I am a Research Associate (Academic) in the Centre for Advanced Research in Experimental and Applied Linguistics (ARiEAL) at McMaster University. A psycholinguist by training, I am interested in the language and reading development of English language learners in higher education. You can find my latest published work below.
PhD in Cognitive Science of Language, 2016
McMaster University, Canada
MSc in Developmental Linguistics, 2011
University of Edinburgh, UK
BA in English Language and Linguistics, 2010
York St. John University, UK
My research delves into how linguistic, cognitive, and experiential factors influence reading behavior. I use a variety of experimental methods (e.g., eye-tracking, corpus linguistics) and statistical tools to study how reading skill is shaped by individual cognitive and linguistic abilities. Within this larger framework, my research is dedicated to moving beyond studying the linguistic behaviour of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations. Examples of this research include eye-movement studies of reading in non-college bound adults and corpus studies of linguistic phenomena across varieties of English. My current research uses eye-tracking to understand English reading development of ESL students enrolled in an academic bridging program.
These projects are currently under peer review or have recently been published.
Does growth during an English bridging program benefit future academic flourishing? In this study we found that change in reading speed of passages of text, measured using the eye-tracking methodology, was a significant predictor of grade point average up to 3 years after the completion of the bridging program. Larger gains in reading speed are linked to higher GPAs.
Many words in English are morphologically complex (assign-ment, teach-er) and so being able to proficiently apply the correct endings (suffixes) to complex words is an important skill to acquire. In this project, we analyzed the written production of English language learners and asked What are the statistical properties that make morphologically complex words easier for EFL students to learn? This project was published in Applied Psycholinguistics.
This technical report assessed the change in English language skills of 340 EFL students enrolled in a university bridging program. This report used of the Reliable Change Index Statistic in order to test if the change in scores is greater than might would be expected from random variation alone. The report is available on ResearchGate.
Does your incoming reading skill give you a developmental advantage in an academic English bridging program? This longitudinal eye-movement study of over 400 EFL students showed that if you enter a bridging program with stronger English reading proficiency, you may have a head start in your reading fluency and reading comprehension. However, students of all incoming reading ability levels develop reading fluency and comprehension at the same rate. This project was published in Bilingualism: Language & Cognition.
I have taught the following courses at McMaster University:
(2023). In this course students collaborate to plan, carrying out, analyse and report an experiment that addresses a cognitive aspect of language processing.
(2017, 2019, 2020, 2021, 2022). An introductory course to statistical methods custom-tailored to the needs of language researchers. This course provides an introduction to R, a free software environment for statistical computing and graphics.
(2018, 2020, 2021). This course studies computational tools and techniques of language processing using large electronic collections of texts. Students are trained in basic text-processing, statistical and programming skills using R.
(2017). This course offers senior undergraduate students the opportunity to participate in a learning-centred leadership program, involving peer-to-peer mentoring of students in the MELD program. The course provides up-front and on-going training and development in active leadership and mentorship.
R, Shiny, ggplot, Markdown
LMER, longitudinal analysis, growth curve modeling
Programming: EyeLink, Psychopy, Java, DMDX; Platforms: Pavlovia, Inquisit, GitLab, Amazon Turk, DMDX
Line thickness corresponds to the number of co-authorships.