Learning-Oriented Assessment (LOA) Implementation in EFL Tertiary Contexts: Towards a More Task-based Learning (TBL) Environment
Bibliographic record
Abstract
Over the past decade, learning-oriented assessment (LOA) has gained increasing attention as an emerging approach to classroom-based assessment. LOA prioritises learning and focuses on engaging learners actively in assessment and feedback activities. To enhance the learning environment in higher education, it is crucial for teachers of English as a foreign language (EFL) to be aware of and implement innovative assessment methods that support student learning, such as LOA. The purpose of this mixed-methods study was to investigate the knowledge, use, and challenges of LOA in tertiary contexts. A total of (93) male and female EFL teachers teaching in tertiary education participated in the study. An adaptation of The Teachers' Learning-Oriented Assessment Questionnaire survey (Alsowat, 2022) and items from semi-structured interviews (Fazel & Ali, 2022) were used to collect the data for this study. The findings of this study show that EFL teachers had good knowledge of LOA concepts but suggest that they need further focused training on the implementation of LOA. The study also shows that the teachers faced pedagogical, practical, attitudinal, and institutional challenges that prevented the better implementation of LOA practices.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".