HI Kevin,

Earthquake Rebuild uses game-log-based, Bayesian-network-oriented stealth assessment (SA) to track students’ math-related performance during game play. The help panel will present math problem-solving scaffolds dynamically based on the SA-based performance evaluation. The scaffolds will integrate learner self-control and adaptive support: The scaffold window is activated/presented when the learner’s performance in evaluation indices fall below threshold values, and the learner is urged to interact with the scaffolds (presented in both symbolic and iconic forms) and get gameplay-relevant rewards (e.g., time and material credits) by interacting with the scaffolds.

This is a major challenge. There is a balance we need to achieve between giving the player a lot of freedom and interesting choices and ensuring that they are achieving the learning outcomes. For example, how can we stop the player from spending their time trying to spell their name with the building tools and instead focus on the learning task? We could limit their actions or give them a message if we notice they aren’t doing the learning task, but that threatens the motivational benefits that a good game can provide. Telling a player they are playing the game wrong is frustrating, and not in a good way. At that point, why not create a word problem?

To accomplish this, a lot of our meetings are focused on designing tasks that are intrinsically motivating. We need to create tasks that middle school students will be interested in for their own sake. This is why testing is so important. In our test sessions so far, we discover what the students want to do, what intended learning tasks they are completing, and which one they are not.

In short, it is a hard problem that we solve by thoughtful game design and as much user testing as we can get.

We are still in the design phase, so we have not examined the educational impact yet. However, we have run a number of testing sessions with middle school students. The main purpose of these sessions was to determine how students feel about the game mechanics, what they enjoy about the game, what they don’t like, if there are bugs/poorly designed game elements that are getting in the way, and, most importantly, if our design is creating situations that are conducive to learning. With this information, we iteratively improved the game. User testing is an ongoing and important part of this project, both for improving gameplay and, in the future, for determining educational impact.

We totally agree that teacher input and participation are critical for any game-based learning projects. During the design/development phase, we have been inviting math teachers from a local school and a class of math teacher students from our college of education to review our design prototypes. One teacher in North Carolina has been field-testing our demo episodes in her class during the past semester. The formal in-field testing studies will be conducted this fall and next spring.

At the same time, we are exploring the potential approach to extend Earthquake Rebuild as a participatory design platform, in which a non-programmer, teacher user can design and create a math-problem-based game level following the existing game episode template and the structure of exemplary math word problems. This approach aims to let teachers simply customize the parameters of a math-specific game challenge to create procedurally generated game levels.

Excellent, it is certainly an interesting and innovative way of teaching math and I believe when more teachers learn that they can use your system without being hinder by not knowing programming then it will impact more teachers and students coming to the table and learning math in a very exciting way. Thanks for developing this tool.

Thanks, Deborah!

Hi Jim, great to see your comment! Our current demo episodes are available for download and play. I will send you the link if you send me an email :).
The major performance track is the game-log-based, Bayesian network modeling. Currently game logs can be archived both in local machines and via server uploading. A middleware is developed to extract math-learning-related gameplay data, and Netica is used for developing and validating the Bayesian network models. In Earthquake Rebuild, there is also an internal scratch pad that enables and records students’ calculation and notation for math problem solving. Keywords searching and content analysis will then be conducted with the scratch pad content.