Multimodal Modeling Activities with Special Needs Students in an Informal Learning Context: Vygotsky Revisited
Bibliographic record
Abstract
Background:In light of the challenges facing science educators and special education teachers in Singapore, this study entails design-based research to develop participatory learning environments.Material and methods:Drawing upon Vygotskian perspectives, this case study was situated in an informal workshop around the theme of “day and night” working for Special Needs Children (aged from 7 to 14 years old) in Singapore. Moving away from traditional astronomy teaching, we aim to explore interdisciplinary multimodal modeling activities towards developing a participatory learning environment.Results:As the main finings of this case study, the central benefits of interdisciplinary multimodal modeling activities are twofold: (1) promoting multiliteracies development using digital and multimodal resources for supporting the emotional and social experiences in developing learners’ astronomical understanding; and (2) integrating learners’ everyday experiences with scientific astronomical understanding for the development of higher cognitive functions.Conclusions:These findings emphasize the need for the cultural development of Special Needs Children.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".