ADVANCES IN ENGINEERING EDUCATION - Tools for large-scale data analytic examination of relational and epistemic networks in engineering education

Oct 15, 2026

Uploaded by Rizky

Digital information technologies have enabled new ways to capture, store, and analyze data relevant to engineering education. The paper presents the iKNEER information system—Interactive Knowledge Networks for Engineering Education Research—which integrates large-scale data mining, social network mapping, and time-series analysis. The system supports analyzing publications, conference proceedings, funding information, and derived knowledge products. Topic modeling illustrates how iKNEER reveals the emergence and growth of research topics within the engineering education research community.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages36

Reading Time1 h 31 min

AccessFree to download

Abstract

Introduction

Engineering Education Research community memory challenges

Goal and design of iKNEER

Applying large-scale analytics to derive actionable knowledge

| | Ad | vances in Engineering Edu | cation | | | | --- | --- | --- | --- | --- | --- | | | | | | | | | | | | | | | SUMMER 2014 Tools for large-scale data analytic examination of relational and epistemic networks in engineering education KRISHNA MADHAVAN Purdue University West Lafayette, Indiana ADITYA JOHRI George Mason University Fairfax, VA HANJUN XIAN Microsoft Corporation Bellevue, WA G. ALAN WANG Virginia Tech Blacksburg, VA AND XIAOMO LIU Thomson Reuters R&D New York, NY ABSTRACT 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](http://www.ikneer.org). SUMMER 2014 1 ADVANCES IN ENGINEERING EDUCATION Tools for large-scale data analytic examination of relational and epistemic networks in engineering education Key Words: Interactive knowledge platform, academic collaboration, research trends, big data INTRODUCTION In 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. Engineering 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

FAQs

What problem does the iKNEER system address in engineering education research?

It addresses the difficulty of developing and sustaining community memory and accessing shared knowledge as the EER community expands.

What methods and components does iKNEER combine?

It combines large-scale data mining, social network mapping algorithms, and time-series analysis to analyze knowledge about the engineering education community.

How is iKNEER demonstrated in the paper?

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.

Oct 15, 2026

Uploaded by Rizky