DIY productive failure: boosting performance in a large undergraduate biology course
Bibliographic record
Abstract
Students in first-year university courses often focus on mimicking application of taught procedures and fail to gain adequate conceptual understanding. One potential approach to support meaningful learning is Productive Failure (PF). In PF, the conventional instruction process is reversed so that learners attempt to solve challenging problems ahead of receiving explicit instruction. While students often fail to produce satisfactory solutions (hence "Failure"), these attempts help learners encode key features and learn better from subsequent instruction (hence "Productive"). Effectiveness of PF was shown mainly in the context of statistical and intuitive concepts, and lessons that are designed and taught by learning scientists. We describe a quasi-experiment that evaluates the impact of PF in a large-enrollment introductory university-level biology course when designed and implemented by the course instructors. One course-section (295 students) learned two topics using PF; another section (279 students) learned the same topics using an active learning approach, which is the standard in this course. Performance was assessed on the subsequent midterm exam, after all students had ample opportunities for practice and feedback, and after some time has elapsed. PF students scored nearly five percentage-points higher on the relevant topics in the subsequent midterm exam. The effect was especially strong for low-performing students. Improvement on the final exam was only visible for low-performing students. We describe the intervention and its potential to transform large introductory university courses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".