Hong Kong EFL Learners’ Perception on the Relationship between Blended Learning and Critical Thinking: In an Era of AI
Bibliographic record
Abstract
With the rapid advancement of educational technology, the blended learning model has gained increasing prominence as an effective approach to modern education. The emergence of ChatGPT, developed by OpenAI, is further expected to serve as a powerful tool for facilitating personalized learning and enhancing student engagement in blended learning environments (Malhan et al., 2024). Previous research has demonstrated that blended learning can effectively foster students’ practical competencies, including critical thinking, problem-solving, and literacy skills. The present study investigates Hong Kong EFL learners’ perceptions of blended learning, with a particular focus on whether this learning model enhances their critical thinking skills. It also explores the key factors that contribute to the development of critical thinking in blended learning contexts. A mixed-methods design was employed, combining quantitative and qualitative approaches. Specifically, 65 Hong Kong EFL learners completed a questionnaire, and 4 participants were subsequently invited to take part in semi-structured interviews. The findings indicate that blended learning has a positive impact on the development of EFL learners’ critical thinking. Moreover, factors such as classroom discussion, learner–teacher interaction, instructional materials, learning topics, and teaching strategies were identified as significant contributors to this improvement. These results offer pedagogical implications for both EFL learners and educators seeking to promote critical thinking in blended learning environments. This study also lays the groundwork for future experimental research aimed at examining the extent to which blended learning influences Hong Kong EFL learners’ critical thinking development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".