Teaching to Innovate with Taskscape
Bibliographic record
Abstract
Teaching innovation requires effective methods to encourage creative thinking and design problem-solving. One such method is taskscape, derived from Ingold’s (1993) concept of tasks within a contextual "landscape." This framework emphasizes analyzing human and non-human interactions across tasks to uncover design complexities. Taskscape differs from journey mapping by focusing on a network of interactions rather than sequential events. Applied in educational settings, taskscape was used in projects at two universities involving diverse student groups. Students developed taskscapes and design criteria for projects like summer sportswear for seated clients and crime prevention. While some initially struggled with the concept, taskscape ultimately facilitated deeper ideation and refined designs, with tools like personal logbooks aiding understanding. Students found it easier to derive design criteria after taskscape analysis, identifying core values and priorities. The results highlight taskscape’s potential as a systematic approach in design education, warranting further research into its applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".