Constructively Aligned Assessment: An Integral Approach to Translation Teaching and Learning
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
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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