BD Spokes: Spoke: NORTHEAST: Collaborative: Grand Challenges for Data-Driven Education

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This project supports teachers, administrators and researchers to collaborate around online education resources and big data. It will increase the capacity of participants in Educational Big Data in the Northeast to analyze data from schools, students and administrators and to improve teaching and learning. However, as more refined data comes from online instructional systems and the use of data mining techniques, participants will learn to search for patterns and associations and to draw conclusions about student knowledge, performance and behavior. This research addresses several grand challenges in education: 1) Predict future student events, e.g., college attendance, college major, from existing large-scale longitudinal educational data sets involving the same thousands of students. 2) Help teachers to make sense of dense online data to influence their teaching, e.g., what should they say or do in response to student activity. 3) Provide personal instruction to each student based on using big data that represents student skills and behavior and infers students' cognitive, motivational, and metacognitive factors in learning. The project will improve the capacity in data-driven education by sharing educational databases, managing yearly data competitions, and conducting educational data science workshops and hackathons. Measurable results include studying gigabytes of data to: create actionable recommendations for classroom teachers; make effective and successful predictions about students; develop new AI methods for education; and create new data science tool sets. Key outcomes include introducing many researchers to educational big data, learning analytics and models of teaching interventions. The team intends to improve classroom learning and leverage the unique types of data available from digital education to better understand students, groups and the settings in which they learn.Computers have been in classrooms for decades and yet educators have not identified the most effective ways of using them. Despite advances in evaluation methods to measure human learning, most researchers still use measures available 50 years ago. This project will leverage and extend state-of-the-art big data bases and technologies to measure online learning, especially features of student engagement and learning associated with improved student outcome. This project has the potential to reach millions of students (while learning), hundreds of researchers while measuring human learning (from education, cognitive science, learning sciences, psychology, and computer science) and a dozen other organizations (publishers, testing organizations, non-profit organizations, teachers, parents, and stakeholders). The team brings together a unique blend of researchers from data science (Baker, Heffernan); adaptive education technology and computer science (Woolf, Arroyo); and learning sciences (Arroyo, Heffernan). It includes women and minorities (Woolf, Arroyo), people who helped develop the largest educational database in the world (Baker), developers of data science teaching materials (Arroyo, Baker), and others who have developed online tutoring systems that achieve significant student success in learning (e.g., Heffernan, Arroyo, Woolf).