It seems reasonable to claim that since Q&A is about learning, Aardvark’s network-based Q&A site can be described as a type of learning network. The less obvious but, in my opinion, more interesting claim is that the design of learning networks can (and should!) be viewed as a social search problem. Illich argued this, Meetup demonstrates it quite nicely, and Grockit is moving in that direction, too. In an email exchange published last year, I suggested that the future of AI in Education will have a lot to do with social search:

I think the AIED systems of the future will be less about teaching directly, and more about providing guidance: when and how a student would benefit from working with someone else (perhaps a teacher, tutor, or peer.) When I get stuck solving a particular type of problem, who (that’s online and available) can best help me understand it? A good system will have predicted the frustrating challenge, and will have already lined up the person best-suited to explaining it to me in a way that I will understand. After I’ve demonstrated that I mastered the necessary skills, who can I then explain it to, both to help them and to clarify it for myself? A good system will be able to seamlessly coordinate this process. Through these interactions, the system will unobtrusively be learning more about me — both as a learner and as a peer-tutor — in order to improve with time.

Downright Aardvarkian.

I’m happy to announce that Grockit will be offering its paid summer research internship program for the summer of 2010. This is the second year that we’re doing this (thanks again, Angela!), and I think it’s a great opportunity for doctoral students to apply their own research experience to a system that a large (and growing) community of learners uses everyday. It’s worth mentioning that Grockit has a large and interesting set of educational data, a variety of research interests, a very talented team, and a fantastic work environment. I just posted details about this program (with an application form) on the 2010 Summer Research Internship, and I encourage you to check it out.

I wanted to share a few thoughts on why we’re offering this, what we have in mind for the program, and why you (or perhaps someone you know) should consider applying.

Grockit, as you may know, is a San Francisco-based web startup building a platform for — and a community around — synchronous collaborative learning games. We strive to provide our growing global network of learners with a smart platform informed by peer assistance and adaptive support. Towards this end, we’re constantly exploring new ways to support collaborative learning online, and we’re frequently examining and applying techniques for analyzing the learning data that we’ve been collecting. One reason that we’re offering this program is to expand on the ways in which we pursue these goals.

Two of the challenges in studying computational systems for peer learning — both of which I faced in completing my own graduate work — is that these systems can take quite some time to build, and it can often take even longer to cultivate a sufficiently large community of participating learners. As a result, the time required to get from hypothesis to data analysis can be (or at least can feel) quite long. At Grockit, we’ve been making good progress with regards to both challenges, and hope that this internship will provide an enterprising graduate student with the opportunity to speed up this process for their own research questions.

In addition to the research opportunity, we’re offering a program stipend, an accommodation stipend, and a travel stipend. You’ll also get a healthy breakfast and lunch cooked in the office every weekday and the chance to spend your summer in vibrant San Francisco. So if you are a doctoral student studying in a university in the United States and interested in applying for a summer research position with us, I’d encourage you to submit an application.

The deadline is March 1, 2010, and you can apply today.

I’d love to hear your feedback, so please leave a comment about QuestionLab on the DML site!

Update: The public viewing/commenting period is now closed. For the curious:

QuestionLab: Using inquiry to power a community of peer learning online

QuestionLab encourages inquiry and collaboration through social online games. By expanding on Grockit’s live web-based collaborative learning platform, QuestionLab complements the existing focus on students answering challenging questions with a new activity engaging small groups of students in asking/authoring their own questions, assessing peer responses, and sharing their work via automatic publication to the Connexions OER Content Commons.

While Grockit games are primarily built on instructor-written multiple-choice questions, QuestionLab supports student-written open-ended questions. Using a real-time collaborative editing environment, small groups write STEM-oriented questions of interest to them. Students are encouraged to draw on Open Educational Resources in composing these questions, and QuestionLab affords doing so directly from Connexions, a “Content Commons” of free and open-licensed educational materials. By attaching descriptive tags and estimating question difficulty, students shape which of their peers are later presented with that question.

Complementing this new collaborative inquiry activity is an expanded form of Grockit’s existing problem-solving game, in which students work alone, with peers, or with a teacher to research and answer questions. Students specify their topics of interest and a Grockit game is prepared based on those criteria. After seeing a question and submitting an answer, the group reviews and assesses past responses and can award points and achievement badges to past respondents and/or the question authors. All student work produced within QuestionLab will carry a Creative Commons license and we automatically be externally published to Connexions.

QuestionLab leverages several strengths of Grockit’s platform: its social and game dynamics that motivate engagement, its growing global network of peer study groups, and its popularity both in and out of school. By fueling these games with the products of student inquiry, peer-assessment, and open educational resources, QuestionLab creates a culture of participatory learning among a community of peers.

My favorite find, so far, is in the billing controller:

I read this as a sign of a Lean Startup in action.

My bicycle has a flat tire, so I’ve recently been walking to work. I seem to end up spending much of this time thinking about how to be less late when commuting without a bike. While I always follow the same route when biking to work — one that balances total distance with hilliness — I have found no clear best route for commuting by foot. My path changes the most in the Mission, where the ground is flat and the streets are on a grid. I never walk any more than necessary to get there, but I hate the idea of standing still at an intersection while waiting for a light to turn green.

The beauty of walking is that the sidewalk grid is more detailed than the road grid. A standard intersection has two crosswalks on each of the four corners. If you are trying to walk northeast, a North-facing red light isn’t a slow-down, since it will be accompanied by an East-facing green light. Since I can’t really remember which intersections have lights, I can’t really plan ahead too much. So I commute by heuristic.

So this is the sidewalker’s dilemma: When arriving at a street corner en route from Point A to Point B, how do you decide whether to turn or to walk? I’ll lay out a few variations to make things interesting. Feel free to leave your heuristics (or additional variations) in the comments:

1. Let’s start simple: There is a rectangular grid of streets with sidewalks on either side. Your work is n blocks over and m blocks down from your home.
2. Some intersections have traffic lights, but you don’t remember which ones. When you arrive at a street corner, you can see the color of the lights at that intersection.
3. You can look a block ahead of you to see if there is a light at that intersection, but you don’t know what color the light will be by the time you arrive.
4. Each light is on a different schedule, and the schedules seem to change. Lights have pedestrian crosswalk signals that count down seconds before the light turns. You can see these numbers as you approach the crosswalk. You can muster up the energy to sprint a bit to get to the intersection in time to make the light. But only some maximum number of times during the commute, because you’re not really a morning person.
5. You remember a few of the intersections that have lights and a few that do not.
6. There are several coffee shops in the neighborhood. You know where they are, you know you want a coffee, and you don’t really care where it’s from.
7. You’re running too late to walk. You head to the nearest bus stop.

Does this describe your commute? What’s your strategy?

My bike has had a flat tire for longer than I’d care to admit, so I’ve been commuting recently by foot and by bus. This leaves me with plenty of time to think, but mostly just thoughts about walking and/or taking the bus.

If you’ve spent time waiting for erratically-timed buses, you know this question well: Will I get there faster if I continue waiting or if I start walking? You can look to recent literature in recreational mathematics for some general guidance on this dilemma [1] [2]. But while waiting may pay off for the lazy mathematician, I like to get some exercise in the process. So here’s a twist on the problem: How far can I walk without missing the next bus? I’ll propose three practical solutions:

1. Walk backwards, so you can spot the bus as soon as it becomes visible. When you do, make a run for the closest stop (preferably in the direction of your destination.)
2. Move to a city that displays bus arrival predictions at bus stops. Run to the first such display, check out the next arrival prediction, and do some quick mental math as you start to walk.
3. Live somewhere that offers open access to city datasets including real-time bus location and prediction feeds. Write the following iPhone application, and then email me when it’s ready.

App description – Given your current location (determined by GPS), your walking speed (based on past commutes), and your destination and bus route (stored in preferences or assumed based on time-of-day), it calculates two commute options and displays each with an estimated time of arrival. The first option minimizes your commute duration while maximizing the portion traveled by foot, and the second option minimizes your foot-only commute time. For example:

Your best commute options today are:
* ETA 8:55am – Walk to Church St, then get on #48. (0.5mi exercise)
* ETA 9:05am – Walk directly. (2.5mi exercise, saves $2.00)

Any takers? Seems like it would be a great candidate for the DataSF App Showcase.

I, too, have generated statistics on various spellings of particular words, and have just yesterday posted them online. This includes about 100,000 spellings of about 3,000 English words, collected through the SpellBEE activity, as part of my dissertation work. So if you’ve been wondering about the relative frequency of various misspellings of the word “accommodation”, you can now rest easy: check out spellbee_errors1.txt. Details, descriptions, and downloads are here.

I’m looking forward to see what else is in the book!