Immersive Learning for Lost Architectural Heritage: Interweaving the Past and Present, Physical and Digital in the Monastery of Madre de Deus
Bibliographic record
Abstract
This paper presents the creation of an immersive learning experience of the lost 16th-century Monastery of Madre de Deus, now the National Tile Museum in Lisbon, Portugal. It builds upon previous virtual reconstruction research which resulted in several digital models accompanied by paradata supporting the construction of different hypotheses. Reinforced by a review of relevant literature intersecting virtual heritage dissemination, research transparency and immersive learning, this paper details an immersive experience created with Shapespark 2.9.7, an online platform designed for architectural walkthroughs but repurposed for heritage dissemination. The result is a prototype that takes place in the existing building wherein the visitor can be transported to equivalent spaces of the 16th or 17th century to gain first-hand experiences of speculative pasts. While the constraints of the Shapespark platform necessitated a counterintuitive narrative workaround, this enabled creative associations to be made between the physical and virtual and the past and present. This paper identifies various advantages and disadvantages of the platform in the context of immersive learning and the long-term virtual sustainability of lost architectural heritage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".