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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".