The Colorado Learning Attitudes about Science Survey (CLASS) for Use in Biology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes a newly adapted instrument for measuring novice-to-expert-like perceptions about biology: the Colorado Learning Attitudes about Science Survey for Biology (CLASS-Bio). Consisting of 31 Likert-scale statements, CLASS-Bio probes a range of perceptions that vary between experts and novices, including enjoyment of the discipline, propensity to make connections to the real world, recognition of conceptual connections underlying knowledge, and problem-solving strategies. CLASS-Bio has been tested for response validity with both undergraduate students and experts (biology PhDs), allowing student responses to be directly compared with a consensus expert response. Use of CLASS-Bio to date suggests that introductory biology courses have the same challenges as introductory physics and chemistry courses: namely, students shift toward more novice-like perceptions following instruction. However, students in upper-division biology courses do not show the same novice-like shifts. CLASS-Bio can also be paired with other assessments to: 1) examine how student perceptions impact learning and conceptual understanding of biology, and 2) assess and evaluate how pedagogical techniques help students develop both expertise in problem solving and an expert-like appreciation of the nature of biology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it