A Case Study: Investigating High School English Student Engagement in Language Learning Through YouTube Music Videos
Bibliographic record
Abstract
This case study investigated high school student engagement in acquiring second language knowledge through YouTube music videos. A Chinese English learner who had lived in the United States for a year developed fundamental language skills but was hesitant to engage in language learning at the start of the project. Data collection was conducted over 10 weeks and involved semi-structured interviews, classroom observations, and in-class artifacts related to YouTube music learning activities. The findings indicated that the learner engaged with YouTube music videos from four perspectives: behavioral, emotional, social, and cognitive. From a behavioral perspective, singing recurring routines continuously engages the learner by promoting participation in language development. From an emotional and social perspective, music genres engage the learner through high affective input and peer interaction, facilitating the acquisition of cultural knowledge and daily communicative skill. From a cognitive perspective, multimodal features (e.g., lyrics and rhythm) stimulate the learner’s cognition by providing mnemonic aids that enhance memory for vocabulary and pronunciation competencies. Theoretical and pedagogical implications are provided for English language learning contexts, shedding light on the conceptual development of student engagement, multilingual teacher education, and multimodal learning. This study identifies future research trends in the instructional use of YouTube videos from multiple perspectives, including the transfer of working memory to long-term memory, digital multimodal composing (DMC), and generative AI.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".