Speaker
Professor Haomin ZHANG
City University of Macau
Haomin Zhang is a Professor of Applied Linguistics at City University of Macau. His research focuses on psycholinguistics and biliteracy acquisition, with particular interests in metalinguistic awareness, bilingual and multilingual reading development, language assessment, and data-driven approaches to language and literacy learning. His recent work also explores the application of machine learning, corpus analysis, and large language models to educational assessment and language research.
His research has been published in linguistics, psychology, and education journals such as Studies in Second Language Acquisition, Contemporary Educational Psychology, Reading and Writing, and Reading Research Quarterly, among others. He has been awarded grants from the National Social Science Foundation of China, the Philosophy and Social Science Foundation of Shanghai Municipality, and the Shanghai Pujiang Talent Program.
Before joining City University of Macau, he served as Associate Dean of the School of Foreign Languages and Associate Dean of the Institute of Humanities and Social Sciences at East China Normal University. He currently serves on the editorial boards of the Journal of Educational Psychology, Reading Research Quarterly, Scientific Reports, and Language and Education.

Event Details
This talk presents an overview of our research on literacy and reading development across diverse learners. Our work is grounded in a cognitive–linguistic perspective, which examines how oral language, phonological processing, orthographic knowledge, and metalinguistic awareness support reading acquisition. Using studies of multilingual learners, I show how cross-linguistic resources contribute to variations in literacy outcomes. The presentation also discusses a methodological shift in our work from psychometric and experimental analyses to data-driven approaches. I will introduce recent studies that apply machine-learning models to categorize reading abilities using combined L1 and L2 indicators, and corpus-based analyses using large psycholinguistic databases to examine relationships among orthography, phonology, and semantics in word recognition. I conclude by discussing how predictive modeling and fine-grained learner profiling can support literacy research and assessment.

Registration Link/ Event Page
No registration is required.

Enquires
lin@cuhk.edu.hk