Still Running Errands for Open Learning Ideas

A big day planned today.  I’ll be spreading the word about Domains of One’s Own projects, Reclaim Hosting, and open learning to community colleges. I’ll be presenting twice today at the League for Innovation in Community College’s big Innovations 2017 conference.  Actually, we will be presenting and spreading the word today.

First up, I assist my fantastic colleague Leslie Johnson as she tells how the LCC Center for Teaching Excellence  uses our Open Learning Lab to promote sharing of teaching ideas and teaching faculty how to use open learning techniques like writing-in-public assignments.  The session is titled “Connect and Create: Teaching Faculty by Modeling Open Learning”.

Then in the afternoon we’ll switch roles and she’ll help me as I spread the word about open learning and Domains projects to community colleges. It’s the latest incarnation and update of story of how a community college started a Domains of One’s Own project. I call it “Running Errands for Ideas”.  The slides are here.

If you’re interested in learning more, contact us either by comment here or on the Twitter. I’m @econproph on Twitter and Leslie is @mtflamingo.

The OER Content Trap

Recently I’ve been following  a discussion about the future of OER (Open Educational Resources). Most of the discussion has been via blog posts between David Wiley (@opencontent) and Rajiv Jhangiani (@thatpsychprof).  Others have contributed via Twitter.  It’s a friendly exchange with the key blog posts having been David’s

and Rajiv’s

The discussion is not really a new one. It’s the question of how to promote OER. There has been for a while two “camps” or points of view.  To simplify (or oversimplify) the question:  To expand OER use do we argue the “free textbooks” aspect emphasizing retain and reuse, or do we argue the “open pedagogy” practices aspect and emphasize revise and remix powers.  The points are, of course, well made and I’m not writing here to disagree.

Rather, what I want to suggest is that we’ve fallen into a trap by our use of the word “resources”.  We need to stop thinking about “resources” period.  No more OER.

Yes, I have a proposed replacement – wait for it.  First, let me explain where I’m coming from. The past couple of weeks I’ve been reading a book called The Content Trap by Bharat Anand.  Read it. Do it. It’s a highly readable, story-laden book about business strategy for digital businesses. But despite being so accessible, it’s also strongly supported by good research and the economics of business strategy.  Trust me. I’ve been a full-time academic for the past 15 years, but I’ve spent nearly four decades in the business strategy world. I spent a good 20+ years working on these kinds of issues: how to expand adoption of a new technology/product/process/service and how to compete. I spent a lot of time a few years ago helping to craft strategy for a college.  I don’t praise business strategy books easily. Most are crap or pablum. The Content Trap is not. It is based on both sound empirics AND sound economics and behavioral analyses.  The oversimplified, too short TL;DR version of Content Trap is this: focusing on the product is a trap. It’s the connections that count: connections between products, between customers, between producers.

Rajiv is right that part of the problem is changing minds and certainly understanding the relevant psychology should inform our advocacy.  David is also right about a very important thing: we are competing against the for-profit publishers and the publishers are pivoting their strategies towards platforms.  But the essence we’re facing is a strategic competitive problem.  It’s the Open folks vs. the for-profit, lock-down, lock-in publishers.  I think by focusing on the “resources”, the content, we’ve fallen into the content trap.  We worry about how to finance the costs of production of “free” textbooks. We worry about competing for adoption of OER texts vs. the publisher texts. We’re trapped into focusing on the content.  Even when we talk about open educational practices or pedagogy, OEP, we’re still focused on the content because we focus on how the content is used.

We’re not alone in this trap. Nearly all higher ed institutions are there too.  They almost all think their special sauce is are the courses they teach or the research publications they produce. They’re wrong.  Similarly, the special sauce in open education isn’t the OER, the resources, books, videos, and content. The real special value is in the connections people make, the community that forms, and the identities they forge.

So what should we be focusing on? Open Education Connections or Open Educational Communities. OEC.

I’ll have more to say in the coming month, God willing.  I know this is just kind of a tease so far but I don’t have time tonight to go further. In my own head I’m beginning to visualize winning strategies built around this concept of OEC’s. It’s a lot more complex than just a simple name, great strategies always are.

I’ve got a panel discussion at OER17 conference coming up in April.  I’m trying to put together a couple longer blog posts in preparation for that.  Right now I’m thinking this OEC idea might fit.

 

WPCampus Online – Ten Plus Ways to Teach With WordPress, a.k.a Open Education

Please join me today at the first WPCampus Online conference. It’s free. If you’re involved with higher education you’ll find it helpful – regardless of whether you’re experienced or new to WordPress.   I’ll be talking about examples of using WordPress to engage students and create an open education.  Here’s the full description:

All times are listed in Central Standard Time.

Date: Monday, January 23, 2017 Time: 2:00 – 2:45 p.m. Location: Room 2

The Magic of Teaching Using WordPress: 10+ Ways to Easily Transform Classes & Excite Students

Open Learning means no more boring disposable assignments and no more locked-down closed LMS’s. In Open Learning, students become to become creators and publishers, instead of passive receptacles for lecture. WordPress is the magic that enables professors to create open learning experiences such as student portfolios, writing-for-public assignments, collaborative open texts, and more. In this session, I will describe ten (or more) ideas and designs for how to customize a WordPress site for a particular instructional use case. For each, I will provide ideas for how faculty can get started themselves – regardless of whether their institution has a formal blogs or domains program. All examples are based on our experiences at the Lansing Community College Open Learn Lab or at some other Domains-of-One’s-Own hosting universities.

Here’s a link to my slides in case this viewer doesn’t display them or you want to download.  Links to all examples are in the slides.

https://docs.google.com/presentation/d/1DAmm7hk7QyXjIelIyMslSGkVnAdRIdfqo9rlAxHhlOU/pub?start=false&loop=false&delayms=3000

The Mastodon In the Room

I’m writing this post because I can’t fit my thoughts into 500 characters. This is a very loose set of (probably) ill-connected thoughts triggered by discussions on Mastodon.social.  If you don’t know what Mastodon, it’s a kind of open source, decentralized/federated alternative to Twitter. Sort of.  Of course some have said it’s an alternative to Slack. Sort of.  Who knows?  This post is an attempt to add to that confusion.  If you’re still interested but don’t know Mastodon, check out Maha Bali’s piece on a Social Network of Our Own.

What prompted this post was my own post on Mastodon a day or so ago:

mastodon

Part of what I love about Mastodon (as compared to Twitter) is the 500 vs. 140 char limit.  It makes a huge difference. It enables more thoughtful posts – as in they not only express deeper/richer thoughts, but reading the posts often requires more thought.  They’re more engaging.  It makes a very happy medium IMO between Twitter-like “conversations”, which are really just rapid exchanges of 1-liner quips, vs. the blogosphere which is more like an exchange of letters.

First some semantics. I’m using the following words to mean:

  • Followees:  the people a person “follows” on the social media. In other words the people I’m interesting in reading their stuff.  This is in contrast to followers who are the set of  people who read what I write.
  • Stream:  the reverse chrono list of posts that person reviews as their primary way of finding out what their followees said.  In Twitter, it’s the main stream you read.  Mastodon is different because there’s the Public Stream of all things (not really accessible except via API in Twitter) and the Home stream.  The Home stream is closest to the Twitter main stream.
  • Scale:  more of the same.  Example: If I add 50 more followees who are all interested in the same types of things such as Open Ed, I’m scaling up.
  • Scope: adding stuff/things/followees who are different from the rest.  Increasing scope means increased heterogeneity.  For example, if I already have 50 followees that tend towards the open ed-ish, and then I add 20 folks who don’t talk open ed but talk about games and then add 10 more who talk football, I’m increasing my scope.
  • Filter:  a rather tech term that allows for creating a subset of the stream by applying some boolean logic to some aspect of the toots/tweets. Filtering is often done on tags but could conceivably be done on text items or names.
  • Rooms:  a non-tech term used to describe the experience of having/seeing/speaking with a group of particular tooters/tweeters

Here’s what’s occurred to me so far:

  •  Scale in the Stream:  Twitter’s small, short 140 char style makes it possible to scan/review the a stream a lot more feasible when there are larger numbers of contributors to your stream. Of course, if you have enough folks you’re following on Twitter, the primary stream you see becomes difficult to deal with but mostly just because the sheer volume of tweets per minute.   The mix of short and longer toots on Mastodon, make it harder to cognitively deal with a stream much sooner as you scale up followers.  This is because the longer posts encourage more cognitive engagement and (at least amongst my peeps) more responses that are at least cognitively linked.  I suspect a smaller number of followees (people you follow and hence read in your home stream) will trigger a  feeling of “maxed out” in Mastodon than in Twitter.
  • Scope in the Stream: This problem of cognitive load & time involved to process the stream gets particularly bad if you increase scope.  I can easily process two tweets on different subjects that are juxtapositioned.  They tend to stand alone and they’re short and shallow cognitively.  Toots are much harder when scope increases.
  • On the counter side to increased cognitive load is the need to have some openness to new topics, new speakers, etc. That’s often where the serendipity comes from.  We don’t want to last that aspect because then it just becomes an echo chamber.
  • I don’t think filters can get us the “room” experience.  Filters are text-specific somehow: tag, keywords, etc.  Further, setting up filters must be done in advance but that then precludes the serendipity and closes off the open.
  • Jeroen Smeets asked if what we were (I was) talking about was creating a Storify type thing.   In some ways, yes, it would be like creating a Storify, except Storify is dead – it’s an archive of the past.  I’m interested in viewing my live stream in ways that give me the storify experience in real time.

So I’ve come up the idea of a “lens” or “lenses”.   I’m aware that I might be reinventing something called lists, but since I’m not really familiar with Twitter “lists”, so be it.  Won’t be the first time I’ve reinvented the preexisting.

Let’s start with the public stream. It’s everything that’s coming through the network. While I like the ability to see the public timeline stream on Mastodon, as soon as Mastodon users start to achieve really large numbers it will be useless for direct human reading except for the occasional dip into a small segment of it just  for grins. Nonetheless, the public timeline stream holds great potential because with open source, who knows what folks might create that can make use of that computer-wise some day.

A lens is a way that a user can view the giant public timeline.  On Twitter, there’s only one lens per user.  That lens creates your home timeline stream from the all-public stream.  The primary element used to create the Twitter lens for each user is the list of your followees.  If a Tweet in the big timeline involves your followee (from, to, mentioned) it becomes visible through your lens.  This is the original functionality that we fell in love with on Twitter.

What happened?  Well two things.  First, Twitter expanded your lens without the your involvement by using algorithms to select tweets to put in your stream even if you didn’t want to follow those people. A lot of this advertising and “promoted tweets” related. Part of it is because Twitter as a company  also needed to boost the amount of time you spent on your stream.  All of this is because of business model & $.  Mastodon should be able to avoid this because there’s no VC/investors to be made rich (although we need to make sure @gargron and others live a decent life!) and because the decentralized federated servers model allows what I expect will actually be a lesser cost per toot in the total system than the centralized system of Twitter.

But there’s another thing that expands your Twitter lens.  Twitter needs/wants numbers:  users, tweets, views, minutes spent. That’s what they need to monetize. To do that they enable trolls.  Suppose for a moment there are not-quite-human like entities we’ll call trolls and their mission is make people miserable on social media.  Trolls can find you and force their way into your stream – force their way through your lens.  Your only alternative is to be reactive and block everything the troll ever says in the future.

To boost numbers, Twitter also encourages the use of bots.  Your human friends have a cap on how many tweets than can make per hour or minute.  Bots don’t. Your stream starts to get polluted with trolls and bots. You get tired. You feel attacked.

So how do we avoid this?  First, we need to build a culture in Mastodon that numbers don’t matter. It’s about the conversation, not the monetization.

Second, we -note how I bravely use the royal “we” knowing I can’t code this thing, 😉 – might want to pay attention to code or sign-up provisions to verify that there’s a human at the keyboard/phone making those toots.  Machine made toots will just turn the place into a sewage treatment plant.

Third, I’d like to see a two-level lens created.  The first lens is the existing Home stream: it’s a subset of the public timeline stream where all I can see are those people I follow and anything directly connected (like a mention or reply or boost) by/about one of my followees.  This lens should be done at the server level.  It’s what I should get back when I refresh.

But what if I could define for myself (user defined) a second-level lens:  a subset of  my followees.  In the user interface, I can turn the secondary lens on-or-off. I could define 2, 3, or so different second level lenses.  Selecting a lens means I see my home stream as if I only had that subset of followees as my entire stream.  This would enable folks to deal with their social connections as they would in real life. My home stream is the comments of everybody I know and care about in my life. But I am surrounded primarily by academics when I’m at work – it’s my academic secondary lens that’s activated there.  When I go home, I turn off the academic lens and put on my family-neighbors lens.

A user-defined lens would also allow me to more frequently watch/monitor my stream for the people that I consider time sensitive. For example, for me the folks I think of as “open ed academics” are people I want to monitor frequently during the day regardless of what they say.  The folks I follow that are more techies – say WordPress or Mastodon developers, are folks that I want to know what they’re saying/thinking, but I might only want to see / hear it once a day.  I could do that.

The lens concept, by being user/viewer defined, also means we don’t have to have social agreement a priori on a hashtag, or who’s in or who’s not.  I see the room as I want to see it.  I might think of you as part of my “open ed” lens.  Assuming we follow each other, you might want to see my stuff as part of your “white guy blowhards” lens.  To each their own.

The lens concept also allows a user to see less of a possible troll without necessarily having to permanently block them.

Well, that’s my $0.0185 worth.  (inflation has reduced the value of two cents).

Who’s Zoomin’ Who?

How do you know that? Why do you think that?  How does that make any sense?  

I was a highly opinionated child with a lot of crazy ideas. But my Dad was patient. He never told me “that’s crazy” or “that’s wrong”.  Instead he usually greeted my pronouncements with some variation of those three questions and often he strung them together into a dialogue.  I’d answer and he’d ask the next question or repeat the first.  At some age, I don’t really recall when,  I began to internalize those questions and the resulting dialogue.  When I got to college I had the chance to study rhetoric and semantics. I added my own questions to his three.

Why these words? What do they want me to think/feel/do? Why are they saying this?

I guess these questions are what the education folks call “critical thinking”. What I know is that we’d be better off asking these questions when we read. I’ve been reading lots of stories, tweets, and posts about “fake news” websites and the need for improved “fact-checking” and digital literacy.  But I’m not too sure we’re getting at the problem. The problem is a lack of critical thinking as my Dad would have approached.  Instead, people seem to be emphasizing the following questions:

What are the “facts”? Is this true? Is this a “legitimate” news site? Should I trust this source? How do we filter out the “fake news”?

These are the wrong questions. They won’t lead to critical insight. They’ll only lead to more deception and propaganda.  I see two problems with these questions people are posing.

First, everything cannot be reduced to some “fact” status as either true or not true. I don’t want to get into some deep philosophical exploration of the nature of truth, I just want to point out any statement of the future  or intentions is inherently speculative and cannot be “fact checked”. All statements of policy intents are statements about the future.   A person can lie about their intents (and even lie to themselves) but it cannot be “fact checked”. The lie can only be challenged by building an argument of reasoning why the person should not be believed. Further the class of things that can be called “facts” includes only objectively verifiable things. Yet subjective things matter too. Feelings, preferences, and perceptions cannot be “fact-checked”. Culture is made of more feelings and perceptions than it is facts.

I could elaborate on the inadequacy of “fact-checking” and likely will in some future post, but right now I want to focus on the second issue: the problems involved in focusing on “legitimate” vs. “fake” news sites.  This isn’t really critical thinking at all. It’s a reliance on authority as the sole arbiter of truth. It’s actually the approach that says we don’t have to engage the actual message itself and critically think about it. This approach advises to divide the world into approved “legitimate” news sources, presumably nice establishment entities such as the New York Times, or Washington Post, or ABC/CBS/NBC/CNN.  I suppose whether Fox News qualifies depends on whether you’re Republican or Democrat.  But other sources are deemed suspicious and likely to be “fake”.  Folks, the problem isn’t whether the news publisher is “legit” it’s whether the news story itself is “legit”.  Big difference.

Let me use a story that has made the rounds in the last day or so.  The Washington Post published a story with the headline:
Russian propaganda effort helped spread ‘fake news’ during election, experts say

Almost instantly, the Twittersphere and blogosphere lit up with mostly unhappy Clinton supporters claiming this is the biggest news story and everybody is missing it.  And yet, the Washington Post site fails on all my Dad’s questions. There’s nothing really there. And when I ask myself about their semantics and ask myself “cui bono?” from this piece, I find it seriously lacking.  I don’t have to take it apart for you because Fortune magazine and journalist Caitlin Johnstone, quoting Glenn Greenwald, did it for me.  You can read for yourself:

Fortune:  Russian Fake News

Caitlyn Johnstone on Newslogue: Glenn Greenwald Just Beat The Snot Out Of Fake News Rag ‘The Washington Post’

(update 28Nov2016: An even better critical thinking take-down of the Washington Post article from William Black at New Economic Perspectives: The Washington Post’s Propaganda About Russian Propaganda )

I’ll reiterate what I’ve said on Twitter and FB.  We shouldn’t be calling out “fake news” sites. We shouldn’t even be calling out “fake news”.  We should call it what it is: propaganda.  Calling it “fake news” will mislead us and get all of us into trouble.  It leads to binary thinking: is this “true” or “fake”?  The problem is propaganda. The most effective propaganda is neither true nor fake. It contains at least some elements of truth or facts but uses rhetorical sleight of hand to get you to believe something you really don’t know. We used to call it spin, but I guess that’s gone out of style.

Let’s remember “legitimate” news sources can and often do deliver propaganda, “fake news” if you will, just as easily and even more effectively than any “fake news sites” spun up by some troll teenager in his basement.

I’m old enough to remember that the legitimate news sources delivered the news to us about Gulf of Tonkin incident and Saddam Hussein’s weapons of mass destruction and anthrax.   Those were propaganda, “fake news”, spun up to work the nation up to war. They worked unfortunately and hundreds of thousands died. Indeed, the march to war is always accompanied by the whole hearted support of the merchants of death and the “legitimate” news sources.

Crying “Russians! Russians!” is dangerous. Accepting such stories uncritically is even more dangerous.  It allows people, especially establishment Democrats, to ignore their own culpability in creating this disaster of an impending Trump presidency. But even more dangerous is it feeds the war machine. We have a populace that wants to look elsewhere to blame their problems: Republicans want to blame Arabs, Muslims, and immigrants.  Now Democrats are crying to blame Russians.  That way lies madness. Let’s remember, when it comes to world wars, it’s three strikes and we’re all out.

So I humbly ask that we all ask ourselves as we read these days: Who’s zoomin’ here?

hat tip to the Queen of Soul, Aretha Franklin for the inspiration for the post.  Enjoy:

 

 

Critical Analytics: It’s Stories All the Way Down

I’ve been hearing much lately about stories, narratives, analytics, data, and “big data”.  I have no need to call out exactly who or which pieces of writing. You know who you are. My aim here is not to criticize, oppose, or take sides. It’s to take a brief critical look at what’s being discussed.

Much of the discussion strikes me as one tribe (I’ll call them non-quants) pleading that stories and narratives are important too!  All of which is an understandable reaction to how the other tribe (I’ll call them quants) have seemingly gained a favored position and perceived superiority at divining the “truth” because they are evidence based!  Because data! I’m actually a member of both tribes and find the posturing of stories and narratives as alternative to quantitative analysis disheartening.

The most encouraging blog piece I’ve read recently comes from Michael Feldstein.  In his lengthy (and excellent) post called Analytics Literacy is a Major Limiter of Edtech Growth.  Please do read it.   He argues for the dissolving this false juxtaposition between “stories” and “data”.

…some of these arguments position analytics in opposition to narratives. That part is not right. Analytics are narratives. They are stories that we tell, or that machines tell, in order to make meaning out of data points. The problem is that most of us aren’t especially literate in this kind of narrative and don’t know how to critique it well.

I wholeheartedly agree.  Feldstein is (correctly) arguing that data points are nothing without stories.  The meaning we take from the data is itself nothing but a story we weave using the data points as we might use punctuation or particular words.  In essence, quantitative analysis is itself a story.

This really isn’t news or at least it shouldn’t be.  I remember how powerful McCloskey’s Rhetoric of Economics was for me when I read it decades ago.  McCloskey powerfully made the point that no matter how much we wrapped an idea in data, mathematical formalism, or econometric analysis, everything we said in economics was just a metaphor or a story we imposed on the data. Alan Grossman long ago pointed out that even that high temple of data-driven evidence, Science(tm), it’s still just rhetoric and it’s still just stories.

Yes, the meaning we attach to a set of data is itself a story.  So stories are not alternatives to data. Data is a story.  But it’s not just the obvious story we tell with the data. There’s a story unstated underneath the data the we use. Our choice of particular data variables constitutes a story itself. We (or at least the data collector) have in mind a story and narrative of what’s important before they collect the data.  They don’t collect data about the context that they don’t see as important or relevant (or easy enough to collect), so they assume a story about that uncollected contextual data holds no meaning.  There’s a story underneath the story we told with the data.

But it keeps getting deeper. Much like the philosophical turtles, it’s stories all the way down. That measure of the data you’re using. The one you think is just basic stats or math, something like the average (properly called arithmetic mean), or the variance, or correlation, or whatever.  It has a story too.  Let’s take that arithmetic mean (average) and each observation’s difference from the average. We think of that average as “the norm” – but that’s just a story invented by a couple of different statisticians in the 19th century.

I can’t really do justice here to the story of how that story of what the average or norm is.  I strongly urge you to read The End of Average by Todd Rose.  It’s fully accessible to members of both tribes, quants and non-quants.  You’ll never use your quantitative data the same way again. Todd Quinn writing in the Elearning magazine of the ACM had the same kind of dramatic reaction as I had.

I’ve finished reading Todd Rose’s The End of Average, and I have to say it was transformative in ways that few books are. I read a fair bit, and sometimes what I read adds some nuance to my thinking, and other times I think the books could stand to extend their own nuances. Few books fundamentally make me “think different,” but The End of Average was one that did, and I believe it has important implications for learning and business.

Rose’s point is pretty simple: All our efforts to try to categorize people on a dimension like GPA or SAT or IQ are, essentially, nonsensical.

But going another level down, as Rose explains in End of Average, there are assumptions beneath the calculation and use of ordinary stats like the average or the variance.  Let’s face it, “assumptions” is another way of staying “believed a story to be so true that it didn’t need to be stated”.  In the case of the average and the calculation of differences from “the norm”, that assumed story has to do with the ergodic properties of what’s being examined.  So what’s “ergodic  properties”? Well here’s Wikipedia’s attempt to explain ergodicity. It’s not very accessible to non-quants (or even most quants!).  Again, I would refer you  to Rose’s book for a beginning glimpse of what ergodicity means. I can’t explain it here, but the essence is that mathematically, statistically the vast majority of the stories being told with quantitative analytics are complete nonsense. Garbage. Invalid. Wishful alchemy.

It’s stories all the way down.  At first this might seem discouraging. But it’s not. I’m calling for not just analytics literacy but a critical analytics.  We need to investigate and become aware of not only the stories we tell using data, but also the assumed stories we slide under the table by choosing particular measures and statistical techniques without thinking about them. We wouldn’t let the semantics of narratives escape critical examination. Why should we let analytics?

 

Brexit, Trumpworld, and the Future of Open Ed: A Topic for OER17?

The deadline is looming in a few days for next April’s OER17 conference in London. I’m not even sure yet if I can make to the conference yet but the events of the past week seem to me compelling to us.
I’m thinking of proposing a panel discussion to discuss Open Education in a time of Brexit, Trumpworld, & whatever other shifts to the hard right happen before April. Specifically we would look at not only whatever threats the political shift from globalized neo-liberalism to far-right nationalism might mean, but more importantly in my opinion other issues:
  • examining the idea that open, connected, learning is more important than ever, and that open, connected, learning is the vehicle by which we combat long-term these trends
  • the implications for the more decolonization and opportunity in the rest of the world, after all, Brexit-Trump-Putin etc is pretty much a Euro-North American phenomenon.
  • what hidden opportunities might this shift away from neo-liberalism offer?
  • how might we change our approach to promoting open, connected education?
Martin Weller has already offered some thoughts from last September in Open Education and the Unenlightment.  I intend to blog heavily in the coming months on the subject and also include my Comparative Economic Systems class in the work.
Here’s the catch. I really don’t want to create another all-white-male panel.  We need more voices. If you’re thinking of attending OER17, interested in being part of it, and you don’t look like me (lucky you!) please contact me ASAP.  Either follow me on Twitter (@econproph) and DM me, or email me at   econproph(at)gmail.com.