Open Educational Resources for distributed hands-on teaching in molecular biology
Bibliographic record
Abstract
Abstract The urgent need to develop a more equitable bioeconomy has positioned biotechnology capacity building at the forefront of international priorities. However, in many educational institutions, particularly in low-and middle-income countries, this remains a major challenge due to limited access to reagents, equipment, and technical documentation. In this work, we describe Open Educational Resources (OER) composed of locally produced biological reagents, open source hardware and free software to teach fundamental techniques in biotechnology such as LAMP DNA amplification, RT-PCR RNA detection, enzyme kinetics and fluorescence imaging. The use of locally produced reagents and devices reduces costs by up to one order of magnitude. During the pandemic lockdowns, these tools were distributed nationwide to students’ homes as a lab-in-a-box for remote teaching of molecular biology. To test their performance, a total of 93 undergraduate students tested these resources during a biochemistry practical course. 27 out of 31 groups (∼87%) successfully achieved the objectives of the PCR activity, while 28 out of 31 groups (∼90%) correctly identified the target using LAMP reactions. To assess the potential application in secondary school, we organized three workshops for high school teachers from different institutions across Chile and performed an anonymous questionnaire, obtaining a strong agreement on how these OER broaden teachers’ perspectives on the techniques and facilitate the teaching of molecular biology topics. This effort was made possible through a close collaboration with open source technology advocates and members of DIYbio communities, whose work is paving the way for low-cost training in biology. All the protocols and design files are available under open source licenses.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".