Impact of Affective Factors on Senior High School Students with Low English Reading Ability
Bibliographic record
Abstract
In 2017, the Ministry of Education promulgated the General High School English Curriculum Standards (2017 Edition), which stressed the importance of affective factors in English learning. This study uses a combination of qualitative and quantitative research methods such as student questionnaires and interviews to explore the motivational function of affective factors and its negative influence upon English reading of 80 students with low English reading ability in a high school in Jiujiang. The results of the study show that reading anxiety is common among high school students, the internal motivation for English reading is weak and the external motivation is strong, the self-efficacy of reading is not clear and firm. The study further found that the great academic pressure and high school students’ self-awareness which are subject to external environment, language and other factors cause high school students’ anxiety, the international social environment and examination system have a large impact on high school student’ external motivation. In order to improve the English reading ability of high school students, this research tries to make corresponding suggestions for teachers, students and other aspects from the perspective of affective factors. As for the English teachers, they should set up diversified activities and increase the interest of teaching content in order to promote emotions with knowledge. As for the students, they should clarify their tasks to enhance learning motivation. The school should set up mental health courses and psychological counseling room and provide psychological training for teachers. Then, parents should try to communicate with students and learn to ask for help rather than paying too much attention to students’ performance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".