The Learning Toolkit
Bibliographic record
Abstract
In this chapter the authors summarize the design, development, testing, and dissemination of the Learning Toolkit—currently a suite of three highly interactive, multimedia tools for learning. ABRACADABRA is early literacy software designed to encourage the development of reading and writing skills of emerging readers, especially students at-risk of school failure. The authors highlight the important modular design considerations underlying ABRACADABRA; how it scaffolds and supports both teachers and students; the evidence on which it is based; the results of field experiments done to date; and directions for future research, development, and applications. They also present ePEARL and explain how it can be used with ABRACADABRA to promote self-regulation, comprehension and writing. They briefly discuss ISIS-21 the prototype of a tool designed to enhance student inquiry skills and promote information literacy. As an evidence-based toolkit available without charge to educators, the authors believe the suite of tools comprising the Learning Toolkit breaks new ground in bringing research evidence to practice in ways that promote wide scale and sustainable changes in teaching and learning using technology.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.062 |
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".