Development of a Tool to Assess Interrelated Experimental Design in Introductory Biology
Bibliographic record
Abstract
Designing experiments and applying the process of science are core competencies for many introductory courses and course-based undergraduate research experiences (CUREs). However, experimental design is a complex process that challenges many introductory students. We describe the development of a tool to assess interrelated experimental design (TIED) in an introductory biology lab course. We describe the interrater reliability of the tool, its effectiveness in detecting variability and growth in experimental-design skills, and its adaptability for use in various contexts. The final tool contained five components, each with multiple criteria in the form of a checklist such that a high-quality response-in which students align the different components of their experimental design-satisfies all criteria. The tool showed excellent interrater reliability and captured the full range of introductory-student skill levels, with few students hitting the assessment ceiling or floor. The scoring tool detected growth in student skills from the beginning to the end of the semester, with significant differences between pre- and post-assessment scores for the Total Score and for the Data Collection and Observations component scores. This authentic assessment task and scoring tool provide meaningful feedback to instructors about the strengths, gaps, and growth in introductory students' experimental-design skills and can be scored reliably by multiple instructors. The TIED can also be adapted to a number of experimental-design prompts and learning objectives, and therefore can be useful for a variety of introductory courses and CUREs.
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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 | 0.109 | 0.344 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".