Children's use of personal, social and material resources to solve a music notational task : a social constructivist perspective
Bibliographic record
Abstract
In this inquiry, I examined how young children use their personal, social and material resources to solve a music notational task. I asked 13 children, ages 5-9 to notate a song they learned the previous week, sing it back, explain what they did and then teach the song to a classmate the following week. I used Lightfoot and Davis' concept of portraiture as a qualitative research methodology to collect, code, analyze and interpret my data. Data included the children's invented notations and videotaped transcripts of their actions as they created their notations and taught the song to a classmate. Sociocultural Vygotskian developmental theory, activity theory and Bakhtin's dialogic theory provided the interpretive lens through which I examined how the children used their resources as mediational tools to complete the task. Findings revealed that children who had no previous music training used increasingly sophisticated representational strategies to notate a song, and that they were able to refine their notations when singing the song from their notation, teaching the song or when prompted by an adult or a peer. I concluded that the peer-peer situation was a motivating force for triggering a recursive process of reflections-on-actions and knowing-in-action. Classmates' questions, comments and their singing played a critical role in moving the children to modify their notations and their singing, verbal explanations and gesturing in ways they did not do alone or with me. Analysis of the children's notations, verbal explanations and teaching strategies provided insights not only into what they knew about music, but also their appropriation of the cultural conventions of writing and their aesthetic sensibilities, as gleaned from their choice of symbols, colours and how they presented their symbols on the page. Interviews with parents, teachers and school principal provided contextual background for interpreting the children's notations and how they approached the task. This study shows the value of adopting a social constructivist approach to teaching the language of music. It also demonstrates that researching the products and processes of children's invented notations from a social constructivist perspective enables more detailed portraits of children's musical and meta-cognitive understandings.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".