Speaker
Abstract section 1: Relevance
In the digital age, many EFL learners struggle to balance listening and processing (Lin, 2006). Traditional note-taking relies heavily on translation, suggesting that frequent cognitive code-switching can strain working memory (Piolat, Olive, & Kellogg, 2005). While note-taking is known to improve memory (Tsai & Wu, 2010), specific language transitions (L1 vs. L2) in handwritten notes have not been well studied due to the difficulty of manual data entry. This study addresses this gap by using generative AI to digitize and classify students' notes, providing foundational insights into bilingual cognitive processing and offering teachers a data-driven approach to improving listening strategies.
Abstract section 4: Outcomes/results
Preliminary findings suggest that students often resort to Katakana or Japanese when cognitive load exceeds working memory capacity. The AI analysis reveals distinct patterns: "translators" (L1 heavy), "recorders" (Dictation-focused), and "visualizers" (symbol/image heavy). The study identifies that shifting from L1-based translation to L2 or symbolic note-taking correlates with higher listening engagement. Results highlight the English teacher's role in moving students away from "translation-as-listening" toward "processing-as-listening," using AI-generated feedback to visualize the student's own thought processes for self-reflection.
Abstract section 3: Content/method
In this study, handwritten notes were collected from first-year students. Generative AI was used to read the images and convert them into text. The data was then quantitatively analyzed to calculate the proportion of English, Japanese, and non-verbal symbols (e.g., diagrams, arrows). These proportions were correlated with the total amount of notes (number of words/characters) and student survey data regarding listening awareness and difficulty.
Abstract section 2: Contribution/research questions
How does the linguistic composition (English, Japanese, or symbols/images) of student notes correlate with their self-assessed listening proficiency and learning attitudes?
How do note-taking volume and language ratios evolve over multiple sessions?
To what extent does L1 usage in notes indicate cognitive code-switching during L2 processing, and how can instructors guide students toward more effective, comprehension-oriented note-taking strategies?
Abstract section 5: References
Ipek, H. (2018). The Role of Note-taking in Listening Comprehension. Journal of Education and Training Studies.
Lin, M. C. (2006). The effects of note-taking, memory, and rate of presentation on EFL
learners' listening comprehension. La Sierra University
Piolat, A., Olive, T., & Kellogg, R. T. (2005). Cognitive effort during note taking. Applied cognitive psychology, 19(3), 291-312.
Tsai-Fu, T. S. A. I., & Wu, Y. (2010). Effects of Note-Taking Instruction and Note-Taking Languages on College EFL Students' Listening Comprehension. New Horizons in Education, 58(1), 120-132.
| Title | AI Analysis of Note-taking and Code-switching in Listening |
|---|---|
| Teaching Context | College and university education |