Courses based on competitions highly motivate students for research-based learning
Bibliographic record
Abstract
Specially designed research-based learning (RBL) modules are highly motivating, especially when they are based on competitions. We designed a project module at Technische Universität Berlin to motivate students from all fields to get involved in synthetic biology and evaluated the perceiption by our students and the marks they achieved in a final oral exam. During five years we experienced highly successful student groups that won prizes on the international student competitions iGEM and BIOMOD in Boston and San Francisco. The student teams organized themselves and invested strong efforts over long time periods to achieve their ambitious self chosen goals during the projects. The comparison between evaluation results and marks showed that the students achieved a high competence level. We conclude that RBL modules which are coupled with competitions are highly motivating. Our experience shows that international student competitions like iGEM or BIOMOD represent an excellent basis for designing RBL courses in synthetic biology and biotechnology. Since the teams were interdisciplinary also students from informatics, engineering, physics, chemistry, mathematics and even arts were involved, successfully. The aim of the study is to present both 1) the methodological concept to set up similar competition and research based learning modules and 2) the outcome of our evaluation and the marks which are obtained by the students.
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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; both teacher heads agree on what is shown here.
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".