Bibliographic record
Abstract
One of the most used and abused approaches to technology studies in the schools is creative design and technological problem-solving. Current research suggests that it is not clear what students learn, if anything, in many creative design and technological problem-solving activities. Recalling the previous chapters, it is not enough to merely involve students in activities and problems. Emotions, knowledge, and skills must be articulated, organized, and demonstrated. Inferences from mistakes and successes must be drawn. Procedures must be practiced. One of the reasons that creative design and technological problem-solving activities are often without adequate results is that technology teachers tend to take creativity, design and problem- solving for granted. We assume that creativity, design, and problem-solving are automatic components of what we practice in technology studies. However, little is automatic in education. There is more to design and problem-solving than learning methods and resolving technical problems. In this chapter, current research is brought to bear on creative design, ingenuity, and technological problem-solving. In technology studies, one of our missions is to demystify the processes and products of design and technology. It is not enough to merely teach students to express their creativity, design or solve problems. We use the processes of creative design and problem-solving to disclose self-knowledge and feelings as well as the cultural and material conditions of subsistence, work, and home life. It is relatively easy to say this is the case. What remains is for us to describe how technology teachers can derive knowledge and feelings from technologies. How does doing lead to knowing? This chapter explains eleven methods of disclosive analysis for teachers to use with their students to demystify the processes and products of design and technology. The chapter concludes with an explanation of design briefs, an essential tool for engaging students in design and problem-solving.Request access from your librarian to read this chapter's full text.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".