In Search for Implementing Learning-Oriented Assessment in an EFL Setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it