Technology Category
Machine Learning: Multimodal LearningNatural Language Processing: Language Grounding & Multi-modal NLPIntelligent Robots: Multimodal Perception & Sensor Fusion
Rapport du Projet de Recherche TRAIMA
🤖 AI Summary
This study addresses the high cost and limited scalability of manual analysis of multimodal interactions—encompassing verbal, paralinguistic, and nonverbal behaviors—in educational settings. Drawing on discourse analysis and interactional linguistics, it proposes a tripartite structural definition of explanatory discourse and develops a multimodal analytical framework designed for automation. The research systematically evaluates the applicability of various transcription conventions in both native and foreign language teaching contexts in French. Leveraging the TechnéLAB platform, it integrates multi-camera video, synchronized audio, eye-tracking data, and digital interaction logs to build an extensible annotation scheme grounded in the INTER-EXPLIC and EXPLIC-LEXIC corpora. This work establishes a methodological foundation and interdisciplinary pathway for AI-driven research on educational interaction.
Technology Category
Application Category
📝 Abstract
The TRAIMA project (TRaitement Automatique des Interactions Multimodales en Apprentissage), conducted between March 2019 and June 2020, investigates the potential of automatic processing of multimodal interactions in educational settings. The project addresses a central methodological challenge in educational and interactional research: the analysis of verbal, paraverbal, and non-verbal data is currently carried out manually, making it extremely time-consuming and difficult to scale. TRAIMA explores how machine learning approaches could contribute to the categorisation and classification of such interactions. The project focuses specifically on explanatory and collaborative sequences occurring in classroom interactions, particularly in French as a Foreign Language (FLE) and French as a First Language (FLM) contexts. These sequences are analysed as inherently multimodal phenomena, combining spoken language with prosody, gestures, posture, gaze, and spatial positioning. A key theoretical contribution of the project is the precise linguistic and interactional definition of explanatory discourse as a tripartite sequence (opening, explanatory core, closure), drawing on discourse analysis and interactional linguistics. A substantial part of the research is devoted to the methodological foundations of transcription, which constitute a critical bottleneck for any form of automation. The report provides a detailed state of the art of existing transcription conventions (ICOR, Mondada, GARS, VALIBEL, Ferr{\'e}), highlighting their respective strengths and limitations when applied to multimodal classroom data. Through comparative analyses of manually transcribed sequences, the project demonstrates the inevitable variability and interpretative dimension of transcription practices, depending on theoretical positioning and analytical goals. Empirical work is based on several corpora, notably the INTER-EXPLIC corpus (approximately 30 hours of classroom interaction) and the EXPLIC-LEXIC corpus, which serve both as testing grounds for manual annotation and as reference datasets for future automation. Particular attention is paid to teacher gestures (kin{\'e}sic and proxemic resources), prosodic features, and their functional role in meaning construction and learner comprehension. The project also highlights the strategic role of the Techn{\'e}LAB platform, which provides advanced multimodal data capture (multi-camera video, synchronized audio, eye-tracking, digital interaction traces) and constitutes both a research infrastructure and a test environment for the development of automated tools. In conclusion, TRAIMA does not aim to deliver a fully operational automated system, but rather to establish a rigorous methodological framework for the automatic processing of multimodal pedagogical interactions. The project identifies transcription conventions, annotation categories, and analytical units that are compatible with machine learning approaches, while emphasizing the need for theoretical explicitness and researcher reflexivity. TRAIMA thus lays the groundwork for future interdisciplinary research at the intersection of didactics, discourse analysis, multimodality, and artificial intelligence in education.
Problem
Research questions and friction points this paper is trying to address.
multimodal interactions
educational settings
manual transcription
classroom discourse
interactional analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
multimodal interaction
automatic transcription
machine learning in education
explanatory discourse
annotation framework
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