Co-Principal Investigators: Aleven, Vincent; Bett, Michael; Bier, Norman L.; Burckhardt, Philipp; Carvalho, Paulo; Greenhouse, Joel B.; Herckis, Lauren; Koedinger, Kenneth; Lovett, Marsha C.; Nugent, Rebecca; Rosé, Carolyn P.; Sakr, Majd

Because of increased capabilities for collecting and analyzing education data, the field has new opportunities for research-based, data-driven improvements in education activities. However, education practitioners have not been fully able to leverage these developments and data to inform large scale improvements in the field. In this program, trainers will prepare education practitioners to use established and emerging data science methods with an emphasis on statistical thinking and computational approaches for educational research. During the training, participants will engage with an open repository of detailed learner interaction data to support their understanding the affordances and challenges of working with big learning data. The training is intended for education practitioners who have foundational knowledge in data and statistics and want to learn how to use data collected from educational settings to improve learning outcomes while contributing to education research through the application of data science methods.

The training team will recruit 3 cohorts of 25 participants (75 participants total) in a year-long training program. The training includes 12 weeks of asynchronous online instruction to build foundational data science skills; a week-long in-person workshop providing hands-on experience in designing, running, and analyzing big data; and sustained mentoring and collaboration. The training team will make training materials available to both participants and non-participants looking to extend their data analysis skills through Carnegie Mellon University's Open Learning Initiative platform.