Learning by creating and exchanging objects: The SCY experience
Bibliographic record
Abstract
Abstract Science Created by You (SCY) is a project on learning in science and technology domains. SCY uses a pedagogical approach that centres around products, called ‘emerging learning objects’ (ELOs) that are created by students. Students work individually and collaboratively in SCY‐Lab (the general SCY learning environment) on ‘missions’ that are guided by socio‐scientific questions (for example ‘How can we design a CO 2 ‐friendly house?’). Fulfilling SCY missions requires a combination of knowledge from different content areas (eg, physics, mathematics, biology, as well as social sciences). While on a SCY mission, students perform several types of learning activities that can be characterised as productive processes (experiment, game, share, explain, design, etc), they encounter multiple resources, collaborate with varying coalitions of peers and use changing constellations of tools and scaffolds. The configuration of SCY‐Lab is adaptive to the actual learning situation and may provide advice to students on appropriate learning activities, resources, tools and scaffolds, or peer students who can support the learning process. The SCY project aims at students between 12 and 18 years old. In the course of the project, a total of four SCY missions will be developed, of which one is currently available.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| 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".