Research team
Expertise
Jennifer Thewissen specialises in learner corpus research and second language acquisition. She actively researches the concept of accuracy and its development across proficiency levels (https://uclouvain.be/fr/instituts-recherche/ilc/accuracy-across-proficiency-levels-a-learner-corpus-approach.html). Using error-annotated learner corpus data from different mother tongue groups at different proficiency levels (from B1 to C2), she has shown that learners' errors do not de facto necessarily decrease as proficiency increases but that different error types develop differently across the proficiency groups. This research has concrete implications for the language teaching and testing fields as empirical findings about accuracy profiles at different levels can be fed into the current Common European Framework descriptors of language competence which are labelled as overly vague. To gain further insight into learner language development, Jennifer Thewissen is currently investigating the development of syntactic and lexical complexity across proficiency levels to (1) determine whether these constructs help discriminate between learner writing at different levels and (2) analyse how they interact with the construct of accuracy. She is also investigating the impact of individual learner variables on L2 learner performance and how these variables can be measured and included in learner corpora. She has also fostered the development and cohesion of a team of researchers at the ¶¶Òõ¶ÌÊÓƵ who all work on aspects of learner language analysis, thus providing more visibility for this area of research at the university. Jennifer Thewissen has been actively working with the Louvain School of Management (UCLouvain) and has co-authored several papers on the linguistic aspects of entrepreneurial communication and how they impact subsequent ICO funding success.
Keeping the Human in the Text: Learner Voice and GenAI
Abstract
First, I will revise and resubmit the FWO project proposal entitled "Why mAI Voice Matters: Voice Expression in GenAI-Assisted and Unaided L2 English Writing", incorporating the reviewer feedback received during the previous evaluation round. This project aims to provide the first empirical account of how GenAI assistance shapes voice expression in L2 English writing. Drawing on a newly developed corpus of GenAI-assisted and unaided L2 texts across two registers, complemented by chatlogs, process data, and interviews, the project examines (1) linguistic manifestations of voice in GenAI-assisted and unaided writing, (2) lay raters' perceptions of voice strength in these texts, and (3) the relationship between writers' beliefs and attitudes towards GenAI-assisted writing and their actual voice expression in writing. Second, I will continue my ongoing collaborative research at the University of Antwerp. This includes the collection of additional learner data for the corpus described above, the finalisation and publication of a collaborative paper on functional adequacy in unaided and GenAI-generated texts, and the co-preparation of a methodological paper describing the development of the corpus.Researcher(s)
- Promoter: Thewissen Jennifer
- Fellow: Jadoulle Pauline
Research team(s)
Funding
- BOF
Project type(s)
- Research Project
ESL writing in a changing landscape: Examining the impact of GenAI assistance and task type on L2 writing quality.
Abstract
The main goal of this project is to investigate how writing environment (unaided vs. GenAI-assisted ) and task type (formal persuasive essay vs. informal diary entry) affect L2 English writing quality, operationalised as four core dimensions: functional adequacy (i.e. communicative task fulfilment) (Kuiken & Vedder, 2017), accuracy, syntactic complexity, and lexical diversity (CAFA for short). Theoretically, the study will contribute to a reconceptualisation of L2 writing quality which is no longer a purely cognitive-linguistic activity performed by individual learners, but rather the result of human-machine interactions. Empirically, the study will showcase how learner corpus tools and methods can be used to quantitively and qualitatively capture changes in L2 writing quality as a function of both writing environment and task type.Researcher(s)
- Promoter: Thewissen Jennifer
- Fellow: Guo Shiyi
Research team(s)
Funding
- BOF
Project type(s)
- Research Project
Learner voice in the age of GenAI
Abstract
This BOF post-doc challenge develops a research proposal on the extent to which the construct of authorial voice in L2 English writing is being reshaped by the growing use of Generative AI (GenAI) tools at university. Voice is the expression of a writer's personal views, authority and presence in a text. It is widely seen as central to the development of intellectual agency and authorial identity. This construct is considered fragile among L2 writers, who must build a textual presence while still mastering the linguistic conventions of academic texts. As GenAI tools become more widespread, fears are growing that they threaten L2 writers' already fragile voice. Whether this is in fact happening, and if so how, is a question that has not yet been answered.Researcher(s)
- Promoter: Thewissen Jennifer
Research team(s)
Funding
- BOF
Project type(s)
- Research Project