Research-based Undergraduate Linguistics Experience (RULE)

The Research-based Undergraduate Linguistics Experience (RULE) is a course (LING 202) that pairs undergraduate students with Ph.D. students carrying out linguistics research. This pairing provides graduate students with research advising and mentoring experience and gives undergrads the opportunity to participate in original linguistics research as research assistants. Applications are solicited a few weeks before the start of each semester. Research mentor/mentee teams for Fall 2021 are below:

RULE Projects:

Helen Dominic with Caitlin McDermott on “A sociolinguistic analysis of triadic healthcare interactions”

This project focuses on triadic interactions between physicians, patients with Limited English
Proficiency (LEP), and their family companions. Family companions are often seen as a risky
group of people whose role as interpreters in medical contexts is controversial due to their
possible biasedness. However, LEP patients oftentimes bring a family companion with them to
medical consultations even when a professional translator is present. This project will study audio
responses to a web questionnaire to understand the social and linguistic role of companions as
nonprofessional interpreters and how they might be perceived by physicians.

Ayşenur Sağdıç with Grant Brown and Charlie Dees on “Learning by stimulating: Second language pragmatic development in a task-based digital simulation game”

While language learners are increasingly required to engage in multilingual and multicultural
interactions, the affordances of technology-assisted language learning only continue to grow.
These realities demand theoretically-sound, data-driven digital instructional responses.
Competent use of a second language (L2) involves not only attending to the explicit rules of the
language, but also to its pragmatics, the implicit rules of what to say, how to say it, when, and to
whom. Task-based language teaching (TBLT), a learner-centered pedagogy using authentic tasks
as the unit of instruction, has been shown to promote second language acquisition (SLA),
including pragmatics. Meanwhile, gamified digital simulations have introduced new affordances
for language learning by delivering lifelike tasks with individualized feedback and immersing
learners in experiences that might otherwise be inaccessible.
To reveal the potential impact of gamified task-based digital simulations for language education,
this dissertation study examined the extent to which task-based digital simulation with more and
less explicit feedback contributes to L2 learners’ pragmatic development. One hundred thirteen
Turkish EFL (English as a foreign language) learners were randomly assigned to one of three
experimental groups: (a) implicit feedback in the form of clarification requests, (b) explicit
feedback with metapragmatic explanation, or (c) control group with no feedback. Learners’
pragmatic learning gains were measured before, immediately after, and one week after simulation
practice. These experimental data were triangulated with simulation activity, surveys, stimulated
recalls, and interviews to gain insights into learners’ individualized experiences of the practice
and feedback. Findings expand the currently limited research on technology-enhanced task-based
language education and will provide theoretical, pedagogical, and methodological implications for language learners, instructors, curriculum designers, and digital learning application
developers worldwide.

Shira Wein with Calvin Engstrom, Alex Nelson, and Ethan Ricker on “Spanish abstract meaning representation”

The Abstract Meaning Representation (AMR) framework is a graph-based representation of
language, designed to strictly reflect the meaning/semantics of the sentence(s). While AMR was
developed for English, the framework has since been extended to a number of languages through
annotation schema. Nonetheless, the data available in other languages is limited. This work will
constitute the first large-scale annotation project for Spanish AMR, which will enable testing of
parsing and generation work in Spanish, and be ripe for use in downstream Spanish applications
such as machine translation.