Quantitative PER Wiki

A wiki to organize quantitative education research projects by researchers at Michigan State University and the University of Oslo including discussions of data, methods, and analysis.

This wiki uses both the doku wiki default syntax and markdown. When creating new pages PLEASE ONLY USE MARKDOWN. It is much more readable and standard than the doku wiki syntax.

This wiki has limited access - an account is needed to access anything but this page. If you think you have access to this wiki, please log in using the button at the upper right. If you require a new account, please contact danny or john on slack.

Learning resources

Are you new to the group and want to learn the various scientific skills and practices that we use here? Here are some learning tutorials that are worth going through. These are listed here for self learning, but we will occasionally run workshops on these topics to bring members up to speed as well.

  • LEARN SQL NOW - This website will teach you the basics of SQL that you will need to know to write queries to access data.
  • Learn python for data analysis! - These are the course materials for a course to teach python for data analysis to seismologists that John Aiken wrote. You can read a paper about this material here. The good news is, this course teaches you skills that are good for quantitative education research as much as it teaches the same skills to seismology students.
  • Learn machine learning! - what is machine learning? why do we care? what is the goal? more later when john make the workshop
  • Learn network analysis using python! - who can know what network analysis is? nobody thats who. That's why one day we will have a workshop teaching such skills.
  • GETTING STARTED WITH GITHUB - there are a billion tutorials on this stuff, this is only here to get you started. To learn more about using git and github, go here.

Computational Resources

We have a number of computational resources at our disposal. You can connect to many of them remotely. Please think before you use your own computer to develop or do science within our group. These computing resources already have many tools like anaconda python, R, SQL Server, git, etc. installed and are typically much more powerful than your laptop. Resources are as follows:

  1. Vegeta, UiO, 128GB RAM, 1x 1080ti nvidia graphics card, 10-core Intel i9. Logging in to UiO Workstations
  2. Goku, MSU, 768GB RAM, 14-Core Intel Xeon (x2).
  3. Piccolo, Digital Ocean instance, 1vCPU, 1GB RAM, 25GB Disk, 1TB transfer.

We have a number of databases that are administrated by a variety of group members. These databases are as follows:

  1. Pathways database - This database contains all registrar data from MSU for students attending MSU 1992 to 2017. It is administrated by John Aiken. Please contact him on slack for questions.
  2. Surveys database - This database contains all survey data collected at MSU (e.g., ECLASS, FCI, PMQ, etc.). It is administrated by Rachel Henderson. Please contact her on slack for questions.
  1. semesterData - data storage class for organizing pathways data by semester.
  2. model_tools - traditional statistics functions for sklearn models.

Since we love Dragon Ball all computers and computing resources having Dragon Ball character names. Servers are named after male Dragon Ball characters. Other resources (e.g., NAS, routers, etc) are named after female Dragon Ball characters. If you need help picking a name you can find Dragon Ball character names here.

We use github as version control for all of the code, notebooks, and software that we develop within the Learning Machines Lab group. If you have never used github before and have created a notebook or other codes for the group please follow this tutorial.

You can find our github organization here. If you are not a member of our github organization, please message john on slack to join.