Data Science Courses for Economists and Epidemiologists using RTutor
This article is originally published at http://skranz.github.io/
Were there no Corona virus pandemic, German Universities would regularly start the summer semester in around a month (soon after Eastern). Now, it seems likely that courses will be offered digitally and students must learn from home.
If you have a course that uses R, you may take a look at RTutor. It allows students to solve interactive problem sets at home. They can test their solutions, get automatic hints and then submit their solution for automatic grading.
To get some material and ideas you can take a look at the following three courses using RTutor:
A data science project course taught by Alex Rieber for business and economics students at Ulm University. Before students work on their own data science projects, they learn basic skills in R, including tidyverse data wrangling and econometric and machine learning basics via several RTutor problem sets. Alex published the problem sets and other course material here on Github. You find on the Github pages also links that allow you to test the problem sets on the rstudio cloud. The course is in German but Alex already started to make an English version of the problem sets, which we publish once finished.
Jade Benjamin-Chung from UC Berkeley School of Public Health has created with RTutor online tutorials for an introductory R course for epidemiologists. Here is the course page. If you click on a tutorial the corresponding RTutor problem set can be directly solved on shinyapps.io. There is no need to log in.
I have published the RTutor problem sets and other material from my course in empirical industrial organization class in this Github repository. You can directly work on the problem sets here on rstudio.cloud. The course focuses a lot on estimating demand functions, but the R problem sets also cover other material, like data wrangling with dplyr.
In addition, you can find on the RTutor page many interactive replications of interesting economic and interdisciplinary research papers, e.g. about the effects of water polution on cancer, an environmental assessment of driving electric cars, the effect of soap operas on fertility, a study of how better contracts could reduce traffic jams, the effects of CO2 pricing on firm relocation, an assessment of free trade agreements, and more…
For our courses, Alex and me have not included on Github the Rmd solution files from which the RTutor problem sets were created. (We want to avoid that students just copy those solutions). If you are a lecturer who is interested in using and modifying these problem sets, just send Alex or me an email and we can send you these files. Alex has further developed a multiple choice test exam, based on these problem sets which you could also receive upon request.
If you are thinking to make your students study at home with RTutor, you may also take a look at two older blog posts. This one describes how you can automatically grade submitted problem sets. That one compares some RTutor with learnr.
If you are using RTutor and perhaps want to share some course material please let Alex or me know! We are happy to get insights from your RTutor usage, and if you like, we can put a link in a blog post or on the RTutor website.
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This article is originally published at http://skranz.github.io/
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