Spaced Learning: The Design, Feasibility and Optimisation of SMART Spaces
Bibliographic record
Abstract
The projectThis report describes the development and pilot evaluation of SMART Spaces.This programme aims to boost GCSE science outcomes by applying the principle that information is more easily learnt when it is repeated multiple times, with time passing between the repetitions.This approach is known as 'spaced learning' and is contrasted with a 'massed learning' approach, where content is learnt all at once with no spacing.The development of the programme was led by a team from the Hallam Teaching School Alliance (HTSA).SMART Spaces prepares Year 9 and 10 students for GCSE examinations at the end of Year 10.Teachers were trained to deliver three lessons focused on chemistry, physics, and biology curriculum content, which were repeated over three consecutive days.Pupils did an unrelated physical activity in the spaces between intensive repetitions of science content.Teachers received one day of training and were provided with PowerPoint slides to deliver during the lessons.The Centre for Evidence and Social Innovation (CESI) at Queen's University Belfast (QUB) worked with HTSA to develop SMART Spaces, test its feasibility, and test three different approaches to arranging the spaced learning across the three days (see Table 1).This project was jointly funded by the
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".