Learning multiword items through dictation and dictogloss: How task performance predicts learning outcomes
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
This article reports a quasi-experimental study which compared the effectiveness for multiword item learning of three listening-based activities: dictation, dictogloss, and answering text comprehension questions. In a dictation, students write down segments of text immediately after listening to them, whereas in dictogloss students try to reconstruct the text from memory. Chinese learners of English ( N = 142) first engaged in one of the three activities, then received the transcript of the text and used a different colour to make corrections to what they had written. The learners were given an unannounced immediate and a two-week delayed posttest concerning 10 expressions from the text. Both dictation and dictogloss led to better scores than answering comprehension questions in the immediate posttest, but the advantage diminished in the delayed test, and this most markedly so for the dictation activity. Items that were successfully retrieved during the text-reconstruction stage of the dictogloss activity rather than rectified by the students afterwards with the aid of the transcript stood the best chance of being recalled in the posttests. This suggests that dictogloss could be made more effective if it were implemented in ways that promote accurate retrieval at the text-reconstruction stage.
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 | 0.004 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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