Best Mentoring Practices of Design Education at the Design High School Level and Instructional Resources
Bibliographic record
Abstract
This article examines education practices intended to maximize the effectiveness of education in a design high school. Consistent with the requirements of national curricula, design high schools seek to optimize formal education by developing programs that provide a framework for teaching students the skills and knowledge needed to become active participants in planning and shaping their world. Numerous studies have concluded that design-based learning and mentoring relationships are a significant factor in succession planning, career development, and skill development. To research the role and value of design-based learning and mentoring relationship, the author recorded observations of visiting speakers from various disciplines who presented design-based learning experiences and mentoring system to design education classes at The Design High School in Los Angeles. Because the purpose of this study was to investigate educational practices of a design high school, to explore the role and importance of design-based learning and mentoring relationship, and to generate theoretical concepts of the ways in which such a high school is structured, a grounded theory approach was used to guide and interpret the collection and analysis of the data. Three primary methods were used for data collection: document analysis, observation, and interviews. The finding shows that design education can enhance learning in K-12 students. The author concludes by suggesting educational strategies that could be incorporated into a design high school curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".