News & Events
The 10th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT 2013)
This paper is available in its entirety on IEEE Xplore
October 21, 2013
TCR’s Center for Advanced Research on Educational Assessment Technologies, in collaboration with the New York University College of Nursing, is presenting an invited paper on “Rethinking K-12 Educational Data Collection Infrastructures”. The authors are: Dr. Teresa Piliouras, Dr. Nadia Sultana, and Pui Lam (Raymond) Yu.
Abstract- Improvements in identity and privacy protection in educational data collection infrastructures are essential to the long-term “health” of our country’s education. These improvements are needed for the protection of youth, as required by law, and also to facilitate the collection of sensitive information needed for a deeper understanding of the dynamics of the educational setting and individual learning processes. The fragmentation of current systems should be supplanted by holistic, secure infrastructures capable of supporting rigorous scientific research on a national scale. We imagine a re-invention of educational infrastructures to allow analysis of critical dimensions of educational delivery systems, including: 1) Identification of causal factors which hinder or promote student academic success in Science, Technology, Engineering, and Mathematics (STEM) curriculums. Causal factors include individual characteristics, program interventions, family and economic circumstances, and features of broader social and cultural context. 2) Examination of students’ educational and career choices over time and with respect to different age and gender groups. Research on variation among males versus females, and underrepresented minorities and socioeconomically disadvantaged students is essential for the optimal design of educational strategies and curriculums. In this paper, we discuss the shortcoming of existing systems, and our recommendations for a more robust, secure, and research-oriented technology platform for data collection and analysis.