POE: Understanding Innovative Learning Places and Their Impact on Student Academic Engagement—Index 6–8 ‘Alpha’ Survey Developments
Bibliographic record
Abstract
New evidence builds upon the Student Engagement IndexTM and Teacher Engagement IndexTM research (Scott-Webber, Konyndyk, & French, 2019; Scott-Webber, Konyndyk, French, & French, 2018; Scott-Webber, Konyndyk, French, Lembke, & Kinney, 2017) determining post-occupancy answers to, “Can we demonstrate that the design of the built environment for grades 6–8 impacts student academic engagement levels post-occupancy?” The early studies used respondents from grades 9–12. This one is from users in grades 6–8 (‘alpha’ pilot). All studies were conducted in the USA as convenience samples. Engagement performance is a high predictor of student success across multiple domains and learning/work experiences. Specifically, “Research that shows that engagement, the time and energy students devote to educationally purposeful activities, is the best single predictor of their learning and personal development” (Anonymous, NSSE, 2010, p. 2), and thus our research focus. From both the students and educators perspectives, design of the built space impacts engagement performance (p < .0001).
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".