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|  | |  |\n|  |  |  |  |  |  |\n\nSUMMER 2014  \nTools for large-scale data analytic examination of relational and epistemic networks in engineering education  \nKRISHNA MADHAVAN  \nPurdue University  \nWest Lafayette, Indiana  \nADITYA JOHRI  \nGeorge Mason University Fairfax, VA  \nHANJUN XIAN Microsoft Corporation Bellevue, WA  \nG. ALAN WANG Virginia Tech Blacksburg, VA  \nAND  \nXIAOMO LIU Thomson Reuters R&D New York, NY  \nABSTRACT  \nThe 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](http://www.ikneer.org).  \nSUMMER 2014 1  \nADVANCES IN ENGINEERING EDUCATION  \nTools for large-scale data analytic examination of relational and epistemic networks in engineering education  \nKey Words: Interactive knowledge platform, academic collaboration, research trends, big data  \nINTRODUCTION  \nIn today’s globally competitive economy, success is increasingly driven by the ability to act on knowledge and intellectual capital. Academic communities that have developed a corpus of knowledge artifacts over decades or sometimes centuries of research are uniquely positioned to capitalize on their expansive knowledge bases. Yet, this process is fraught with difficulties. To be innovative, an organization [or community] has to be adept at exploiting existing knowledge as well as exploring new ways of producing knowledge [1] . To do so, a community must have a holistic, deep, and accessible understanding of what it knows.  \nEngineering education has recently undergone a resurgence and reorientation that mirrors growing recognition of the challenges faced by both engineering educators and learners in the 21st century. A new field of Engineering Education Research (EER) has emerged, in part coalescing around theories of how people learn in the domain of engineering. Yet as the EER community expands, it is becoming increasingly difficult to develop and sustain community memory. This has the potential to significantly hinder progress as the inability of a field, discipline, or more generally – a problem space – to recognize what it knows increases the risk that isolated researchers and groups will tackle similar problems using relatively similar approaches. Lack of easy access to community knowledge also hinders the acculturation of newcomers into the community. The expansion of engineering education over the past decade has led the field to a critical juncture that demands new tools and methods to enable the community to expand and build on prior work. In this paper, we address this challenge by describing the development and deployment of an interactive knowledge platform – entitled Interactive Knowledge Networks for Engineering Education Research (iKNEER) . This system is designed with the goal to help members of this growing community explore the current state of knowledge withi","cbCaic7jkHMuvWhg","https://ap.wps.com/l/cbCaic7jkHMuvWhg","pdf",6723506,36,"English","# Abstract\n# Introduction\n## Engineering Education Research community memory challenges\n## Goal and design of iKNEER\n## Applying large-scale analytics to derive actionable knowledge","[{\"question\":\"What problem does the iKNEER system address in engineering education research?\",\"answer\":\"It addresses the difficulty of developing and sustaining community memory and accessing shared knowledge as the EER community expands.\"},{\"question\":\"What methods and components does iKNEER combine?\",\"answer\":\"It combines large-scale data mining, social network mapping algorithms, and time-series analysis to analyze knowledge about the engineering education community.\"},{\"question\":\"How is iKNEER demonstrated in the paper?\",\"answer\":\"The paper applies topic modeling to a subset of data to identify how research topics emerge and grow over time, showing the platform’s epistemic value.\"}]","ADVANCES IN ENGINEERING EDUCATION - Tools for large-scale data analytic examination of relational and epistemic networks in engineering education | PDF",1792065631,91]