Learning multiword items through dictation and dictogloss: How task performance predicts learning outcomes
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.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".