The Application of Teacher-Student Collaborative Assessment (TSCA) in Private Colleges
Bibliographic record
Abstract
College students’ writing abilities were slowly progressing in the Chinese context. This problem was mainly due to the over-dependence on the teacher’s feedback and the confusion about the writing assessment criteria. This resulted in the low ability of students to self-evaluate their essays. Thus, teacher-student collaborative assessment (TSCA) was introduced to guide students in finding the problems that existed in their peers’ writing which in reverse could also help them detect similar errors in their writing. This study aimed to explore the practicality of the TSCA principle in private colleges with students’ English proficiency below the average. Altogether 42 sophomores majoring in English were selected as the participants. They had taken English writing courses for one year and had some foundation. The questionnaire was given before the course to acquire students’ attitudes towards the TSCA principle. Then students’ writing scores for four assignments were collected during the implementation of the TSCA principle. The results showed that it was applicable to implement the TSCA principle in private colleges for lower-proficient students with their high willingness to this principle. Hopefully, students could have a general idea of writing assessment criteria and do self-evaluation in later autonomous learning.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".