Constructively Aligned Assessment: An Integral Approach to Translation Teaching and Learning
Bibliographic record
Abstract
For the last 15 years, higher education has dramatically changed in terms of its mission and modes of delivery, involving many changes in how teachers approach course design and implementation, mainly because the final aim of learning is no longer the transmission of knowledge but the acquisition of competences for professional practice that promote graduates’ employability. One of the most affected processes has been evaluation, insofar as assessing these competences requires using strategies beyond the mere evaluation of declarative knowledge. Traditionally, evaluating in translation degrees has been said to be based on continuous assessment. However, the meaning and implications of ‘continuous assessment’ and its relation to ‘formative’ and ‘final’ assessment have often been misinterpreted as revealed in the literature. In this paper, we analyse the most common misconceptions in higher education assessment and, particularly, in translation teaching and learning. Furthermore, we present constructive alignment as a solid pedagogical framework for use in this field. Combining several formative methods and instruments is found to be most beneficial after reviewing the methods and instruments available and measuring the extent to which the intended learning outcomes were achieved as well as spotting individual learners’ needs. This paper emphasises the usefulness of continuous formative assessment as compared to continuous summative assessment, which measures the results of learning but does not act on the learning process.
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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 | 0.071 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".