This “active essay” put out by MIT’s medialab illustrates how emergence works. You can really see how the patterns form out of simple rules; maybe this is the best way to teach the concept. Note that the Java applets didn’t work properly on my Mac using Safari, though they seemed fine on Windows and Firefox.
Category: Ed Tech
The “Ed Tech” category includes posts about educational technology products themselves, including LMSs and other learning platforms, adaptive learning and other digital curricular materials products, learning analytics, and educational apps of all types. It also includes technical aspects of ed tech products, especially interoperability.
-
More Emergence Hoo-Ha
I got an email this morning calling my attention to the existence of something called the “Emergent Learning Forum.” I don’t know this group and I don’t know what they mean by “emergent learning”; my previous posts on emergence were in response to articles that have appeared in eLearn and the echoes of them that I have seen in the blogosphere. For the record, I’m quite open to the possibility that the concept of emergence can be used fruitfully to improve our efforts at organizational learning and other forms of knowledge sharing. However, emergence is also a particularly hard concept to wrap one’s head around, and that the conversations about it that I have seen so far haven’t yet gotten to the heart of the idea.
-
Credit Where Due
I should have pointed out in my last post that Stephen Downes has already made the point that the term “emergence” is being misused in the context of “emergent learning.”
-
"Emergent Learning" Is an Oxymoron
In the introduction to Steven Johnson’s oft-referenced but seldom understood book Emergence: The Connected Lives of Ants, Brains, Cities, and Software, he describes emergent systems as follows:
In the simplest terms, they solve problems by drawing on masses of relatively stupid elements, rather than a single, intelligent “executive branch.” They are bottom-up systems, not top-down. They get their smarts from below. In a more technical language, they are complex adaptive systems that display emergent behavior. In these systems, agents residing on one scale start producing behavior that lies one scale above them….[emphasis added]
-
Blogging: A world stuck on itself
This article by VC David Hornik does a great job of summing up my own feelings about social software in general and weblogs in particular. Here’s an excerpt:
We’re at the very beginning of the evolution of social software. In the coming years, we are all going to learn well more than we already know about how people interact with this technology and vice versa. And for the time being, start-ups still have the upper hand.
Social software, as a general matter, is a good idea. But in the particular instances we’ve seen to date, there are a lot of things that make little sense, provide little value and will not sustain the interest of the users.
Yet over the last 12 months, we have all done about as much talking as we have building. It is time to call a moratorium on the “blah blah blah” and get down to the business of building great software. To paraphrase Kenny Rogers, there’ll be time enough for talkin’ when the building’s done.
-
The Problem with "Emergent Learning:" Informational Cascades
I just submitted an article to eLearn Magazine that starts to get at one of the reasons why I am skeptical about emergent learning as a panacea. I’m not going to give away too many of the surprises here (unless eLearn decides not to publish the article, in which case I will post it here), but let’s just say that you can find the roots of the argument in the article Conversation, Learning, and Informational Cascades [PDF]. From the abstract:
We offer a model to explain why groups of people sometimes converge upon poor decisions and are prone to fads, even though they can discuss the outcomes of their choices. Models of informational herding or cascades have examined how rational individuals learn by observing predecessors’ actions, and show that when individuals stop using their own private signals, improvements in decision quality cease. A literature on word-of-mouth learning shows how observation of outcomes as well as actions can cause convergence upon correct decisions. However, the assumptions of these models differ considerably from those of the cascades/herding literature. In a setting which adds ‘conversational’ learning about both the payoff outcomes of predecessors to a basic cascades model, we describe conditions under which (1) cascades/herding occurs with probability one; (2) once started there is a positive probability (generally less than one) that a cascade lasts forever; (3) cascades aggregate information inefficiently and are fragile; (4) the ability to observe past payoffs can reduce average decision accuracy and welfare; and (5) delay in observation of payoffs can improve average accuracy and welfare.
-
Great Breakdown of Issues for Higher Ed Distance Learning
Despite its imposingly academic title (“Four Families of Multi-variant Issues in Graduate-level Asynchronous Online Courses”) this article provides an accessible and pragmatic breakdown of problems confronting the development and evaulation of higher ed distance learning programs. There are too many good insights in the article to list here, so I’ll just give one as a sample:
In ABCD 888, several individuals withdrew from the course throughout the semester. Of the original students who enrolled in the elective online course two took incompletes and five individuals (one female, four males) withdrew. One cited that he felt that the course required too much time and effort while others cited personal reasons. Interestingly, still others cited that they used a strategy of enrolling in “extra” elective course and then later withdrawing from one or more of the courses as part of their online degree-seeking strategy to enhance their academic success possibilities. This enrollment/withdrawal strategy allowed them to ensure adequate number of semester hours as well as enabled them to due a risk analysis regarding the convenience, academic demands of a course, and potential benefits for the student’s overall degree plans. This strategy was most often cited in elective course offerings and was seen as a benefits rather than being considered by those who withdrew as being particularly problematic. Unfortunately, this strategy can also send the wrong message to administrators seeking to fill online classes and question the completion and retention numbers. Interestingly, convenience and time saving educational options are both marketing terms and phrases intended to attract the distance education consumer, but at the same time call into question the issues of institutional credibility, academic rigor, standards, and accreditation. Yet, retention and student satisfaction as a merit standard for course and faculty excellence continues to be employed throughout academia.
In other words, the ease with which students can add, sample, and drop courses without rearranging their day schedules means that there will tend to be much more churn in distance learning classes. This isn’t a problem per se unless you are evaluating class success based on retention rates, in which case the increased use of the “try before you buy” strategy that distance learning affords to students can be misread as a strong negative reflection on the quality of the courses being offered. Which, in turn, tempts the institution to make the courses more “fun” at the expense of academic rigor. I wouldn’t go quite as far as the authors seem to go in terms of rejecting student retention and satisfaction as valid measures of course success, but the cautions that they offer are well worth noting.
Overall, this article is a must-read for people putting together higher-ed programs.