You may have heard me say some of this before.
By definition, open educational resources (OER) are licensed in a manner that gives you permission to change, update, and improve them. However, that permission gives you no insight into what needs to be improved. Learning analytics, on the other hand, can provide great insight into where course materials – including OER – are not effectively supporting student learning. However, that insight does not grant you the permission necessary to make improvements to ineffective course materials.
When OER and learning analytics are used together, it becomes possible to engage in continuous improvement – empirically identifying weaknesses in OER, making improvements to those OER that need to be improved, and starting the cycle again.
As you might imagine, this process can be incredibly time-consuming.
Last year, Bob Bodily, Rob Nyland, and I published The RISE Framework: Using Learning Analytics to Automatically Identify Open Educational Resources for Continuous Improvement in IRRODL. In this open access article we outlined a method for automating the identification of the OER in a course that are most in need of improvement. The problem we were trying to solve is a special kind of search task, but instead of asking a web search engine for the ten most relevant pages (out of millions) on a given topic, you want to ask which OER (out of thousands) are most in need of improvement. Like Google, RISE doesn’t fully automate the task of finding the resource you’re looking for, but it drastically reduces the time and complexity of the task by providing you with a small set of top results.
Academic papers are often fun to read but often not immediately useful. Since we published the paper last year, the concept of RISE analysis has been openly available but an actual implementation has not. Today I’m excited to announce an open source implementation of RISE that is immediately useful. This MIT-licensed R package is available at:
The R package makes RISE analysis as simple as:
output <- rise_analysis(your_data_frame)
Details are available in the Github repo and package documentation. Improvements and pull requests are welcome. RISE has also been published in the Journal of Open Source Software.
I sincerely hope this release will jumpstart more activity around continuous improvement of OER. RISE analysis dramatically reduces the drudgery of sorting through data trying to decide which OER need attention, allowing your team of course designers and faculty to spend their time actually making improvements to the course – and this is really fun and rewarding work.
Though I’ve been using R for several years I had never built a package before. This was a super fun and rewarding process for me personally and I’m already working on my next one. Even if you never intend to release your package publicly, creating a package makes reusing your own code across analyses SO much faster and easier. I’m sure I will more than recover the time spent creating the package in the coming weeks and months. If you’d like to try your hand at developing R packages, I would recommend starting with these resources:
- You can make a package in 20 minutes (video) by Jim Hester
- Writing an R package from scratch by Hilary Parker
- How to develop good R packages (for open science) by Maelle Salmon
- R Package Primer, by Karl Broman, including its exceedingly practical Getting your R package on CRAN
- R Packages by Hadley Wickham