Bibliographic record
Abstract
What started off as a field of interest in studies revolving around Conversation Analysis in the late 1970s (Sacks et al.), has experienced an increasing interest in research on second language learning in institutional settings – repair. Many studies have found that repair is not exclusively targeted at error correction but has been shown to fulfill discourse-related functions as well (e.g. Liebscher and Dailey-O’Cain; Razfar). However, despite its crucial role in institutional settings, assessment situations have been largely neglected in this research. This study aims to fill this gap. It examines how repair is done amongst the instructor and beginner students of German during oral exams. The instances of repair are categorized as self- or other-initiated self-repair (Schegloff et al.). Self-initiated repair is described following the categories identified by Levelt. Nine beginner learners of German, who have previously shown different levels of learning success, were video-recorded during their oral exams. Using conversational analyst methods, this study aims at identifying 1) What forms of repair occur and which functions they fulfil, and 2) How successful repairs are depending on the learners’ level of success. While self-initiated self-repair and error corrections are the most dominant form, the findings also indicate that the oral exam setting elicits economic and pragmatic functions as well and further sharpens the learners’ self-perception of their own performance depending on their success level, which influences the ability to spot and repair trouble sources. Pedagogical implications of these findings will be discussed.
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.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".