Using Pimsleur for the self-regulated learning of spoken phrases in Brazilian Portuguese: a case study
Bibliographic record
Abstract
This case study examines the self-regulated use of Pimsleur, a Language Learning Platform (LLP), as a tool to aid in the acquisition of spoken phrases in Brazilian Portuguese (BP) and their related pronunciation. Like many LLPs, research on Pimsleur is scant, as is the number of studies done on BP compared to other major languages. This study aims to address this gap in research. The participant-researcher completed the Pimsleur program through daily study over a 10-week period, after which quantitative data were collected through a post-test and delayed post-test. The results showed that Pimsleur contributed to the learning of the target phrases in the short term and that the participant produced speech that was highly intelligible, moderately comprehensible, but heavily accented. This shows that Pimsleur can be an effective tool for developing spoken BP and can offer a unique learning experience with its methodology and mobile capability that mitigates some of the issues around mobile-assisted language learning (e.g. app attrition).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".