REU Site: Undergraduate Research in Educational Data Mining

点击翻译按钮获取中文摘要

The Research Experiences for Undergraduates (REU) Site at George Mason University focuses on data science and engineering research applied to solving grand challenges within the educational domain. Each summer for ten weeks, ten undergraduate students will participate in training and research activities, gaining skills pertaining to the typical lifecycle of a data analytics project. This program will focus on "learning by doing" which will illustrate to students the creativity and technical skills involved in solving complex real world problems. The project is led by a team of experienced faculty who will mentor the participating researchers through a number of professional development activities that prepare them for future careers as professionals in the field of data science, which has been identified as priority areas for the workforce of the future. This program will engage and recruit underrepresented minorities and women to pursue multi-disciplinary careers that transcend computer science, engineering and educational sciences. Careful student-student and student-mentor(s) pairings, as well as opportunities for developing organically-formed partnerships, will foster an inclusive academic culture. Given the trans-disciplinary focus of this program, there will a direct impact in increasing the awareness of data analytics and visualization techniques, and their potential impacts in a broad range of domains. The educational domain is useful in that it is a common denominator: most students have direct experience with education processes through primary, secondary, and postsecondary schools. The primary goal of this interdisciplinary REU Site program is to expose undergraduate students to advanced topics in analytics, data mining, and visualization techniques as applied to data from the education domain. Students will participate in projects with the central theme of improving instructional technology design, enhancing academic curricula and modeling learning experiences. From an analytical methodology standpoint, projects will investigate approaches to model the dynamic knowledge state of a student enrolled in a sequence of courses within a sequential academic program. Projects will model the learning behavior of younger students in informal settings by analyzing the server logs of the Scratch framework or Online Engineering Communities. Projects will also model the learning habits of practitioners while interacting with online programming environments. This focus on education-centered analysis will provide the participating undergraduate students a deeper understanding of both general data mining techniques and a broader taste for educational design. The program will have an immediate impact on a diverse group of students by introducing them to the nuances of interdisciplinary research process, sparking an interest in computing and learning sciences, and more importantly fostering lifelong professional relationships with mentors and peers. More information about this project and additional resources will be available at http://www.cs.gmu.edu/reuThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.