This is a part of the Math 2.0 Interest Group series of weekly open webinars. We will discuss a few game design books, see what game mechanics correspond to math ideas, and look at examples of favorite (or not) math games. Wednesday, February 17th 2010 we will meet in the LearnCentral public Elluminate room at 6:30pm Pacific / 9:30pm Eastern time: <a class="wiki_link_ext" href="https://sas.elluminate.com/d.jnlp?sid=lcevents&password=Webinar_Guest" rel="nofollow">https://sas.elluminate.com/d.jnlp?sid=lcevents&password=Webinar_Guest</a> The <a class="wiki_link" href="http://mathfuture.wikispaces.com/GameGroup">game subgroup</a> has been focusing on the subject of serious games for learning mathematics since the Fall of 2009. We are working on a conceptual framework for evaluating and designing math games. It is based on the series of decisions in design. Definitions of decisions come from game theory research and gaming studies. The gameplay consequences of each decision are analyzed based on existing games viewed through the lens of these definitions. The mathematics education consequences of each decision are then analyzed based on the pedagogy embodied in the gameplay, and viewed through the lens of learning theories. A series of parallels between gaming concepts and pedagogical notions helps mathematics educators make sense of game theory concepts, and apply these concepts to teaching. The resulting structure makes it clear that some types of math games are overused, and other promising types are rarely employed by mathematics education game developers. The decisions, as well as their mathematics and math education parallels, are made along these dimensions that provide dichotomies, gradients or levels: · Abstraction dichotomy: narrative-based vs. abstract; situated vs. formalized · Revelation gradient: full disclosure to hidden information; open-book to closed-book · Strategic gradient: strategic to typed; problem-solving to exercises · Resource levels: bounded rationality gameplay or not; level or stage learning theories · Agency and autonomy gradient: high to none; open-ended to closed-ended tasks · Planning levels: interactions, tasks, tactics, strategies; order of math tasks · Depth gradient: expert to superficial knowledge; deep learning to expository learning · Goal gradient: sandbox play to clear goals; conceptual learning to procedural fluency Cheers, Maria Droujkova <a href="http://www.naturalmath.com">http://www.naturalmath.com</a> Make math your own, to make your own math.