Hi! Welcome to my personal site. I am a postdoctoral fellow in the Centre for Advanced Research in Experimental and Applied Linguistics (ARiEAL) at McMaster University in Ontario, Canada.
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
The central thread of my research program is the study of both between and within individual differences during language tasks in order to understand the development of the cognitive-linguistic system. In particular, I use eye-tracking and other behavioural paradigms to measure the influence of linguistic, cognitive, and experiential factors on the dynamics of word identification, passage reading fluency and text comprehension. I run experimental psycholinguistic studies, including longitudinal eye-movement studies, and conduct observational studies of verbal behaviour using large-scale electronic language corpora. I use growth curve analysis (e.g., using linear mixed-effects models), distributional analyses (e.g., survival analysis, quantile regression), and machine learning techniques (e.g., random forests) to determine 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 non-college bound adults and corpus studies of linguistic phenomena across varieties of English, such as those found in Tanzania, Pakistan and Ghana. My current research uses eye-tracking to understand the English reading skill development of ESL students enrolled in an academic bridging program.
These projects are currently under peer review or have recently been published.
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 kinds of morphologically complex words are the most difficult for EFL students to learn?
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.
I have taught the following university-level courses at McMaster University:
(2017, 2019, 2020, 2021). 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).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.
I supervised the undergraduate research thesis of Shaina Benjamin.
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.