Highly effective active learning in a one‐year biochemistry series with limited resources
Bibliographic record
Abstract
We investigate the effectiveness of an active learning curriculum designed for an upper division Biochemistry series at a large, public research university. The goal was to determine how effective this format was when compared to a parallel conventional course, and to see if the active learning series can be run with limited resources (one instructor, one teaching assistant). The study involved 160 students in the first quarter and 92 students in the second quarter. The active learning curriculum consists of learning goals for each chapter, online quizzes, in-class questions targeting the problematic areas, small group (3-4 students) discussions during class in which students presented their assumptions and arguments in support of their responses to online and in-class questions, and two-stage exams involving the ability to "re-answer" as a group following a discussion). The in-class questions involved the use of a student response system (i > clicker) (multiple choice) and short answer formats. Students in the active learning course and a control, conventional lecture course, took identical midterms and finals for the first, and second quarters. We found that students enrolled in the active learning curriculum had consistently better performance, with statistically significant higher scores on all tests for both quarters. The effect sizes of the improvements are medium to large and are independent of prior GPA and grades in prerequisites. This model curriculum redesign offers promise for improved student learning with less monetary investment than a flipped course model relying on, for example, an extensive collection of instructor-produced videos. © 2018 International Union of Biochemistry and Molecular Biology, 47(1):7-15, 2018.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".