Jarvis Looi
Researcher in applied linguistics, corpus linguistics, statistics, instructed second language learning
Teaching and Research Fellow (ATER), Analyse et Traitement Informatique de la Langue Française (ATILF), CNRS-Université de Lorraine, France
Jarvis Looi is a researcher specialising in applied linguistics, corpus linguistics, quantitative linguistics, and instructed second language learning. He holds a PhD in language learning and assessment, earned through an international joint programme between Universiti Malaya (Malaysia) and Analyse et Traitement Informatique de la Langue Française (ATILF), CNRS–Université de Lorraine (France).
Research interests
His research lies at the intersection of applied linguistics, corpus linguistics, and language learning, with a particular focus on how empirical linguistic evidence can inform the teaching and learning of French as a foreign language (FLE). His work combines corpus-based analysis, advanced statistical modelling, and classroom-based research to better understand both linguistic phenomena and learning processes.
One strand of his research investigates linguistic variation and usage patterns in French through corpus-based methods. His work examines phenomena such as adjective positioning, lexical semantics, and distributional preferences in authentic language use. These studies employ quantitative methods including logistic regression, Bayesian modelling, and multiple correspondence analysis to uncover the multifactorial patterns underlying linguistic variation.
A second strand of his research focuses on data-driven learning (DDL) and inductive language learning. He investigates how learners interact with authentic linguistic data and how knowledge is co-constructed during corpus-based learning activities. Recent work explores learner behaviour in inductive learning environments, including the identification of learning sequences and learning events that characterise how learners observe patterns, formulate hypotheses, and collaboratively construct linguistic knowledge.
Building on this line of research, he is particularly interested in the role of collaboration and scaffolding in corpus-based learning environments, including how different interactional configurations (individual, pair, and group learning) influence learning processes. His current projects also explore the potential role of artificial intelligence as a learning companion, examining how AI-mediated scaffolding may support collaborative inductive learning.
More broadly, his research seeks to bridge linguistic research and pedagogical practice. A growing part of his work therefore examines how corpus tools, digital resources, and AI technologies can be integrated into teacher training and digital education, with the aim of equipping future language teachers with corpus literacy and data-informed pedagogical practices.
Teaching
Alongside his research, Jarvis has extensive experience in university teaching across Europe and Asia. He has taught French language and linguistics at Universiti Putra Malaysia, and English for specific purposes at Université de Lorraine and Sciences Po Nancy.
He currently serves as a Teaching and Research Fellow, or in French, Attaché temporaire d’enseignement et de recherche (ATER), in the Department of Language Sciences at the University of Lorraine, where he teaches both undergraduate and postgraduate courses. His teaching covers areas such as language teaching and learning, psycholinguistic processes in second language acquisition and comprehension, corpus linguistics methods and digital humanities, and grammar and metalanguage. He also contributes to the methodological training of Master’s students in French as a Foreign Language (FLE), including academic writing, research preparation, and supervision of Master’s dissertations.
His pedagogical approach emphasises active learning and inquiry-based teaching. Drawing on his research in corpus linguistics and data-driven learning, he regularly integrates corpus tools and authentic linguistic data into his courses to foster students’ analytical skills, metalinguistic awareness, and autonomy as learners and future language teachers.