About

Data science has emerged as an important part of educational research and practice in recent years, producing a rapidly growing demand for a workforce that is literate in data science methods and cyberinfrastructure, as well as competence in the specific nature of educational data, research, and practice. However, there is not a sufficient number of graduate programs or other sustained training activities to meet this need. As a result, much of the learning analytics workforce lacks key competencies.

To address this gap, a consortium consisting of the University of South Australia (Florence Gabriel, Shane Dawson), the University of Pennsylvania (Ryan Baker), and the University of Texas Arlington (Justin T. Dellinger, George Siemens) have worked to create the Learning Analytics Learning Network (LALN). Monthly meetups will be held worldwide and local research community leaders in 25 cities have agreed to participate, from New York City and Silicon Valley to Kyoto, Manila, and Frankfurt. We anticipate yearly attendance of over 500 researchers and practitioners at our events.

Cities will take turns hosting a distinguished speaker, streaming the event online so other cities can join (events will be recorded for asynchronous participation). Moderated discussions will be held locally and online in Canvas. There will also be a learning activity and time for project group formation and networking. Activities and exercises will be focused on beginner, intermediate, and expert categories. They will range from introducing participants to learning analytics to helping them learn to use modern and emerging cyberinfrastructure for data science, including activities such as Python and R in cloud computing, deploying common learning analytics algorithms such as Bayesian Knowledge Tracing efficiently at scale, or analyzing unstructured text data.

Our activities will serve both as an introduction to methods for new members of the field (such as graduate students and teachers) and as continuing education for existing members of research workforce, responsive to changes in the tools, algorithms, and the technologies needed for data science. Schedules will be determined centrally, but local coordinators will take turns creating activities and exercises for a broader audience.