This biannual learning unit is not being organized in 2023-2024 !
Teacher(s)
Language
English
Prerequisites
It is strongly recommended that you have completed LGERM2829 English Linguistics: Corpus Linguistics or LCLIG2250 Méthodologie de l'analyse de corpus en linguistique (or any equivalent introductory course in corpus linguistics).
Main themes
Learner corpora are electronic collections of language data produced by second or foreign language learners. They are used to investigate topics in second language acquisition (SLA) research and linguistic theory in general, inform foreign language teaching, develop learner-corpus-informed tools (e.g. courseware, proficiency tests, dictionaries, and automatic spell- and grammar-checkers), or conduct natural language processing (NLP) tasks.
The aim of this research seminar is to introduce the field of learner corpus research via the completion of an empirical research project. We will focus on one particular topic (e.g. learners’ use of phraseology, linguistic complexity, crosslinguistic influence) and go through each of the necessary steps to conduct a research project (literature review, data selection, quantitative and qualitative analyses of the data, discussion and conclusion).
The course will provide an opportunity for students to develop their knowledge and skills for carrying out a learner corpus research project. Depending on the selected topic, special attention will be paid to aspects of learner corpus design (e.g. collection, transcription), data annotation (e.g. coding schemes, manual and automatic annotation, annotation reliability), and/or the different types of methodologies used to analyse learner corpora (comparisons with reference corpora, error analysis, quantitative approaches, etc.).
The aim of this research seminar is to introduce the field of learner corpus research via the completion of an empirical research project. We will focus on one particular topic (e.g. learners’ use of phraseology, linguistic complexity, crosslinguistic influence) and go through each of the necessary steps to conduct a research project (literature review, data selection, quantitative and qualitative analyses of the data, discussion and conclusion).
The course will provide an opportunity for students to develop their knowledge and skills for carrying out a learner corpus research project. Depending on the selected topic, special attention will be paid to aspects of learner corpus design (e.g. collection, transcription), data annotation (e.g. coding schemes, manual and automatic annotation, annotation reliability), and/or the different types of methodologies used to analyse learner corpora (comparisons with reference corpora, error analysis, quantitative approaches, etc.).
Learning outcomes
At the end of this learning unit, the student is able to : | |
| 1 | demonstrate knowledge and understanding of research into English as a Foreign Language based on learner corpora; |
| 2 | conduct a learner corpus research project (both as a group project in class and individually); |
| 4 | identify research gaps, formulate research questions and hypotheses on the basis of the current state of the art; |
| 5 | annotate and analyse a learner corpus with the help of corpus linguistic tools and techniques; |
| 6 | situate the results against the current state of the art; |
| 7 | work in team (collaborate, share the work, discuss, prepare guidelines, synthesize, report orally on each step of the research process, use tools for collaborative work and citation/reference management); |
| 8 | write a research paper that meets the requirements in terms of content and structure. |
| 3 | identify and critically examine the relevant scientific literature; |
Content
In 2022-23, the seminar will focus more particularly on the use of learner corpora in the fields of second language acquisition and language assessment.
Keywords: linguistic complexity, phraseology, proficiency, CEFR
Keywords: linguistic complexity, phraseology, proficiency, CEFR
Teaching methods
Various: lectures, flipped classroom, discussions, students' presentations, group projects, hands-on sessions
Evaluation methods
5-credit course
The research seminar is evaluated on the basis of the following activities:
- Continuous assessment (exercices, group project, etc.) (30%)
- A term paper on a topic related to the course content (70%). Students whose mark for the term paper is below 10/20 automatically fail for the course.
Students who have more than two unjustified absences will not be allowed to register for the exam.
10-credit course
The research seminar is evaluated on the basis of the following activities:
1. Group project on a topic related to the course content. This group project includes continuous assessment activities (exercices, student presentations, etc.) (50%)
2. Individual term paper on a topic related to the course content (50 %). Students whose mark for the term paper is below 10/20 automatically fail for the course.
Students who have more than two unjustified absences or who have not taken part in the group project will not be allowed to register for the exam. In case of resit, the course evaluation will be based on a term paper only.
Generative artificial intelligence (AI) must be used responsibly and in accordance with the practices of academic and scientific integrity.
The research seminar is evaluated on the basis of the following activities:
- Continuous assessment (exercices, group project, etc.) (30%)
- A term paper on a topic related to the course content (70%). Students whose mark for the term paper is below 10/20 automatically fail for the course.
Students who have more than two unjustified absences will not be allowed to register for the exam.
10-credit course
The research seminar is evaluated on the basis of the following activities:
1. Group project on a topic related to the course content. This group project includes continuous assessment activities (exercices, student presentations, etc.) (50%)
2. Individual term paper on a topic related to the course content (50 %). Students whose mark for the term paper is below 10/20 automatically fail for the course.
Students who have more than two unjustified absences or who have not taken part in the group project will not be allowed to register for the exam. In case of resit, the course evaluation will be based on a term paper only.
Generative artificial intelligence (AI) must be used responsibly and in accordance with the practices of academic and scientific integrity.
Online resources
Bibliography
- Granger, S., Meunier, F. & G. Gilquin (2015) The Cambridge Handbook of Learner Corpus Research. Cambridge: Cambridge University Press.
- Tracy-Ventura, N. & M. Paquot (2021) The Routledge Handbook of Second Language Acquisition and Corpora. Routledge.
Teaching materials
- Slides, exercices, scientific articles available on Moodle
Faculty or entity
