliographic record
Abstract
Due to the tumultuous COVID-19 pandemic, the use of advanced technology in language education is currently on the rise, with English education being no exception. Simultaneously, the advancement and expansion of technology provide English as a foreign language students with multiple channels and opportunities to reinforce the four skills of English (reading, writing, speaking, and listening) to varying degrees both inside and outside the classroom. While previous studies have highlighted the feasibility and sustainability of blended learning in facilitating English skills, few studies have investigated the impact of mobile blended active language learning (MBALL) on promoting the English critical reading skills of Thai high school students. In hopes of filling this gap, the present study used both quantitative and qualitative methodologies to investigate the effects of MBALL on improving Thai high school students' English critical reading skills and their opinions on the use of MBALL. Pre- and post-tests were used to compare the students' critical reading scores before and after the MBALL implementation. A questionnaire was used to determine the students’ opinions on the use of MBALL, and individual semi-structured interviews were employed to obtain more-detailed information. The results of the tests revealed that the Thai high school students' English critical reading skills had improved after implementation of the MBALL curriculum. Furthermore, the findings of the questionnaire and interviews suggested that the Thai high school students were enthusiastic about the use of MBALL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".