Video-Based Bioinformatics Tutorials Developed as an Open Educational Resource to Improve Students’ Understanding and Practice in Data Science Analyses
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide With the development of digital pedagogical resources, courses, and the recent COVID-19 pandemic, there has been a rise in the use of video-based learning (VBL) and teaching as one of the primary methods of instruction. Additionally, in recent years, bioinformatics has surfaced as an integral discipline in life sciences, where scientists are able to manipulate and analyze large sets of data. As a result, the need for digitally enhanced undergraduate and graduate teaching of basic bioinformatics skill sets of an applied nature has become increasingly high. Here, we designed and implemented a set of video-based bioinformatics tutorials as an open educational resource to be taught in an online synchronous, asynchronous, as well as HyFlex setting. These tutorials were designed to identify a ligand against unknown amino acid and nucleotide sequences to unveil their context in diverse species. This was achieved by navigating online bioinformatic databases, performing multiple sequence alignment, phylogenetic analyses, protein structure prediction/comparison, and docking. In the end, students also completed a survey questionnaire outlining their experience with the VBL. By the end of the term, VBL enabled the students to learn and apply bioinformatic concepts and tools to predict the protein structure from an unknown sequence and dock it with the ligands. Students rated VBL as one of the most powerful learning mediums out of many used as part of the module. Bioinformatic videos, besides capturing and distributing the bioinformatic information, also provided an invigorating environment where students better learned, understood, and retained the content.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.016 |
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".