This image from the BBC is not subject to’s Creative Commons license.

And now for something completely different…

I’m taking a pause from talking directly about open for a moment to share some resources I’ve recently found that have made my data life much more efficient and enjoyable. But don’t worry – there’s still a connection to open.

I spend a lot of my time working in the data generated by use of Lumen’s open courseware. In addition to regular meetings with partner schools where we share insights both surprising and mundane, this work also supports our continuous improvement efforts to make our open courseware objectively more effective term after term. As I’ve said many times:

  • “open” gives you permission to make improvements to course materials but doesn’t tell you what needs changing.
  • “learning analytics” give you information about what needs improving in your course but doesn’t give you permission to make the changes.
  • ∴ to do continuous improvement in education, you need OER (permission to change) plus analytics (info about what to change).

I’m a huge fan of R and R Studio and use them regularly for data extraction, cleaning, and analysis. If you don’t know these, R is open source software for statistical computing and visualization, and RStudio is an IDE that makes R easier to use and includes a code editor, as well as debugging and visualization tools. I love these tools because they make doing reproducible research so much easier – instead of trying to remember how I changed that Excel file and which menus I clicked on in SPSS, I can write R scripts that repeat the entire process from extraction to cleaning to analysis to reporting, so I can always repeat (or audit) my work.

I’m an especially big fan of R Markdown in R Studio, which lets you intermingle analysis code (R) with presentation instructions (Markdown) in one file so that you can plug in new data, literally push a button, and automatically create slides or HTML or PDF that contain updated findings and graphics. When you’re sharing a semester’s worth of findings with multiple schools in short time window, this capability turns out to be an absolute lifesaver. It’s also a sinch to then combine the data from all schools and re-run what is now an omnibus analysis that gives you insight into how things are working overall and share findings in support of continuous improvement efforts.

But recently I’ve found myself spending more time in the machine learning space. And this is where two new (to me) open source tools have proven to be really powerful and interesting. The first is vtreat. vtreat is a data.frame processor/conditioner that prepares “real-world” data for predictive modeling in a statistically sound manner. The main idea with vtreat is that even with a sophisticated machine learning algorithm there are many ways messy real world data can defeat the modeling process, and vtreat helps with at least ten of them.

The second is Rattle (the R Analytical Tool To Learn Easily):

Rattle makes much of the data preparation (e.g., splitting into train/validate/test sets), exploration, transformation, model building, and evaluation process point and click. This is terrifically powerful for getting started quickly with machine learning tasks. Perhaps more importantly, Rattle logs all the R commands used throughout the process and lets you save them as standalone R scripts. Then you can further refine things from the command line (or in R Studio) and plug the scripts into whatever process you use to periodically run your analyses.

I know this is different from the usual iterating toward openness fare, but given how much these tools are improving my life I wanted to make sure others who could benefit from them knew about them. What tools do you use in your data-related work? Drop your favorites in the comments below.

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How Is Open Pedagogy Different?

I feel like words should mean something.

Especially the word “open.”

Specifically, I’m deeply concerned about the way many have begun using “open” in the context of “open pedagogy,” because I can’t tell what it means.

For many years we have seen openwashing among companies working in the education space, in which they either knowingly or accidentally attempt to equate “open” with something other than a free grant of the 5R permissions. If left unchecked, these attempts would dilute and weaken the meaning of open and, consequently, the community rallies against them.

Now we are seeing something related happening within our own community with “open pedagogy” – where the previously clear and specific meaning of “open” is expanding rapidly to incorporate everything from peer review to reflective practice. In some ways, it seems like we’re confusing “synergizes with open in powerful ways” with openness itself. In others, it seems like we’re taking effective pedagogical practices, bundling them together in novel ways, and labeling them “open.” Because “open is good” in the popular narrative, there’s apparently a temptation to characterize good educational practice as open educational practice.

But that’s not what open means.

As I’ve argued many times, the difference between free and open is that open is “free plus.” Free plus what? Free plus the 5R permissions. While almost the entire internet is free to watch, read, and listen to, only a small slice of the internet is open – licensed in a way that grants you the 5R permissions. These permissions are the distinguishing feature of open, whether you’re talking about open educational resources, open source software, open data, or a range of other open things.

If open is fundamentally about permissions, what then does it mean for a pedagogy to differ in terms of it’s assumptions about copyright permissions? In a nutshell:

  1. We learn by the things we do.
  2. Copyright restricts what we are permitted to do.
  3. Consequently, copyright restricts the ways we are permitted to learn.
  4. Open removes these restrictions and permits us to do new things.
  5. Consequently, open permits us to learn in new ways.

In other words, open pedagogy is the set of teaching and learning practices only possible or practical in the context of the 5R permissions. Or, to operationalize, open pedagogy is the set of teaching and learning practices only possible or practical when you are using OER.

That’s how open pedagogy differs from other pedagogies. And because that difference is quite narrow and specific, it may only make sense in the context of broader pedagogical traditions:

How would open behaviorist pedagogy differ from behaviorist pedagogy?
How would open cognitivist pedagogy differ from cognitivist pedagogy?
How would open constructivist pedagogy differ from constructivist pedagogy?
How would open constructionist pedagogy differ from constructionist pedagogy?
How would open connectivist pedagogy differ from connectivist pedagogy?

What does open add to each of these existing pedagogies? As the simplest possible example, take constructionism. While a constructionist pedagogy only encompasses “learning by making,” an open constructionist pedagogy also encompasses “learning by revising” and “learning by remixing” – things you can do only when working in the context of OER and the 5R permissions.

There are many wonderfully engaging, motivating, and effective pedagogical practices that can be enacted in the context of traditionally copyrighted resources (regardless of whether they are free or expensive). While these practices may powerfully support learning, it is counterproductive to characterize them as open. While some of these practices may powerfully synergize with open, it is counterproductive to characterize them as open.

When you hear someone describe an “open pedagogy,” ask yourself if the specific pedagogical features they’re describing can be enacted in the context of traditionally copyrighted educational resources. If nothing they describe requires the 5Rs, they may be describing effective pedagogy but they I don’t think they’re describing open pedagogy.

Why does it matter? Why argue over terminology? Because we desperately need to hold the line on what open means or, before we know it, it will mean nothing at all.

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Pearson and the Big Winner

In a recent interview, Pearson CEO John Fallon said:

“Education like every other sector and sphere of life is going through this digital transformation. There is going to be a big winner in the transformation in education. We are absolutely determined to make Pearson that winner.”

This is perhaps the clearest statement I’ve ever read of the fundamentally wrongheaded view of the traditional publishers. The only way to survive “this digital transformation” is to be absolutely determined to make learners the big winners. Only the organizations that make this commitment a core value will remain standing when all is said and done.

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