Who Knows What, and Whom
An interactive portal reveals the state of knowledge and connections among engineering education researchers.
By Krishna Madhavan, Aditya Johri, Hanjun Xian, G. Alan Wang, and Xiaomo Liu
As academic communities grow, they undertake more research, attracting increasingly higher numbers of researchers and practitioners who are interested in that field. The digitization of academic research and a significant amount of federal funding via the National Science Foundation and other entities both accelerate the growth of knowledge. Over time, it becomes hard to know what is known and what combination of processes and people resulted in that knowledge. The engineering education community has reached a stage where these problems of growth are becoming apparent.
Given the vibrant state of engineering education as a field, we have designed a prototype of an online portal that allows community members to quickly learn what is known (what research has been done), who are the primary members and what expertise they have, and, how community members are connected to one another. This information, we believe, can assist newcomers and established members alike to get a good sense of the community’s knowledge and allow them to make new connections and move into new research directions. The prototype is called Interactive Knowledge Networks for Engineering Education Research (iKNEER) and is available online at http://ikneer.org.
Our approach relies on many of the technologies, tools, and data that fall under the umbrella of “big data analytics.” The digitization of information has allowed us to leverage data sets such as publishing records, proposal information, and related data sources. Using these, we have been able to create the initial framework for an ecosystem where metadata about publications in journals, conferences, and other forms are readily available. We have built this ecosystem on top of a technical layer of open-source products that allows us to serve this information to a large number of users. Finally, we have incorporated useful information representation techniques to provide easy-to-use interactive visualizations. This infrastructure, in addition to assisting users, allows us to undertake novel research on computational and information science-related topics.
We have been motivated by two elements of research practice that we wanted to support. First, we wanted community members to learn quickly about research that has been done within the community. We call these epistemic practices. To support these practices, we have implemented tools and techniques such as “topic modeling” that allow us to understand what topics have been addressed and how these have changed over time. The second practice we wanted to support was relational understanding of the community. It is important to know who knows what in order to build collaborative relationships. We have supported this by allowing users to visually analyze who is connected to whom as co-author and the other researchers connected to the co-authors (that is, multiple ties). Finally, we also allow dynamic visualizations so that users can understand changes over time. For instance, we developed a visualization of the Frontiers in Education conference over a decade (http://youtu.be/bKA4zJc3bsA).
Overall, by providing an online portal that allows members of the engineering education community to learn more about the knowledge within their community and the knowledge creators and users, we hope to be able to support the community in the generation of new knowledge as well as its application. The iKNEER prototype has already led to research and development of a much larger and scaled-up system for portfolio analysis known as Deep Insights Anytime, Anywhere (DIA2). An alpha version of DIA2 can be found at http://www.dia2.org. We encourage community members to explore the portal and provide us feedback.
Krishna Madhavan is an assistant professor in the School of Engineering Education at Purdue University. Aditya Johri is associate professor and chair in the Department of Applied Information Technology in the Volgenau School of Engineering, George Mason University. Hanjun Xian is a research software design engineer with the Bing Data Mining team at Microsoft. G. Alan Wang is an associate professor in the Department of Business Information Technology, Pamplin College of Business, at Virginia Tech. Xiaomo Liu is a research scientist at Thomson Reuters R&D.