An Action Study on Morning Reading Activity on Campus Among College English Learners
Bibliographic record
Abstract
Having gone through fierce competition and standardized university entrance exam, most of the undergraduates are conversant at English reading and listening, but by and large weak at speaking and writing. At present, there is still considerable room for improvement in English speaking training in the university of China. For instance, students in mounting numbers become nonchalant about improving their speaking on account of the fear of making mistakes, the pronunciation mistakes “fossilization”, the lack of intrinsic motivation and confidence in expression, the scarcity of professional oral English trainees and all-in English environment for students to immerge themselves in. However, as is universally acknowledged, speaking skill should be given more priority in an era of globalization and intercultural communication which is gaining paces. Thus, teachers should try their utmost to prepare students better for their future study and provide them with more opportunities to see and explore the world with no obstacles in language. As the online study goes viral and the vibrant and fast upgradation and innovation of learning method speed up, the factors contributing to student’s learning motivation have subjected to great change. Thus, it is necessary to analyze and delve into the current research condition and future possibilities. Besides, in general, the study on the English learning motivation of undergraduates in China still stays at superficial level and its major contents and principles are applied and fabricated, leading to the lack of features of the time and creativity. This paper is going to put emphasis on and analyze “Morning Reading Activity” on campus, focusing on improving speaking ability of university students based on the past learning and teaching experience and researches. This article is designed to expound on English speaking learning and training in details by the following two questions: (1) How to apply affective filter hypothesis put forward by Steven Krashen in speaking activity? (2) How to exercise motivation principle in the activity?
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".