The proliferation of digital information technologies and related infrastructure has given rise to novel ways of capturing, storing and analyzing data. In this paper, we describe the research and development of an information system called Interactive Knowledge Networks for Engineering Education Research (iKNEER). This system utilizes a framework that combines large-scale data mining techniques, social network mapping algorithms, and time-series analysis, to provide a mechanism for analyzing and understanding data about the engineering education community. We provide a detailed description of the algorithms, workflows, and the technical architecture we use to make sense of publications, conference proceedings, funding information, and a range of products derived from research in EER (also known as knowledge products). Finally, we demonstrate one possible application of iKNEER by applying topic modeling techniques to a subset of the data to identify the emergence and growth of research topics within the community thereby illustrating the unique epistemic value of this knowledge platform. The system can be found at http://www.ikneer.org.