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Conversations about teaching computational skills to undergraduates
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Integration across curriculum (no separate course) #7

Open rachelss opened 7 years ago

rachelss commented 7 years ago

Given many students are not particularly interested in bioinformatics, consider ways to integrate bioinformatics in many courses. This also links biology content to analysis. It does not preclude a separate course to teach more bioinformatics. It might require buy-in from the department so content is scaffolded and you get a significant competency.

Comment below on how you are approaching this.

davidmam commented 7 years ago

I have a small (3 x 1.5hr) component that is couched as a Problem based learning exercise (after a fashion). The students investigate the differences between human and neanderthal genomes and map coding variants through to structure. Start with the biological question and it inspires learning the techniques to get the answer. In our first year undergraduate class they beg us to teach more statistics so they can evaluate their project data more effectively.

With the question of 'significant competency', don't hold your breath. Competency takes time. In the first two years of the degree course we invest ca. 250-300 hours contact time in practical techniques, about 60 hours in stats. If you want 'significant competency' then you need the time investment. If they know what techniques to approach for answering the question then they have a first step, having competence to answer anything but the simplest questions will not happen without time and incentive.

wrightaprilm commented 7 years ago

I'm curious about this approach. I have significant interest among folks at my institution to collaboratively develop computational lab components to go with existing courses. For example, an ecoinformatics lab to go with ecology. Perhaps it would be possible to, in the freshmen or sophomore years, do a computation "core" course (example: Data Literacy Skills for Biological Sciences) that follows the Data Carpentry semester long, or a similar cirriculum. Passing this course allows the students to enroll in computational labs, rather than the standard lab for the course (if there is one).

Edit to add: I think without a single core course, this might be quite hard, particularly if there is no mechanism to enforce that you have to have taken the course that has a computation component.

rachelss commented 7 years ago

We are using R for data analysis in intro bio. It's not much but it is for everyone and it sets up more advanced courses to use R. I'd rather integrate data skills - everyone should be pushed to think about data competently.

davidmam commented 7 years ago

We have some introductory R in our level 1 (probably equivalent to Freshman year) which is mostly limited to reading data in, plotting subsets with base graphics, and simple hypothesis testing (t.tests, chi-sq etc). They are required to use R for some assignments and to include their code and data in submissions. In level 2 we have a more comprehensive module (1/3 of 1 semester credit load) which takes them through comparison and analysis of categorical and continuous data. Practical analyses are scripted with R notebooks, virtually no Excel in sight.

Class sizes are 160-220 this last year, probably larger next year.

In level 3 there is an elective module which about 1/3 of students chose (numbers are capped so it is full) which covers GLMS, survival analysis etc. and similar more advanced topics. 15/60 credits for the semester. This preps the students who are then doing data analysis as an honours project (eg with analysis of TCGA data) That is purely stats. I run a few bioinformatics modules in level 3, and 4, this year I am doing a specific 'Bioinformatics Research Skills' module which is introductory Python, a smattering of Unix and version control with Git. Enough to get them started on a proper honours project where they can spend most of the time thinking Science rather than just debugging simple scripts.

davidmam commented 7 years ago

Again it is about exposure, practice and the motivation to keep on going when it is tough. Grades are a key motivator for those who haven't got hooked on the problem solving side of it. Empowerment takes time, but when it kicks in it is great. OH in the library: 'Why are you trying to do that in Excel, R is so much easier' (and the stereo type of a nerdy boy hassling a needy girl was most certainly not the case).