In Search for Implementing Learning-Oriented Assessment in an EFL Setting
Bibliographic record
Abstract
Learning-oriented assessment (LOA) is a kind of assessment which is used to promote and stimulate learning, and improve instruction and teaching. Learning-oriented assessment is viewed as crucial to language assessment. Therefore, this paper is an attempt to explore whether teachers support the notion of using LOA in an ELF setting or not ,why do teacher support the use of it, how can LOA be implemented, and what are the possible challenges that might be encountered in implementing LOA. 25 teachers were surveyed to answer the six open-ended questions raised by the study. The findings showed that all teachers are in favour of LOA and they support its use and implementation because they believed that it could help learners to learn better and promote active learning. They believed that the best way for implementing LOA is through training both of teachers and students in using self-evaluation, peer-assessment and portfolio assessment techniques and principles. Moreover, assessment tasks should be made as learning tasks and should be well-aligned with the curriculum objectives and goals, and timely feedback should be given to students to scaffold their 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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".