May 13th, 2017 by Jon Becker

Tonight, my wife and I saw the Circle. I’d read the book by Dave Eggers. Frankly, to me, the book read as if it was anticipating a mkvie. So, I was eager to see how it would work as a movie.

Verdict? It was terrible. And that’s unfortunate. The issues the book raises (social media + big data + privacy) are really important. We need for more people to be thinking and talking about these things. While The Circle is not a perfect story, I thought it could work as an accessible entry point into those conversations. But, the movie is just bad. #sigh

Fortunately, Zeynep wrote another good article in the New York Times that advances some of the issues. I hope lots of people read it.

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February 2nd, 2017 by Jon Becker

So, Audrey Watters becomes the first person to earn a 2nd tweet of the day from me. No surprise, really, since of Audrey Watters, I am a big fan.

Today, I had the distinct pleasure of seeing Audrey again, face-to-face, as she delivered a powerful talk, the text of which she shared via the tweet. Though I know it’s been a big part of her thinking and writing, I didn’t know that Audrey’s focus today would be on data and privacy. For whatever reason, I thought it would be focused more on the automation and teaching machines aspect of her research and writing. It was a timely and powerful talk, though. And, it so happens that it came on the same day that the New York Times published an article on “big data” in #highered. On top of that, shortly before Audrey’s talk, a big red blip came across my Twitter radar screen in the form of a new report called The Legacy of inBloom. The report is accompanied by pieces by smart folks like danah boyd and Bill Fitzgerald. I haven’t had a chance to dig into any of those resources yet, but I look forward to it.

So, yeah, today’s major theme for me was education + data + privacy. And, far and away, the highlight of the day was Audrey’s talk (and the hug I got afterwards).

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November 9th, 2016 by Jon Becker

When the going gets tough… I get reading. That’s how I cope and it’s how I spent much of my day today. In fact, I think I read everything on the Internet about the election.

What follows is not my attempt to explain the outcome of last night’s election. It’s more my attempt to synthesize what I learned today. You might think of it as my key takeaways from my day of coping through reading. I hope it helps you as much as writing it has already helped me.

Racism (and sexism)

Cutting to the chase, this election was, first and foremost, about race. And, it was also about sex.

I’ve been telling anyone who would listen that after 8 years of our first African-American president, when the Dcmocratic party offered up the possibility of the first female president. a critical mass of folks who want us to get back to a white (Christian) patriarchy was awakened. Earlier this year, Michael Moore gave us a a glimpse into the mind of the “Endangered White Male”:

Our male-dominated, 240-year run of the USA is coming to an end. A woman is about to take over! How did this happen?! On our watch! There were warning signs, but we ignored them. Nixon, the gender traitor, imposing Title IX on us, the rule that said girls in school should get an equal chance at playing sports. Then they let them fly commercial jets. Before we knew it, Beyoncé stormed on the field at this year’s Super Bowl (our game!) with an army of Black Women, fists raised, declaring that our domination was hereby terminated! Oh, the humanity!

Here’s Jamelle Bouie making the same claim:

More than anything, Trump promises a restoration of white authority. After eight years of a black president—after eight years in which cosmopolitan America asserted its power and its influence, eight years in which women leaned in and blacks declared that their lives mattered—millions of white Americans said enough. They had their fill of this world and wanted the old one back.

Any of the white male candidates from the slew of hopefuls the RNC sent through primary season probably would have sufficed for these folks.

Trump, however, really emboldened the white nationalists.

Bouie again

With his jeremiads against Hispanics and Muslims—with his visions of dystopian cities and radicalized refugees—Trump told white Americans that their fears and anger were justified. And that this fear and anger should drive their politics. Trump forged a politics of white tribalism, and white people embraced it.

And, there were plenty of white people who embraced it, including and especially those hidden from plain sight. I joked (sort of) last night that the comments section showed up at the polls. If you’ve ever read the comments section on articles in mainstream media sites, you see the anger that’s out there. And, if you know much about the dark parts of the Internet, you know what’s out there.

Siyanda Mohutsiwa has apparently been observing some of the dark parts of the Internet for some time and shared her thoughts on Twitter:

Mohutsiwa’s observations of this population of young white men radicalized online feel important to me. And, I think one of her points is worth emphasizing. She writes, “That’s why I never got one strategy of Clinton’s campaign: highlighting Trump’s sexism. Trump supporters love him BECAUSE of his sexism.” I’d say the same about racism.

Filter bubbles and echo chambers

A filter bubble is a result of a personalized search in which a website algorithm selectively guesses what information a user would like to see based on information about the user (such as location, past click behavior and search history)[1][2][3] and, as a result, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles.[4] (Source: Wikipedia)

Folks, we have GOT to be better about understanding how information is presented to us by Google, Facebook, etc. We can’t uncritically let search and/or news algorithms determine what information we see. Facebook is particularly odious in this regard. Joshua Benton, who formerly pushed back against the idea of filter bubbles, wrote a really good piece today that I encourage you to read. Here’s the kicker:

But I’ve come to think that the rise of fake news — and of the cheap-to-run, ideologically driven aggregator sites that are only a few steps up from fake — has weaponized those filter bubbles. There were just too many people voting in this election because they were infuriated by made-up things they read online.

Some say that Facebook’s filter bubble is getting worse, though Mark Zuckerberg denies it. LOL, of course he does.

These filter bubbles we live in online are augmented by the echo chambers we put ourselves in both online and offline. With respect to our face-to-face lives, Marco Rogers’ tweetstorm really resonated. I’ve included only select parts of it below:


We (white liberals) know exactly what Marco means. We know about our liberal enclaves in the suburbs (I’ll include myself as a suburbanite, though I don’t think my part of town is particularly liberal) and even within inner cities (see e.g. places like Park Slope in Brooklyn). From my perspective, liberals don’t like to admit it, but homophily is alive and well in the way we domicile ourselves. As a result, says, Marco, “[w]hite liberals have walled themselves off from the reality of the racism in their community.”

In sum, then, we have walled ourselves off from the realities of racism and when we go online, filter bubbles reinforce our biases. We deny this and then we’re “shocked” at the results of the election.

Big country

I have vivid memories of my days doing field work as an educational researcher. I spent the better part of a year driving up and down, in and out of the hollows of West Virginia. I remember clearly driving through rural Ohio to find a middle school in Gnadenhutten, Ohio. And I’ll never forget driving from one school to another within the Houston Independent School District (HISD) in Texas. Everything really is bigger in Texas and it would sometimes take me over an hour to get from one school to another within the same school district. And, for much of my time in rental cars driving through different parts of the country, I was thinking about the vastness of our nation and passing random homes or trailers and wondering about the story of the people in those homes. So many homes, so many people. And, I was barely seeing the tip of the iceberg.

I share this to say that I’ve also been telling people that I was worried about the election because our country is so much bigger, so much more vast than most of us know or care to know. And, those vast parts of our country contain lots of people who were disengaged and, who had become increasingly disenfranchised and depressed. In predicting Trump’s victory this summer, Michael Moore wrote about what he called our Rust Belt Brexit:

From Green Bay to Pittsburgh, this, my friends, is the middle of England – broken, depressed, struggling, the smokestacks strewn across the countryside with the carcass of what we use to call the Middle Class. Angry, embittered working (and nonworking) people who were lied to by the trickle-down of Reagan and abandoned by Democrats who still try to talk a good line but are really just looking forward to rub one out with a lobbyist from Goldman Sachs who’ll write them nice big check before leaving the room. What happened in the UK with Brexit is going to happen here.

False positivism

Speaking of Brexit…

I don’t know why we didn’t take to heart the failures of polling around that event. For me, those polling failures came on the heels of a more local polling failure. When Eric Cantor lost his House seat to Dave Brat, the people of the greater Richmond area were completely shocked. The polls gave Brat practically no chance, and, yet, he unseated Cantor.

So, I should be ashamed that I got sucked in by the Nate Silver’s of the world who had me believing that Hillary Clinton was going to win the election. Silver’s estimates were more conservative than others, but even on the day of the election, he was still giving Clinton at least a 70% chance of winning. Alas…

Nathan Jurgenson wrote a great post about “factiness”:

Factiness is the taste for the feel and aesthetic of “facts,” often at the expense of missing the truth. From silly self-help-y TED talks to bad NPR-style neuroscience science updates to wrapping ourselves in the misleading scientisim of Fivethirtyeight statistics, factiness is obsessing over and covering ourselves in fact after fact while still missing bigger truths.

I believe it was on Twitter than Jurgenson was lamenting Nate Silver’s work which he suggested was creating a feeling of “faux precision.” To me, this is part of a larger problem of false positivism. Dave Cormier and Lawrie Phipps co-authored a post about this in which they wrote:

2016 has taught us that we cannot rely on analytics, and in fact analytics may have had a negative impact. The most correct predictions of #Brexit and #Trump came from commentators who were not relying on the polling data but were paying attention to the narratives that were created.

Later in the day, I read a tremendous piece by Rasmus Kleis Nielsen, Director of Research at the Reuters Institute for the Study of Journalism at the University of Oxford in the UK. In the piece, Nielsen calls for more of a mixed-methods approach to data journalism in politics.

I think it is clear we don’t know how most people feel about politics and how it ties in with other aspects of their lives and identities. Yes, we may know that some of them are not very interested, don’t like it very much, or are quite partisan. But what does that actually mean? I don’t think we know… In my view 2016 shows we need to start qualitatively researching the (diverse, fractious, fascinating) majority too, and see whether a better, evidence-based understanding of how people relate to politics and public life can help us get it right next time.

Politics are about human behaviors. To understand politics, therefore, we can’t just treat people as quantitative data points. We need to also understand them as humans; to hear and tell their stories. Instead, we were mesmerized by the whizbang statistical models and fancy data visualizations of Nate Silver and other pundits/prognosticators. What we really needed was to talk to and hear each other at scale.

Moving forward

So, now what? What can we do beyond understanding our filter bubbles, getting out of our echo chambers, and doing more thorough, narrative research? Well, this morning, Michael Moore gave us (well, me; I shouldn’t assume you’re on the same side as Michael Moore) a five-part plan. I think that’s a good starting point.

Additionally, I want to make a plea for empathy and for understanding. On the latter, I learned a long time ago that we all mourn differently. For those of us mourning the loss of this election, I ask you all to not judge how we mourn. It will go differently and at different paces for each of us. On the former, I just think empathy should always lead. Please try not to say things like “It will be OK,” because it might just not be OK, particularly for those particularly at-risk. And, “relax” is about the worst thing you can say. Empathy works so much better. So, moving forward, let’s start there.

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January 29th, 2016 by Jon Becker

How many students are currently taking online courses? How many online programs does the university offer? How many students are enrolled in online programs?

These are the kinds of questions those of us in the world of online learning are asked regularly. They seem like innocuous and straightforward questions. They’re not though. Answering these questions turns out to be unbelievably complicated. “It depends…” is never satisfactory for a key university stakeholder, but the truth is that it does depend… on how we’re defining online learning.

We have to report about online learning at three different levels (state, regional and federal): SCHEV (our state education agency), SACSCOC (our regional accreditation agency), and IPEDS (the federal government).


Here is how SCHEV defines online learning1:

  • FACE-TO-FACE, SYNCHRONOUS: …For formal instruction, the instructor and learner share the same physical space more than 50% of the time (understood in terms of Carnegie credit hour equivalency). The instructor and learner interact mostly at the same time.
  • HYBRID; SYNCHROUNOUS: …For formal instruction, the instructor and learner share the same physical space less than 50% of the time (understood in terms of Carnegie credit hour equivalency). Electronic delivery is used for the balance of instruction. During electronic interaction, the instructor and learner interact mostly at the same time (e.g. video conference, teleconference, or Elluminate® live session).
  • HYBRID; ASYNCHRONOUS: …For formal instruction, the instructor and learner share the same physical space less than 50% of the time (understood in terms of Carnegie credit hour equivalency). Electronic delivery is used for the balance of instruction. During electronic interaction, the instructor and learner interact mostly at different times (e.g. discussion board or podcast).
  • ELECTRONIC; SYNCHRONOUS: …Apart from a face-to-face orientation or initial class meeting, for formal instruction, the instructor and learner use electronic means to interact 100% of the time (understood in terms of Carnegie credit hour equivalency). During electronic interaction, the instructor and learner interact mostly at the same time. (e.g. video conference, teleconference, or Elluminate® live session).
  • ELECTRONIC; ASYNCHRONOUS: …Apart from a face-to-face orientation or initial class meeting, for formal instruction, the instructor and learner use electronic means to interact 100% of the time (understood in terms of Carnegie credit hour equivalency). During electronic interaction, the instructor and learner interact mostly at different times (e.g. discussion board or podcast).

SACSCOC distinguishes only between “distance education” and “correspondence” education. They defines distance education as:

For the purposes of the Commission on College’s accreditation review, distance education is a formal educational process in which the majority of the instruction (interaction between students and instructors and among students) in a course occurs when students and instructors are not in the same place. Instruction may be synchronous or asynchronous. A distance education course may use the internet; one-way and two-way transmissions through open broadcast, closed circuit, cable, microwave, broadband lines, fiber optics, satellite, or wireless communications devices; audio conferencing; or video cassettes, DVD’s, and CD-ROMs if used as part of the distance learning course or program.

IPEDS also uses the term “distance education” and defines a distance education course as:

A course in which the instructional content is delivered exclusively via distance education.  Requirements for coming to campus for orientation, testing, or academic support services do not exclude a course from being classified as distance education.


See the problem(s)? Think of the course where the students and the instructor meet 3 times, once for a course orientation, once for a midterm and once for a final exam. That’s about 20% of the seat time. As far as SCHEV and IPEDS are concerned, that’s either not distance education (per IPEDS the content is not delivered “exclusively” via distance education), or it’s hybrid (per SCHEV). But, SACSCOC would consider that a distance education course because for more than a majority of the instruction, students and the instructor are not in the same place.

Now, imagine the rest of the course involves students reading, taking quizzes, watching videos, regularly communicating asynchronously on a class discussion board, etc. Oh, and the instructor holds the occasional live, synchronous videoconference interview with an expert in the field. For SCHEV reporting purposes, is that an asynchronous or a synchronous course?

And that’s just at the course level, which is just the beginning. Online programs?…

IPEDS says an online program is “A program for which all the required coursework for program completion is able to be completed via distance education courses.”

SCHEV requires us to report in quartiles and the key is around “potential.” So, we have to report to SCHEV any program where at least 25% of the coursework CAN/COULD be completed online (but again, what’s an online course?), and again where at least 50% of the coursework CAN/COULD be completed online.

So, here, again, we have IPEDS defining an online program as ALL and SCHEV defining an online program as, essentially, one where 50% of the coursework CAN/COULD be completed online.

Confused yet?

Consider the following not-so-hypothetical programs. Imagine all are 30-credit programs (10 3-credit courses)


Program A: Is this an online program? Yes, per SCHEV, but no per IPEDS.

Course 1 X
Course 2 X
Course 3 X
Course 10 X


Program B: in this program, for all courses, students meet f2f on occasion; maybe 3 or 4 times for about 1.5 hours each time. Is this an online program? Not according to IPEDS, but for SCHEV purposes, this is probably an online program (because more than 50% of the “coursework” is completed online. But, none of the individual courses are coded as fully online (“electronic”)).

Course 1   X
Course 2 X
Course 3   X
Course 10   X

Program C: this is based on an actual program here at VCU. In every semester, there are at least two sections of every course; one is f2f, one is online. In any given semester, students can choose which section(s) of which course(s) they want to take. This is probably the best and most “student-centered” format. But, is it an online program? Per SCHEV, yes, because more than half of the program CAN/COULD be completed online. Per IPEDS, probably/maybe? 

Course 1 X X
Course 2 X   X
Course 3 X   X
X   X
X   X
X   X
X   X
Course 10 X X

But, here’s the real plot twists: IF this is an online program, how many students are in that online program? In other words, when we’re asked to report on the number of students in online programs, do we count all of the students in this program? Even if they’re, say, halfway through and have yet to opt for one of the online sections?

It’s easy for those not involved closely with these issues to shrug and say things like, “Who cares? That’s just semantics…” or “Isn’t it all just learning?” Well, yes and no. But, for many of us, these kinds of semantic issues have real and highly important consequences. Remember the UVA fiasco? If/when our board members ask if we’re doing “enough” with online learning, “it depends” will not suffice as an answer.


More importantly, I hope that this post has at least three effects:

  1. In the future, when you read any kind of report or see any kind of statistic about the number or percentage of students in online courses and/or programs, you’ll view those with a critical eye. There’s no question in my mind that those reports and statistics are generated based on incomparable data.
  2. More people involved in these issues need to be writing and talking about this, if only to generate conversations that might reach stakeholders and policymakers who don’t yet understand the nuances of online learning.
  3. And, finally, I desperately hope that the nuances are not viewed as just reporting problems. Rather, they are seen as opportunities/possibilities.
  1. based on personal communication with Tod Massa, Policy Research and Data Warehousing Director at SCHEV []

Posted in Education, Online Learning Tagged with: , , ,

December 7th, 2014 by Jon Becker

[This is the latest in a series of weekly posts chronicling examples of learning innovation that come across my Web radar. All of the weekly posts are tagged as twili.]

Looks like this isn’t quite a “weekly” series. I certainly aspire to that, so I’ll keep the name. Plus, the tag is already “twili,” So, twili it is.

I’m mindful that I am writing this on the eve of “Finals Week” at VCU.

Like Martha, I struggle with the whole idea of “Finals Week” or “Exam Week,” and rhetoric like “…survive exam week.” Unless you’re a marine biologist studying sharks in the ocean, or something like that, a learning experience should never be about survival. As I wrote on Twitter earlier this week:

It is in that spirit that I carry on with the “This week in learning innovation” series. I hope the examples I share are the counter-narrative to the hegemony of cramming and final exams. Onward…


The Economics of Seinfeld

It is the simplicity of Seinfeld that makes it so appropriate for use in economics courses. Using these clips (as well as clips from other television shows or movies) makes economic concepts come alive, making them more real for students. Ultimately, students will start seeing economics everywhere – in other TV shows, in popular music, and most importantly, in their own lives.

I’ve been waiting to share this with those who haven’t seen it.  The Economics of Seinfeld is a perfect example of using a different, hopefully fun and/or engaging frame or lens to teach/learn an otherwise not-so-interesting topic. Compiled by two professors of economics and a graduate student, these Seinfeld clips are a great augmentation to a larger economic curriculum. Don’t understand the basic economics concept of “fixed costs?” Here’s a clip from a Seinfeld episode that can help. The site is formatted such that each media clip is a post with its own comment section. That’s potentially fruitful territory for students to hash out some of the topics and for “outsiders” to weigh in on the examples. ““It’s chocolate, it’s peppermint — it’s delicious!” – Kramer”



Econ 201: Principles of Microeconomics – An online game for college credit

Educational games are already being used effectively to supplement traditional teaching. Our approach is to make an entire course a game by embedding the elements of good education—content, communication, interactivity, application, and assessment—into the game format. The resulting course will be not only intellectually and emotionally engaging, but also highly effective in teaching economics as a way of thinking

While we’re on the subject of economics, this is an oldie. In gaming terms, 2006 is a LONG time ago. But, I still show this to people when I can because it disrupts some misinformed thoughts about the potential of gaming for learning. In an interview on NPR, one of the professors who developed the game/course said, “This is a game in which the students are literally immersed in a story. And they take on the role of a character… So all of the reading material, all of the content, all of the examinations and homework, if you will, are built inside the engine of the game.” Imagine that!


Nifty assignments

Nifty — the Nifty Assignments often have a playful sort of “fun factor” to them. They are very visual, or they build a game, or they have entertaining output. The assignments invite the students to play around with the material.

Part of what motivated the “This week in learning innovation” series was a sense that we don’t (yet?) have a great place/space where curricular ideas and/or innovations are archived, shared, etc. In the K-12 space, there is Better Lesson and Curriki and other sites for lesson plan sharing. But, if there are similar sites aimed at higher education, I am unaware of them. I know there are open education resource repositories that contain some curricular material in them, but there’s nothing that I’m aware of that is specifically for sharing interesting learning experiences in higher education.

So, while Nifty Assignments looks like a typical website designed by computer scientists (what is it with them? what do they have against modern web design?), it’s an interesting and living example of how interesting lessons and activities might be shared.  “For each Nifty Assignment there is a short blurb in the bulletin, a 15 minute talk given at the SIG-CSE, and finally a web page…that contains assignment material ready for study or adoption. For each assignment, there is material from the assignment itself — things an instructor can use to study or adopt the assignment…”

Plus, I just like that they use the term “nifty.” It’s so… neato.

If you’re a computer scientist, poke around and dig in. If you’re not, you might find this whole idea… nifty.


Spatial History Project

The Spatial History Project at Stanford University is a place for a collaborative community of scholars to engage in creative spatial, textual and visual analysis to further research in the humanities…Our projects operate outside of normal historical practice in five ways: they are collaborative, use visualization, depend on the use of computers, are open-ended, and have a conceptual focus on space.

While we’re on the topic of Stanford…

Actually, just read that quote above. That’s pretty much everything I point to when discussing how the Web might augment learning. I’m a sucker for interesting data visualization projects, too.

Again, poke around their projects. There’s some really interesting stuff, including the Broken Paths of Freedom Project


Take Back the Archive

Take Back the Archive is a public history project created by UVa faculty, students, librarians, and archivists. It is meant to preserve, visualize, and contextualize the history of rape and sexual violence at the University of Virginia, honoring individual stories and documenting systemic issues and trends.

This has been a trying semester for our friends an hour to our west. This whole Rolling Stone saga is… complicated.

There’s not much to the Take Back the Archive project yet, but I’ll be keeping my eye on what the brilliant folks at the UVA Scholars Lab do here. It strikes me as an attempt to build a learning experience around a very important, if not very difficult topic.

Posted in Innovation, Pedagogy, Technology Tagged with: , , , , , , , , , ,

November 15th, 2013 by Jon Becker

I finally watched Moneyball last night. I read the Michael Lewis book pretty much the day it hit the stores, but hadn’t seen the movie until last night (#ParentingIsHard).

Watching the movie came at a particularly interesting moment for me. I’ve been following closely the developments about the research out of Purdue University about CourseSignals. In late September, Purdue issued a press release with the following headline: “Purdue software boosts graduation rate 21 percent.” (NOTE: Signals was developed at Purdue and launched full scale there in 2008. It has since been released as a commercial product by Ellucian called CourseSignals). That research has been called into question by the great Mike Caulfield. Also, Alfred Essa, who, interestingly, works for McGraw-Hill, ran some simulations to effectively debunk the methods of the Purdue study. Caulfield recently wrote a nice summary of the “issue” and provided some good links for those who want to investigate this issue more.

I’m particularly interested in these developments as I attended an information session a few months ago where Ellucian did, effectively, a marketing pitch about CourseSignals to VCU stakeholders. I don’t know where we are with respect to buying access to CourseSignals, but I have some real concerns. My trepidation pre-dates the Purdue research and the backlash; I raised my concerns at the information session. Specifically, I asked the Ellucian representative about the degree to which a faculty member had control over what triggers the changes from green to yellow to red lights and what triggers a warning communication to a student. The response was something to the effect of, “Right now, those are triggered by algorithms determined by the system, but modifications to that are on the ‘product map’.” In other words, generally speaking, the CourseSignals algorithms were defining student success. This felt like academic advisement by algorithm, which felt… well… icky.

Earlier this week, there was big news out of Carnegie Mellon where the Simon Initiative was launched.

Technological platforms are creating unprecedented global access to new educational opportunities. But we do not know the extent to which students using these systems are learning. As educators and researchers, we must partner to ensure that these platforms not only deliver information, but also include useful metrics, standards and methods that maximize learning outcomes.

Science. Technology. Metrics. Standards. Learning Outcomes. And, “deliver information?” Is that all these new technological platforms bring to the learning table? If you look at the list of the individuals on the Global Learning Council (GLC) as part of the Simon Initiative, you’ll see folks from edX, Coursera, Microsoft, Kaplan, Gates Foundation, Google, etc. There’s not a single learning scientist on that list. Compare that list to those on the National Research Council’s Committee on Developments in the Science of Learning who wrote How People Learn.

It just seems like there’s a critical mass of folks with lots of capital who continue to think that with the right amount of data (the bigger the data the better?) and the right amount of computing power, we can, at long last, figure out these wicked problems of learning and assessment.

I’m not all opposed to the idea that we might be able to tap into the power of modern computing to better help us understand learning. In fact, some would say that I’m a recovering positivist. However, my views on learning have been influenced by the likes of David Perkins and Jerome Bruner. And, my thinking around assessment is in perpetual beta, but I like Gary Stager’s idea that for each student in a course, the goal should be to impress themselves.

I have strong reservations about both grades and rubrics. I believe that both practices have a prophylactic effect on learning. Doing the best job you can do and sharing your knowledge with others are the paramount goals for this course. I expect excellence…. Therefore, I am trying a new experiment this term. You should evaluate each course artifact you create according to the following “rubric.” The progression denotes a range from the least personal growth to the most.

  1. I did not participate
  2. I phoned-it in
  3. I impressed by colleagues
  4. I impressed my friends and neighbors
  5. I impressed my children
  6. I impressed Gary
  7. I impressed myself

Really ponder that for a moment. What if we said to every student at every level that your whole goal for your formal learning experience is to impress yourself? Not to impress the teacher; impress yourself. I’ve tried it with some graduate courses I’ve taught and I think it’s fairly empowering and transformative . I also regularly re-visit Stephen Downes’ post on New Forms of Assesment.

Suppose instead students were rewarded for cooperation. Not collaboration; this is just the school-level emulation of the creation of cliques and corporations. Cooperation, which is a common and ad hoc creation of interactions and exchanges for mutual value.  Cooperative behaviours include exchanges of goods and services, agreement on open standards and protocols, sharing of resources in common (and open) pools, and similar behaviours.

Imagine receiving academic credit for contributing well-received resources into open source repositories, whether as softwareartphotography, or educational resources. Imagine receiving credit for long-lasting additions to Wikipedia or similar online resources (we would have to fix Wikipedia, as it is now run by a gang of thugs known as ‘Wikipedia editors’). We can have wide-ranging and nuanced evaluations of such contributions, not simple grades, but something based on how the content contributed is used and reused across the net (this would have the interesting result that your assessment could continue to go up over time).

A part of Downes’ vision has become reality at the UC San Francisco School of Medicine where students are receiving academic credit for editing medical content on Wikipedia.

I don’t want to setup some false dichotomy here. This isn’t a quantitative vs. qualitative thing. I just think that we can use our advanced levels of computing power to collect LOTS of evidence of learning; data in all forms. As one example of what’s possible, consider social network analysis. From a community of learners, we can use social network analysis to MAP (visualize!) and MEASURE (metrics!) individual contributions in relation to others in a learning network. Here’s one of a growing number of examples of what that looks like.

Seven years ago, after reading Moneyball, I wrote about the book and the principles within it with respect to the so-called “data-driven decision making” “movement” in K-12 education. More recently, I wrote about “big data” and the merits of quantitative approaches to political analysis. In both of those posts, I suggest that our increasing ability and capacity to capture and analyze quantitative data doesn’t necessarily offset our ability to make predictions and decisions based on human observations of our world. It’s not a zero-sum game.

Posted in Education, NMFS_F13, Technology Tagged with: , , ,