The Design Charrette in the Classrooms as a Method for Outcomes-based Action Learning in IS Design
Bibliographic record
Abstract
This paper explores the adaptation of a traditional studio technique in architecture – the Design Charrette – to the teaching of New Media design in a large information systems program. The Design Charrette is an intense, collaborative session in which a group of designers drafts a solution to a design problem in a time critical environment. The Design Charrette offers learning opportunities in a very condensed period that are difficult to achieve in the classroom by other means and we have adapted its application from the architecture studio to for New Media instruction. The teaching of information systems has tended to rely heavily on conventional pedagogical approaches although there is growing recognition of the importance of experiential and applied learning. Ac- creditation standards have also placed added emphasis on outcome-based learning and encouraged more mindfulness concerning instructional design (Lee et. al., 1995; McGourty et. al., 1999). As a consequence, more emphasis on experiential learning has emerged in recent years. Architecture has long been used as a reference discipline for Information Systems and much of the language used in information systems design is drawn from architectural discourse. However, while architectural design combines attention to history and form as well as function, most information systems design is driven by functional considerations. Teaching New Media design, like architecture, demands at- tention to the conceptual and aesthetic as well as the functional design perspectives and presents particular challenges. The Design Charrette, a short but intense effort to solve an architectural problem is an outcome focused form of action learning that has enormous potential to enrich the teaching of New Media design, in particular, and information systems in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".