Identifying Landscapes and their Formation Timescales: Comparing Knowledge and Confidence of Beginner and Advanced Geoscience Undergraduate Students
Bibliographic record
Abstract
The Landscape Identification and Formation Test (LIFT) was created in response to previous data from the Student Attitudes about Earth Science Survey (SAESS) at the University of British Columbia (Vancouver). The SAESS data suggested that upper-level students become less confident in landscape identification and formation timescales over the course of a term. The LIFT specifically probes the relationships among student confidence and knowledge in landscape identification and formation timescales and general knowledge in geologic time. The LIFT was validated with “think-aloud” interviews with students and correct answers were determined from interviews with experts. Results from the LIFT suggest that advanced students have higher conceptual knowledge, higher confidence in their knowledge, and are more self-aware than beginner students. Advanced students became more confident in landscape identification and formation timescales over the course of the term, contradicting the results seen in the previous administration of the SAESS. Students are better at identifying landscapes than assessing how long they take to form and are better with extreme timescales, two critical points that should be taken into consideration with future curricular reform.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".