Bibliographic record
Abstract
Is there a link between emotion and memory and, if so, can that link be leveraged in the language learning environment to facilitate the consolidation in memory of lexical semantic items in an L2? L2 subjects were given a list of vocabulary items to learn, including a translation of the vocabulary items. Next they were shown a reading passage which used the vocabulary in context. The participants twice heard an audio recording of the passage while they followed along with the text. Participants were divided into an experimental group, in which the audio recording accentuated the emotional content of the story, and a control group, in which the recording was presented in a neutral tone of voice. A post-test, then delayed post-test, assessed how well the subjects remembered the items. The hypothesis was that the style of audio reading accompanying the written text (emotional or neutral) would influence the results on a post-test and a delayed post-test on the vocabulary used in the passage. However, the hypothesis was not supported. Results are discussed in terms of other learning benefits to enhancing the emotional content of the text, which the students in the experimental group found to be more engaging and interesting. Furthermore, methodological issues are examined.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.482 | 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; both teacher heads agree on what is shown here.
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".