On-Ramps to more effective teaching: Quick-start guides to strategies for actively engaging students in the classroom to improve learning
Bibliographic record
Abstract
The landscape of college and university teaching in the geosciences has changed over the past 20 years. Research has documented 1) that faculty in the U.S. now spend less time lecturing and more time actively engaging students in the classroom, and 2) that active engagement is more common in geoscience classrooms than it is in biology, chemistry, physics, or engineering. The web sites of Teach the Earth and On the Cutting Edge have thousands of web pages of resources for geoscience faculty who want to more actively engage their students in the classroom. But what if you want to incorporate more active learning but aren’t sure where to start or how these techniques might work in your courses? Or what if you are looking for new approaches or fresh ideas to add to techniques that you already use? On-Ramps are quick-start guides designed to bring you up to speed in effective strategies for engaging students more actively in the classroom. Each 2-page On-Ramp focuses on a particular teaching strategy, rather than on how to teach a particular topic. The current On-Ramps cover interactive lecture, brainstorming, concept sketches, jigsaws, discussions, quantitative skill-building, just-in-time approaches, case studies, and re-thinking course coverage and linearity. Each On-Ramp includes a simple example that illustrates the strategy, why the technique is valuable, implementation tips, additional examples and modifications, and links to activities, supporting research, and other resources. On-Ramps will be available at the poster and can also be downloaded as pdfs from serc.carleton.edu/onramps/index.html On-Ramps originated from the 2018 community vision report to US National Science Foundation on Challenges and Opportunities for Research in Tectonics, and their development was supported with a grant from NSF. The On-Ramps writing team is a group of geoscientists at a variety of career levels with specialties across the range of subdisciplines that regularly address tectonic problems. Although examples currently focus on the broad field of tectonics, On-Ramps can be easily adapted for courses in other geoscience disciplines at all levels.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.065 |
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".