An Exploration of Motivational Strategies and Factors That Affect Strategies: A Case of Chinese EFL Teachers
Bibliographic record
Abstract
Based on Self-determination theory, learners’ motivation can be enhanced when the psychological needs—competence, autonomy, and relatedness—are satisfied (Ryan & Deci, 2017). In English as a second language classrooms, teachers can play an important role in this; however, their motivational strategies may be influenced by their beliefs and contextual factors (Hornstra, Mansfield, van der Veen, Peetsma, & Volman, 2015). In this case study, six EFL classrooms in a public school in Northwest China were observed over the period of five weeks. The teachers were interviewed after each observation and at the end of the observation period to explore the relationships among factors that may affect the teachers’ use of motivational strategies, namely teacher beliefs and pressure from “above” and from “below”. The data were analyzed qualitatively using the coding method. The findings revealed a discrepancy between teacher beliefs and motivational practices. All of the teachers regularly exercised controlling strategies regardless of their beliefs in the value of motivation. Nevertheless, relationships between motivational practices and contextual factors were found. These findings suggest the needs for effective teacher professional development on the use of motivational strategies to enhance intrinsic motivation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".