Measuring what matters competency-based learning models in higher education
Bibliographic record
Abstract
EDITORS' NOTES (Mark Canada, Bruce W. Speck). 1. Why Service--Learning? (Bruce W. Speck). Service--learning is generally based on one of two impulses, philanthropic or civil, each with its own distinct philosophical viewpoint. Teachers should be aware of these impulses as well as the major challenges of service--learning. 2. A Smart Start to Service--Learning (Maureen Shubow Rubin). A seven--step model can help newcomers develop a successful service--learning course. 3. Service--Learning Is for Everybody (Robert Shumer). A variety of strategies can help service--learning faculty reach out to include more people with disabilities as providers of service. 4. Creating Your Reflection Map (Janet Eyler). A systematic approach to encouraging reflection can help students get the most out of service--learning courses. 5. The Internet in Service--Learning (Mark Canada). Students can serve their communities by helping agencies create World Wide Web sites and by building university--based Internet resources. 6. A Comprehensive Model for Assessing Service--Learning and Community--University Partnerships (Barbara A. Holland). A global approach to assessing service--learning initiatives provides data to demonstrate that learning is taking place and to refine these initiatives so that they can be even more successful in the future. 7. The National Society for Experiential Education in Service--Learning (Lawrence Neil Bailis). Professors do not have to reinvent the wheel when they teach service--learning courses. The National Society for Experiential Education provides a variety of resources to help both novices and veterans succeed. 8. Advancing Service--Learning at Research Universities (Andrew Furco). Despite their emphasis on scholarship, research universities are appropriate places to use service--learning. Three strategies can help practitioners overcome obstacles. 9. How Professors Can Promote Service--Learning in a Teaching Institution (Kathy O'Byrne). Although a college devoted to teaching seems the ideal place to promote service--learning, faculty at such institutions should actively seek key stakeholders' support to ensure that service--learning thrives. 10. Humanistic Learning and Service--Learning at the Liberal Arts College (Edward Zlotkowski). Faculty at liberal arts colleges can take advantage of their institution's mission in order to promote service--learning. 11. Additional Resources (Elaine K. Ikeda). A number of core resources can help faculty begin or improve service--learning at their institution. INDEX.
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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 | 0.021 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".