An undergraduate research experience in earth science education that benefits pre-service teachers and in-service earth science teachers
Bibliographic record
Abstract
Three cohorts of six pre-service Earth Science teachers (undergraduate majors in Earth Science Education) participated in summer research experiences focused on developing dynamic physical models of Earth processes to help middle and high school students understand complex concepts and confront misconceptions. The pre-service teachers used published criteria for evaluating models. Participants deepened their understanding of specific Earth Science concepts and broadened their perceptions of effective, student-centered, constructivist pedagogical practices through the use of models and model-based learning. Our pre-service Earth Science teachers achieved the same benefits that STEM majors report from their undergraduate research experiences, including better understanding of the nature of science, gains in problem-solving and communication skills, increased confidence, collaborative skills and comfort in working independently. Evaluation of the research experience via the Undergraduate Research Student Self-Assessment indicated that pre-service teachers reported higher gains than STEM majors in nearly all categories.The pre-service teachers presented the results of their projects to in-service teachers in professional development workshops at a science teachers’ conference. In-service teachers’ responses to these workshops were uniformly positive (98.2%; n = 57). Unlike most professional development activities in which participants benefit, but presenters may not, these professional development activities benefited participants and presenters alike.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".