Written extended‐response questions as classroom assessment tools for meaningful understanding of evolutionary theory
Bibliographic record
Abstract
Abstract This qualitative study analyzed grade 12 biology students' answers to written extended‐response questions that describe hypothetical scenarios of animals' evolution. We investigated whether these type of questions are suitable for students ( n = 24) to express a meaningful understanding of evolutionary theory. Meaningful understanding is comprised of factual, procedural (rules, algorithms), schematic (“knowing why”), and strategic knowledge (when, where and how to apply knowledge). Evolutionary theory as a multi‐level concept includes concepts on three different levels (descriptive, hypothetical, and theoretical). Students' answers are examined as to whether they reflect the meaningful linking of all concepts through appropriate use of scientific language. Results showed that students (a) predominantly linked descriptive concepts and, although expected, (b) demonstrated only some cross‐concept‐level links (theoretical–descriptive), (c) exhibited even fewer multi‐concept‐level links (theoretical–descriptive–hypothetical), and (d) avoided the linking of hypothetical concepts with theoretical ones. All these results showed the lack of explanations and reasoning (absence of schematic and strategic knowledge) and knowledge of how to link concepts about evolutionary theory meaningfully. The results indicate further that written extended‐response questions are only partially suitable for demonstrating meaningful understanding. Implications for teaching of evolutionary theory are discussed. © 2008 Wiley Periodicals, Inc. J Res Sci Teach 46: 333–356, 2009
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 | 0.020 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".