A preliminary analysis of the effects of listener-speaker and speaker-listener sequences on learning Chinese as a foreign language
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of different instructional sequences when learning Chinese as a foreign language. Six English-speaking individuals (two male adults, three female adults, and one male student) participated in this study. Combined listener instruction consisted of auditory-visual matching for textual stimuli (i.e., “Point to [name of Chinese character]) and visual-visual matching for textual and picture stimuli (i.e., “Match [name of Chinese character]). Combined speaker instruction consisted of textual and tacting responding in Chinese. The researcher taught six stimulus sets (a total of 30 Chinese vocabularies), with each set randomly assigned to either the Listener-Speaker or Speaker-Listener instructional sequence. Results indicated that four participants required fewer trials to criterion with Speaker-Listener instruction; the other two participants’ learning trials between the two instructional methods were comparable. Five participants demonstrated greater emergent listener responses for stimulus sets taught with combined speaker instruction than emergent speaker responses for stimulus sets taught with combined listener instruction. One participant’s emergent speaker and listener responses were at a high level and not differentiated between the two instructional methods.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".