I’m excited to be collaborating with Shaun Kellogg, Hollylynne Lee, Shiyan Jiang, Rob Moore, and Mark Samberg. Specifically, I’m involved as a senior personnel member for the LASER (Learning Analytics in STEM Education Research) Institute. I’ll primarinly be involved in planning for and teaching during the weeklong summer institute.
I’m especially excited by the prospect of working with other STEM education researchers around the use of R. I’ll admit that learning analytics as a term and research discipline is new to me, but I hope I’ll have the ability to contribute in other ways (along the lines of the data science community with which I do identify). I’ll also be involved in researching the effects of the institute; both the research and teaching align with my interests in data science edu. I’m also excited by the chance to work with such a great team, including scholars in the learning analytics community, and by the chance to learn more about learning analytics. Finally, I’m excited about involving students here at UTK in planning and carrying out the work involved with this project—and thinking about how this work compliments a data science-focused course I’m planning to teach Spring 2020.
Here’s the abstract for the project:
North Carolina State University (NCSU) will establish the Learning Analytics in STEM Education Research (LASER) Institute to build learning analytics capacity in STEM education research. The institute will provide professional development for early and mid-career STEM education researchers in learning analytics, a computational research methodology. The goal of the institute is to increase the capacity of researchers to understand and improve STEM learning and learning environments through the use of new sources of data and analytical approaches. The institute will be anchored by a week-long intensive training program consisting of hands-on learning labs, presentations from experts in the field, support for research planning and data analyses, and opportunities for networking and collaboration. In addition, follow up support will occur throughout the year.
The institute will prepare STEM education research scholars to: 1) understand the methodologies, applications, and ethical issues of learning analytics as these relate to understanding and improving STEM education, 2) gain proficiency in R, a free software environment applied to computational analysis techniques (e.g., network analysis, text mining, machine learning, and data dashboards) using real-world STEM education data, and 3) expand professional networks in support of STEM education research efforts.
I’ll definitely share more information as we begin to plan for the first summer institute, set to take place summer 2021. There will be three institutes in total (each preceeded and followed by participating in an online community of practice).
A bit more is here.