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				The Mapping Lab develops technologies for the future of digital education.
				We create network models of educational systems at all scales.
				We design and create scalable methods and tools for data modeling, data visualization and digital analytics.

				These tools are key to unlocking the value of educational data, to creating personalized pathways and adaptive learning systems, and to achieving data-driven decisions.
			

		

	


	

		

			

				Graph-based data analytics
				
Network Maps

				
Our network maps represent complex relationships. They support scalable graph-based analytics. Example: interactive curriculum mapping and analytics.

			

			

				
				
Tree Hierarchy

				
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Chord Diagrams

				
Our chord diagrams emphasize relationships across entities at multiple scales. Example: relationships among learning outcomes across a curriculum.

			

		

	


	

		

			

				
  Digital maps have changed our lives. 
				Whether we are searching for directions from place A to place B, searching for nearby restaurants, analyzing traffic, or just browsing the map to get the lay of the land, digital maps provide us with rich visual, informative, interactive experiences. 		


				
 Navigating the modern educational landscape  shares many parallels: Learners are often trying to get somewhere (e.g., a job, a certification, or a set of marketable skills). They may want to know what topics or skills are “nearby”. They may want to know what kind of roadblocks to expect along the way. It is hard to imagine navigating the physical world without a map, yet every day learners navigate the educational world mapless.
				

			

		


	


	

		

			

				
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	In the modern era of digital technology and big data, an  educational map  could and should be a richer visual, interactive experience. At the Mapping Lab, we create scalable models of educational data and we create the technologies that extract insights and value from these models. Our models mathematically represent the relationships among the data. Our models manifest as structured data sets that can feed other applications: analytics, interactive visualization, dynamic analysis, and more.		

		

	


	

		

			

				
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The Mapping Lab research is built on scalable and extensible technology.
				To see our most popular by request source code, visit Xoces.js, an interactive nested chord visualization library.
			

		

	


	

		

			

				
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