Success factors in the realization of large ice projects in education
Bibliographic record
Abstract
There has been a long tradition in making ice structures, but the development of technical improvements for making ice buildings is a new field with just a handful of researchers. Most of the projects were realized by professors in cooperation with their students as part of their education in architecture and civil engineering. The following professors have realized ice projects in this setting: Heinz Isler realized some experiments since the 1950s; Tsutomu Kokawa created in the past three decades several ice domes in the north of Japan with a span up to 25 m; Lancelot Coar realized a number of fabric formed ice shell structures including fiberglass bars and hanging fabric as a mold for an ice shell in 2011 and in 2015 he produced an fabric-formed ice origami structure in cooperation with MIT (Caitlin Mueller) and VUB (Lars de Laet). Arno Pronk realized several ice projects such as the 2004 artificially cooled igloo, in 2014 and 2015 dome structures with an inflatable mold in Finland and in 2016–2019, an ice dome, several ice towers and a 3D printed gridshell of ice in Harbin (China) as a cooperation between the Universities of Eindhoven & Leuven (Pronk) and Harbin (Wu and Luo). In cooperation between the University of Alberta and Eindhoven two ice beams were realized during a workshop in 2020. In this paper we will present the motivation and learning experiences of students involved in learning-by-doing by realizing one large project in ice. The 2014–2016 projects were evaluated by Sanders and Overtoom; using questionnaires among the participants by mixed cultural teams under extreme conditions. By comparing the results in different situations and cultures we have found common rules for the success of those kinds of educational projects. In this paper we suggest that the synergy among students participating in one main project without a clear individual goal can be very large. The paper will present the success factors for projects to be perceived as a good learning experience.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".