Synthesizing CSCL perspectives on the theory, methods, design, and implementation of future learning spaces
Bibliographic record
Abstract
This pre-conference workshop brings together a number of leading learning scientists, as well as talented younger researchers, working in an emerging, but fragmented line of research focused on ‘Future Learning Spaces’ (FLSs). Significant advances in this area of scholarship have been made in recent years, spurred by billions of dollars of investments into building or re-designing educational spaces — both physical and digital, formal and informal — to accommodate learning in a networked society. To advance our theoretical understanding on the role of space in learning, vital work remains to be done to frame concepts, synthesize dispersed research agendas and share the results of work that is relevant to the broader FLSs project. To do this, this workshop is organized in four themes that address current challenges and opportunities for FLSs research: Theory, methods, design, and implementation. The workshop includes a combination of invited presenters and key contributors who have advanced research in this area; and active participants, who are interested in deepening their understanding through active participation in the workshop. The objectives of this symposium are to (1) deepen participants’ understandings of current FLSs research; (2) cross-fertilize related threads of inquiry for mutual gain; (3) rise above the individual threads to develop syntheses between them; and (4) build collaborative partnerships for future work.
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 | 0.113 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.012 | 0.067 |
| Scholarly communication | 0.034 | 0.046 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".