Bibliographic record
Abstract
What is intelligence? What do we know about knowledge? Are design and technological knowledge unique? Do different types of knowledge demand different organizations? How can we employ cognitive skills in the resolution of technological problems? This chapter provides an introduction to current theories of intelligence and knowledge with an emphasis on instructional organization. We will discuss learning theories and theories of cognition in Chapter VI. In the previous chapter, we acknowledged that despite the proliferation of communication and information technologies, communication skills for most people have atrophied. At the same time this proliferation of new technologies has created conditions for what we experience as information overload. For this reason, it is extremely important that teachers develop effective skills and techniques for the communication, organization, and presentation of information and knowledge. It is essential that teachers develop working understandings of current theories of knowledge and skills. Our understandings of technological knowledge and literacy along with the theories that we act on determine the way we teach about, through, and for design and technology. Current theories of intelligence, or cognitive pluralism, and the organization of knowledge are fundamental to effective instruction. This chapter builds on the basic communication and organization techniques provided in Chapter I. The effective organization of instruction requires the effective organization of knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".