Investigating the Effects of Dynamic Assessment on Chinese Undergraduates’ English Writing Performance in the Blended Learning Context
Bibliographic record
Abstract
Whereas the effectiveness of dynamic assessment has been investigated in multiple contexts, it has been under-investigated in the context of blended learning mode for writing performance. To address this gap, this study aims to explore the effects of dynamic assessment on Chinese undergraduates’ writing performance as overall writing performance and writing complexity, accuracy and fluency in the blended learning context. To this end, a quasi-experiment was carried out with two intact classes from a Chinese university, one being the control group (n=34) and the other experimental group (n=36). A 12-week intervention was conducted in English writing classes under the blended learning mode, with the experimental group receiving dynamic assessment while the control group having traditional static assessment. At the end of the experiment, six students attended a semi-structured interview. The findings revealed that the experimental group improved significantly in writing performance in terms of overall scores, lexical density, lexical sophistication, and accuracy. However, dynamic assessment had no significant effect on lexical diversity, syntactic complexity and fluency. Besides, the interview findings evidenced that the students held positive attitudes toward the use of dynamic assessment in English writing classes in the blending learning context. Implications for writing instruction and future research are discussed.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".