Documenting and Assessing Learning in Informal and Media-Rich Environments
Bibliographic record
Abstract
An extensive review of the literature on learning assessment in informal settings, expert discussion of key issues, and a new model for good assessment practice. Today educational activities take place not only in school but also in after-school programs, community centers, museums, and online communities and forums. The success and expansion of these out-of-school initiatives depends on our ability to document and assess what works and what doesn't in informal learning, but learning outcomes in these settings are often unpredictable. Goals are open-ended; participation is voluntary; and relationships, means, and ends are complex. This report charts the state of the art for learning assessment in informal settings, offering an extensive review of the literature, expert discussion on key topics, a suggested model for comprehensive assessment, and recommendations for good assessment practices. Drawing on analysis of the literature and expert opinion, the proposed model, the Outcomes-by-Levels Model for Documentation and Assessment, identifies at least ten types of valued outcomes, to be assessed in terms of learning at the project, group, and individual levels. The cases described in the literature under review, which range from promoting girls' identification with STEM practices to providing online resources for learning programming and networking, illustrate the usefulness of the assessment model.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".