Watch the full video of Nash’s EdLab talk here.

Cynthia is presenting at the National Council on Measurement in Education (NCME) Annual meeting:

Padraig will be presenting at the America Educational Research Association (AREA)

Players of epistemic games – computer games that simulate professional practica – have been shown to develop epistemic frames: a profession’s particular way of seeing and solving problems. This study examined the interactions between players and mentors in one epistemic game, Urban Science. Using a new method called epistemic network analysis, we explored how players develop epistemic frames through playing the game. Our results show that players imitate and internalize the professional way of thinking that the mentors model, suggesting that mentors can effectively model epistemic frames, and that epistemic network analysis is a useful way to chart the development of learning through mentoring relationships.

Full article can be viewed here.

http://epistemicgames.org/eg/wp-content/uploads/graesser-automentor-0410141.pdf

We identified the speech act categories and clusters of discourse comments of journalism mentors who interact with students editing news stories. Two important speech act categories are evaluations and suggestions. Latent semantic analysis and principal components analyses helped us discover clusters of comments involving evaluations and suggestions. The comments of mentors were also significantly aligned with epistemic frame elements that motivate the comments at a deeper level of discourse and pedagogy. Such alignments were validated by logistic regression analyses on a sample of hand-coded judgments of the frame elements. There was some modest transfer from a journalism practicum corpus to a game corpus. These analyses provide an important first step in building a virtual AutoMentor for multiparty epistemic games.

View the winning paper here.

Working as a mentor in an epistemic game is also very difficult.

We work with a limited amount of information about what the players are doing: we chat with the players via instant message, but we can’t see the players screens, can’t read their body language, and can’t hear the live conversations that they have offline.

So we are thrilled when we get information from our partners who are on the ground with the players. Here are two, from two versions of Urban Science we are running right now:

1. “Just thought you should know that I found out today that Amanda [not her real name] goes home and talks about this program every week non-stop to her parents over dinner. So, she’s pretty excited about it and enjoying herself.”

2. “That was so cool today. The changes you have made since last year have really made a difference. The access to chatting with mentors and the ability to track the targets as the changes are being made in the maps make all the difference. On the way to lunch, I overheard kids talking about the changes they made and getting close. The child with Aspergers was really engaged and wanting to make the target changes.”

While we were talking, one of her students walked into the room. The teacher enthusiastically told her that the class would be playing Urban Science again this spring. The student looked at us and wordlessly unzipped her coat to reveal the Epistemic Games t-shirt that all of the players got the previous year.

While I don’t want to go too far in interpreting the synchronicity of this encounter, I couldn’t help but think that 5th graders do not make sartorial choices lightly. It can sometimes be hard to know the inner transformations that happen as kids are learning and growing. But every once in a while, if you are lucky, you can get an unzipped glimpse of what kids take with them.

http://epistemicgames.org/eg/wp-content/uploads/ICLS2010_pnash_submission_final.pdf

Players of epistemic games–computer games that simulate professional practica—have been shown to develop epistemic frames: a profession’s particular way of seeing and solving problems. This study examines the interactions between players and mentors in one epistemic game, Urban Science. Using a new method called epistemic network analysis, we explore how players develop epistemic frames through playing the game. Our results show that players imitate and internalize the professional way of thinking that the mentors model, suggesting that mentors can effectively model epistemic frames, and that epistemic network analysis is a useful way to chart the development of learning through mentoring relationships.

A recent hoax, in which the Manhattan Airport Foundation proposed a new airport be built in Central Park, fooled both the Huffington Post and Inhabitat, a weblog about sustainable design.

The Foundation’s site is convincing. The site uses slick design, planning language and high standards of presentation, but funniest of all, justifies itself by touting the environmental benefits (with scientific studies cited) and the variety of stakeholders who support it.

That it is even possible to make such an absurd proposal so convincing speaks to the complicated nature of land-use decisions and urban planning in general. It reminds me of one team of Urban Science planners who technically made the numbers work in a land-use proposal by turning the Overture Center, Madison’s premier arts and cultural center, into a parking lot. They presented their plan to live stakeholders, who were predictably outraged, and that team learned a valuable lesson about thinking through all the consequences of their decisions.

Maybe the folks at the Huffington Post would benefit from playing Urban Science…

Epistemic games have been developed to help players develop domain-specific expertise that characterizes how professionals in a particular domain reason, communicate, and act (Shaffer, 2006; Shaffer & Bagley, 2009). Grounded in a sociocultural and sociocognitive approach to learning, epistemic games are designed to foster situated learning that leads to data structures with high levels of dependencies. As one might expect, traditional measurement models struggle to accommodate such contextual dependencies, especially when data are collected at smaller scales and epistemic network analysis (ENA) has been developed to provide a practically feasible modeling alternative (e.g., Rupp et al., 2009; Shaffer et al., in press). In this paper, we describe a research program that addresses key statistical considerations for modeling data from epistemic games using ENA with an eye toward representing different learning progressions of players within such games. Current approaches for representing learning progressions using ENA are juxtaposed with approaches for simulating such data using particular statistical constraints.