A Comparison of the Learning Efficiency of Business English between the Blended Teaching and Conventional Teaching for College Students
Bibliographic record
Abstract
Over the last thirty to forty years, as technology and supplementary broadcast networks have advanced, an increasing number of teachers have entered the classroom with new perspectives, such as providing students with resources and an environment that adheres to the blended teaching style. Learning can take place whenever and wherever it is desired with a blended teaching approach. The primary goal of this study was to investigate whether the blended teaching strategies and materials or the traditional teaching method in the Business English class increase students' learning satisfaction and academic achievement. Participants in this study included 56 undergraduate students majoring in Applied Foreign Languages in central Taiwan. Business English ESP courses were offered during the 108 and 109 academic years. The four-point Likert scale questionnaire was distributed after students took the course. Moreover, in-person interviews and class observations were also focused to reveal overall students’ learning efficiency and perceptions on the preferences of teaching techniques in Business English and their learning satisfaction. As a result, students considered blended teaching an effective teaching strategy that increased their learning motivation with additional instructional activities. In addition, findings demonstrated that both handwritten exams (associated with traditional teaching methods) and interactive answering Apps (associated with blended teaching methods) work effectively based on different teaching methods. The outcomes of this study suggest that the majority of students have a positive attitude toward blended learning in the ESP program of business English and that their academic performance has obviously improved.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".