The Study of Student Motivation on English Learning in Junior Middle School -- A Case Study of No.5 Middle School in Gejiu
Bibliographic record
Abstract
Motivation plays an important role in foreign language learning. Learning motivation is to promote and guide and maintain learning activities which have been conducted an internal strength or internal mechanism. Learning motivation once formed, the student will use an active learning attitude to learn, and express a keen interest in learning, and can focus attention in class to master knowledge. Through the study of the theory of modern education, this paper discusses the definition of motivation, types of motivation; the role of motivation in English learning are analyzed. The subjects in the thesis are Gejiu middle school students, and the author designed a questionnaire on English motivation. The purpose of the study was to find out the unfavorable factors. According to the results and the related theory, the author presents some suggestions to arouse the students’ English motivation and improve the efficiency of English learning and teaching in Junior Middle School. Among the suggestions, the implications by the study include that students need motivation to help them learning English, they should establish the right goal to enhance them learning English well. Meanwhile, as an organizer in teaching, teacher should pay more attention to communicative learning that can stimulate students to learn effectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".