Measuring Student Knowledge of Landscapes and Their Formation Timespans
Bibliographic record
Abstract
Geologic time is a crucial component of any geoscientist's training. Essential knowledge of geologic time includes rates of geologic processes and the associated time it takes for geologic features to form, yet measuring conceptual thinking abilities in these domains is challenging. We describe development and initial application of the Landscape Identification and Formation Test (LIFT), a concept inventory for measuring abilities to identify landscapes and their formation timespans. Test development included careful choice of concept questions followed by a cycle of validation steps involving student and expert think-aloud interviews. We then administered the test, together with eight validated questions about geological time, to 96 university students in second year and fourth year geoscience courses. Results showed that students' abilities and confidence were more closely aligned with their general knowledge about geologic time than with the level of the course in which they were enrolled. Students were better at identifying landscapes than estimating how long they take to form, and both students and experts had the most difficulty with intermediate formation timespans. Details about students' errors, including common landscape misidentifications and systematic errors in estimating formation timespans, can help instructors prioritize the content and pedagogy of their courses. The LIFT is a validated concept inventory that is available for anyone to use as a pre–post, diagnostic, progress, or end-of-degree assessment that can provide valuable feedback about knowledge and learning to students, instructors and program administrators.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".