Data Scientist Tyler Rinker Represents Campus Labs at International Unconference
May 10, 2017
Tyler Rinker, Data Scientist at Campus Labs, was among an exclusive group of 24 scientists, data scientists, and statisticians invited to attend last month’s Text Analysis Package Developers’ Workshop in London, England. Hosted by the London School of Economics, the workshop highlighted concepts embraced by rOpenSci. Established at the University of California, Berkeley, in 2011, rOpenSci focuses on greater access and collaboration to change how science and research work throughout the world. The goal is an environment in which a global community of researchers can easily share, replicate, and extend data analyses and visualizations to solve problems.
The workshop, which was part of the rOpenSci unconference, took place on April 21 and 22 and featured seven academic roundtables on topics ranging from corpus management to text statistics. The purpose was to bring together R text developers who are at the top of their field to create a unified framework for text analysis researchers. R is an open-source programming language and software environment for statistical computing and graphics. The unconference’s two-day workshop was also designed to build relationships and expand the participants’ professional network on a global scale.
Participants were already familiar with each other’s work through online interactions before the intensive workshop. The unconference workshop provided them with the opportunity to meet in person. “Online conversation has limitations,” explained Tyler. “The face-to-face meeting was an enriching experience, allowing for rapid exchanges of ideas across text analysis.” This more open and collegial environment reinforced rOpenSci’s mission for broader access and collaboration in research.
Leading the data science efforts at Campus Labs, Tyler works closely with Campus Success consultants as well as the product development team. His research focuses on text analysis, computational discourse analysis, multimodal analysis, data visualization, as well as engagement, motivation, and feedback. Text analysis was instrumental in the development of the Campus Labs Bloom’s Taxonomy algorithm, which tags learning outcomes with the verb that corresponds to a specific level of learning. This helps campuses confirm whether or not their institution-wide statements align with their intended measurements.
“I think data science can help higher education realize the potential of their data, especially text data,” he explained. “Many IR offices already are collecting and analyzing student data to better serve the student body. Data science can surface the types of insights and recommendations that might be possible if the data is collected carefully and consistently.”
We congratulate Tyler for being part of this invitation-only event. We’re also excited to continue leveraging his expertise to help institutions make more strategic decisions with their data.