DESIGN LEARNING AND THE ASSESSMENT OF INSTRUCTION
Bibliographic record
Abstract
The accreditation of engineering design components of the curriculum is based upon a characterization of design as “… creative, iterative, and often open-ended …”1. The Universal Student Rating of Instruction (USRI) is based upon a composite of expectations including those involving: organization, detail in course outline, responses to questions, opportunities for assistance, evaluation methods, and perception of instruction. In this paper we examine the competing nature of these two characterizations. Even the most casual perusal of the questions posed by the USRI reveals that the premise underlying this instrument is one that clearly (albeit implicitly) points to a number of expectations. The nature of the subject matter, the nature of the learning process, the learning environment, the role of the student, that of the instructor, and that of the relationships between all of these are all assumed to benefit from adherence to a particular pre-ordained form. Specifically, the curriculum is perceived to consist of a body of facts, the learning process involves a transfer of these facts from instructor to student, the role of the student is passive, and responsibility for learning is the sole preserve of the instructor. The authors hold that this most sterile and simplistic set of expectations is fundamentally at odds with what should be happening in a design course. In this paper we juxtapose the nature of design and design learning with the teaching algorithm underlying the USRI.
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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 | 0.021 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".