Using Team Building-based Instruction to Foster EFL Learners’ Motivation under the Context of Education 4.0
Bibliographic record
Abstract
Under the context of Education 4.0, motivation for language learning takes on a range of meanings and implications that are inextricably bound up with more socially meaningful contexts. Hence, the quality of motivation matters. It is imperative that EFL education make adjustments to strengthen learners’ motivation to achieve the desired learning outcomes and develop skills required in Education 4.0 era. Drawing on interdisciplinary knowledge of applied linguistics and organizational behavioral science, the current research explored the effects of team building-based instruction on EFL students’ motivation. The participants of the study were 84 undergraduate EFL learners at a Chinese university. Questionnaire and open-ended questions were employed to collect data. The results showed that, on average, participants reported high mean values for each of the motivation components, indicating that team building-based instruction played a positive role in motivating most students in the course. Nonetheless, students’ responses towards the components vary significantly, especially for the ‘interest’ scale and ‘usefulness’ scale. Several motivating factors that led to students’ motivation to engage in the course were identified: group dynamics, project design, technology, and assessment. The research concluded that team building-based instruction should take both linguistic factors and non-linguistic factors into consideration to fully motivate and engage students. In the end, the researcher proposed implications for motivational pedagogy and practices.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".