An Attempted Evaluation of Compuer Assisted Language Learning in China EVALUATION DE L'APPRENTISSAGE DES LANGUES ASSISTE PAR ORDINATEUR EN CHINE
Bibliographic record
Abstract
With the rapid development of computer technology and further educational reform in China, more and more colleges or universities start to employ computers for foreign language teaching and learning. This paper, thus, first briefly introduces the evolution of CALL, then examines the constructive value and drawbacks of computer-assisted language teaching and learning, and finally emphasizes some rational cognition in implications and conclusion. Key words: review, benefits, drawbacks, rational recognition Resume: Avec le developpement rapide de la technologie d’ordinateur et la reforme educative approfondie de Chine, de plus en plus de lycees ou d’universites commencent a employer l’ordinateur dans l’enseignement-apprentissage des langues etrangeres. L’article present, tout d’abord, introduit brievement l’evolution de l’ALAO(l’Apprentissage des langes assiste par Ordinateur), et puis examine la valeur constructive et les desavantages de l’enseignement-apprentissage des langes assiste par ordinateur. En fin de compte, l’article insiste sur des cognitions rationnelles dans les implictions et la conclusion. Mots-Cles: ALAO, retrospection, benefices, desavantages, recognition rationnelle
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".