Neither Born nor Made, but Socially Constructed: Promoting Interactive Learning in an Online Environment
Bibliographic record
Abstract
The social constructivist approach to translator training represents a clear statement on the importance of directing university teachers towards a student-centered, learning centered mode. By acknowledging the fundamental role of Vygotsky in determining his approach, Kiraly brought translator training in line with the established, broad-based humanistic approach to Foreign Language Learning; by drawing on Stevick and Schön, among others, he made this debt explicit. In this article, we apply the social constructivist approach through blended e-learning environments in courses offered to final year undergraduate students of translation. Our objective is to determine the success of combining technology and social constructivist pedagogy in promoting effective learner-centered learning. In Kiraly’s terms, we have “scaffolded” our instruction by applying instruments such as rating scales of criterion-referenced descriptors; textual and visual aids; and learner generated corpora. Our qualitative data is drawn from a variety of interactive formats: whole group online discussions, team-based online discussions, e-mail exchanges and specific “reflective” activities. We conclude that the quality of the “scaffolding” is essential to success in stimulating learning and that the e-learning environment is an excellent medium for the social constructivist approach.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".