Computational methods borrowed from evolutionary biology are becoming an increasingly important tool for scholars of historical linguistics. However, these methods require computational skills that do not normally form a part of the humanities curriculum. Prof. David Goldstein is collaborating with HumTech to design and create a lab component for his Spring 2018 Linguistics 110 course, Intro to Historical Linguistics. The lab will teach the students the basics of the R programming language and quantitative reasoning.
The ultimate goal of the lab is for the students to be able to use linguistic data to create and interpret phylogenetic trees showing language descent hypotheses, an important part of current historical linguistics scholarship. In order to achieve this, David and his team (including Canaan Breiss, a graduate student in Linguistics) are building a collection of problem sets for each week of the course that will leveraging the principles of scaffolding, active learning, and formative evaluation to help students practice historical linguistics analytical methods. Each week, the students will work through the problem sets one by one, first watching the lab instructor, then doing the problems themselves in small groups, before finally doing a final problem set completely on their own as homework. Also each week, a few new R functions will be introduced. In this way, the instructors should be able to see in fine detail what aspects of the assignments (both linguistic and computational) prove most challenging for the students and tailor their lecture and future assignments in real time to focus more on the areas where students need the most help.
This project is funded by an Instructional Improvement Program grant from UCLA’s Office of Instructional Development and by HumTech. HumTech is contributing expertise in project management, grant writing, the R programming language, and instructional design to the project team.