Bibliographic record
Abstract
Microcomputer applications to fine arts areas of education have received limited attention although the need for interdisciplinary education made evident in a substantial body of literature would seem to make such applications desirable. The use of microcomputers as creative media for the composition of poetry, art images, and music by grade four students at an elementary school in British Columbia, Canada was observed and analyzed in order to discern what actually happened as a result of the juxtaposition of microcomputers and creative aspects of fine arts. Ethnographic research methodology allowed the classroom teacher to conduct the study in a participant-observer role throughout the 1988-1989 school year. As a side aspect to the study, it was observed that students developed problem solving strategies that involved assessments and value judgements which encouraged those students to accept responsibility for their own learning. Word processed poetry engendered visual awareness that promoted extensive editing and proofreading and stimulated exploration of visual presentations in the genre of concrete poetry. Art images of nonrepresentational and abstract styles predominated because microcomputer capabilities supported such compositions and allowed students to experience satisfaction in their work regardless of their personally perceived proclivities toward portrayal in realistic style. The use of microcomputers facilitated image processing: the explorations of single ideas that resulted in the creation of series of related images. The students revealed developmental stages in music composition approaches and perceptions by the manner in which they structured sound into music. The students integrated concepts and techniques that involved poetry, art, and music into single works and thus demonstrated associative thought processing skills. Microcomputers used as creative media in the fine arts areas of poetry, art, and music enabled unique learning outcomes, provided a previously unavailable means whereby the developmental stages of child music composition were able to be observed, and constantly allowed students to be simultaneously creators and observers of their own work. The students were thus in position to concurrently recognize and respond to artistic form: a position in which aesthetic experiences are possible.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".