Community Analysis

The project will build a data infrastructure that would store, visualize and analyze information about currently funded NSF-IUSE projects. The infrastructure would (1) collect data from PI-Co-PI professional websites and published research, (2) build geospatial visualizations identifying the types and reach across the diverse types of organizations within United States of America and (3) construct an interactive a community network to identify individuals who could serve as experts for the community and belong to institutions not currently represented in IUSE community. The goal of the infrastructure is to motivate resources (e.g. events, meetings and workshops) to expand the community to include types of organizations, groups and individuals not previously included in the community.

Using information about federally funded educational technology projects, this project maps the technological evolutions for this research community in the past five years. Through the use of artificial intelligence techniques (LLMs) the project works towards two goals, (1) to build the data infrastructure to facilitate study of this emergent and dynamic edTech research community and (2) use the data infrastructure to track the the interdisciplinary evolutions in the research interests of this community. The data infrastructure would include extensively building a open-source tool that curates the data about investigators' institutional, departmental, and research affiliations and their research expertise and associations via their funded projects and publications. We imagine the tool and the conceptual analysis of educational technology evolution to be useful for funding agencies to invite reviewers, for conferences and journals to plan their special issues and select members to serve on planning committees, for administrators and researchers within research units to help broker interdisciplinary collaborations, and by students and postdoctoral researchers for identifying mentors and future job prospects. 

Using computational methods like Natural Language Processing (NLP) techniques, Social Network Analysis (SNA), and data visualization, this project 1) characterized projects into relevant topics, 2) mapped the research community through visualizations, and 3) further mined the trends of research from the collaboration graphs created based on the funding information from National Science Foundation and researcher expertise information to empirically define interdisciplinarity in the field. The graphs were analyzed with an intention of understanding the nature and novelty of interdisciplinary collaborative scientific research for the emergent educational technology research community. The metrics innovated by this work could be used to distill interdisciplinary research (IDR) for any other community early and can motivate policy decisions to navigate the gaps and overlaps in the field. The project effort was applauded by NSF evaluation team and considered as a pioneering effort for creating usable brokering tool that can motivate researchers to indulge in interdisciplinary collaborations for future funding opportunities.