Використання інтересу студентів до музики задля мотивації вивчення іноземної мови
Bibliographic record
Abstract
Література 1. Cole, K. 2011. Brain-based-research music advocacy. 2. Music Educators Journal 98 (1): 26–29. Cummins, J. 1981.The role of primary languagedevelopment in promoting educational success for language minority students. In Schooling and language minority students:A theoretical framework, ed. Office of Bilingual Bicultural Education, California State Department of Education, 3–49. Los Angeles: Evaluation, Dissemination and Assessment Center, California State University. 3. Dalton, C., and O. Lewes. 2015. Utilizing karaoke in the ESL classroom:The Beatles. English inTexas 45 (1): 32–36. 4. Deutsch, D. 2010. Speaking in tones. Scientific American Mind 21 (3): 36–43. 5. Ho, P., J. C. I.Tsao, L. Bloch, and L. K. Zeltzer. 2011. The impact of group drumming on social-emotional behavior in low-income children. Evidence-Based Complementary and Alternative Medicine 2011: 1–14. 6. Lapo, N. 2016. SIOP Lesson PlanTemplate #2: Soundtrack to my life. Unpublished academic assignment for CIL505, ESL Methods. Chicago, IL: National Louis University. 7. Lems, K., L. D. Miller, andT. M. Soro. 2017. Building literacy with English language learners:Insights from linguistics. 2nd ed. NewYork: Guilford Press. \n8. Leutwyler, K. 2001. Exploring the musical brain. Scientific American, January 23, 2–4. http://www. scientificamerican.com/article/exploring-the- musical-bra/ 9. Maess, B., S. Koelsch,T. Gunter, and A. Friederici. 2001. Musical syntax is processed in Broca’s area: An MEG study. Nature Neuroscience 4 (5): 540–545. Martin. 2013. How to teach English infographic. KBlog. https://www.kaplaninternational.com/blog/how-to-teach-english-kaplaninfographic 10. Mayo, L. H., M. Florentine, and S. Buus. 1997.Age of secondlanguage acquisition and perception of speech in noise. Journal of Speech,Language,and Hearing Research 40 (3): 686–693. 11. McGowan, K. 2008. Music, memory, and learning. Meeting report from the Neurosciences and Music III—Disorders and Plasticity conference at McGill University, Montreal, Quebec, June 25–28. https:// www.nyas.org/ebriefings/songs-of-experience/ 12. Moreno, S. 2009. Can music influence language and cognition? Contemporary Music Review 28 (3): 329–345. 12. Murphey, T. 1990. Song and music in language learning: An analysis of pop song lyrics and the use of song and music in teaching English to speakers of other languages. New York: Peter Lang. 13. Posner, M., M. K. Rothbart, B. E. Sheese, and J. Kieras. 2008. How arts training influences cognition. Arts and Cognition Monograph, 1–10. NewYork: Dana Foundation. TierneyA., J. Krizman, E. Skoe, K. Johnston, and N. Kraus. 2013. 14. High school music classes enhance the neural processing of speech. Frontiers in Psychology 4: 1–7. Weissman, M. J. 2005. Sing about Martin.YouTube video. https://www.youtube.com/watch?v=dF3hPT3PDI 15. Kristin Lems, Leah D. Miller, Tenena M. Soro. 2017. Building Literacy with English Language Learners, Second Edition: Insights from Linguistics. Guilford Publications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".