<i>In silico</i> research as an active learning platform in a molecular biology course
Bibliographic record
Abstract
Active learning approaches can deepen the students' understanding of major biological and biochemical concepts by integrating course content with practical applications. In order to facilitate the merging of theory and application, a blended course format was developed for a 4 th year class on the Structure and Function of the Human Genome. Briefly, a third of the lectures were replaced by online tutorials, called Webquests. The Webquests were constructed such that the students could independently follow both written instructions and instructions given by a series of video tutorials embedded in the Webquests. Each Webquest required the students to do specific tasks researchers in molecular biology would normally do, using specialized but freely available online software such as UCSC and Ensembl genome browsers, IGV genome viewer, Galaxy/cistrome bioinformatics data analysis platform, NCBI GEO platform and more. These tasks consisted of, for example, planning cloning experiments for protein expression or a luciferase assay or using the various software or platforms to view, manipulate and analyze high throughput sequencing data and discuss the functional organization of different gene loci when looking at several functional makers (CpG islands, histone mark, nucleosome mapping, DNase hypersensitivity, FAIRE‐seq and transcription factor binding sites). Students had to write down and explain their problem solving strategies, results and discussion in a laboratory book, as they would in laboratory research. These were also discussed in class, as a research group would discuss group member's results. Course evaluations revealed this practical aspect of the course was appreciated by students and most thought the Webquests were interesting but very demanding and generally put them in the uncomfortable position of not knowing whether their considerable amount of work was good or bad. Therefore, a teaching strategy using immediate and substantial feedback closer to one‐on‐one mentoring seems to be important for this type of course content and activity in order to nurture and guide students even in basic skills such as scientific writing and maximize their learning experience. Using this blended learning strategy allowed the students to put in practice the theoretical knowledge acquired in class in a novel and engaging manner, better reflecting the process of scientific inquiry in a real world environment. Support or Funding Information The University of Ottawa Teaching and Learning Support Services (TLSS) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.043 |
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".