Teacher-Student Interaction for English-Medium Instruction (EMI) Content and Language Learning and the Effects of Implementing Multimodal Input of Classroom Interaction: University Students’ Perceptions
Bibliographic record
Abstract
The discourse of interaction, Initiation-Response-Feedback (I-R-F) gained its popularity by fostering discussion between the teacher and learners but received critics for developing interactive communication in a controlled manner. With an attempt to provide pragmatic implications for EMI instructors, the study probed into perceptions of students (n=42) from different majors on how two pedagogical approaches (i.e., the I-R-F and the multimodal input of classroom interaction) differed in one EMI Cross-cultural Communication course in one middle-ranked university in Taiwan. The comparative, quasi-experiment research firstly investigated these students’ perceptions of teacher-student interaction for EMI content and language learning and secondly compared their perceptions on the implementation of the multimodal input of classroom interaction against the conventional baseline, the I-R-F. Both quantitative (i.e., survey) and qualitative (i.e., post-lesson student reflection journals and audio recordings) research methods were used. The survey results showed that these students were inclined to engage in extensive and substantial verbal output, expressed the importance of teacher-student interaction for learning the content of the course, and expected chances of lengthy verbal output and corrective feedback from the instructor. The results from students’ journals yielded that the instructor’s use of the multimodal input of classroom interaction significantly outperformed the use of the I-R-F in the categories of effectiveness, the level of student engagement, and the effectiveness of helping them learn the content. Ultimately, it was found that the use of the multimodal input of classroom interaction had triggered their higher-order of cognitive processing more (i.e., analyzing, evaluating, and creating).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".