Rapport du Projet de Recherche TRAIMA

• URL: http://arxiv.org/abs/2601.12844v1
• Date: Mon, 19 Jan 2026 08:55:50 GMT
• Title: Rapport du Projet de Recherche TRAIMA
• Authors: Julie Rançon, Jean-François Cerisier, Emilie Remond, Aurélien Nguyen, Andrew Peterson, Ladjel Bellatreche,
• Abstract summary: The project addresses a central methodological challenge in educational and interactional research.<n>The analysis of verbal, paraverbal, and non-verbal data is currently carried out manually, making it extremely time-consuming and difficult to scale.<n>The project focuses specifically on explanatory and collaborative sequences occurring in classroom interactions.
• Score: 0.7440170908149745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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{é}), 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{é}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{é}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.

Related papers

• MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Games [70.37904949359938]
  We evaluate language models in multi-turn interactions using a suite of collaborative games that require effective communication about private information.<n>We find that language models are unable to use interactive collaboration to improve over the non-interactive baseline scenario.<n>We analyze the linguistic features of these dialogues, assessing the roles of sycophancy, information density, and discourse coherence.
  arXiv  Detail & Related papers  (2026-02-27T17:13:20Z)
• Role-Playing Agents Driven by Large Language Models: Current Status, Challenges, and Future Trends [6.249024503883953]
  This paper systematically reviews the current development and key technologies of role-playing language agents (RPLAs)<n>It summarizes the critical technical pathways supporting high-quality role-playing, including psychological scale-driven character modeling, memory-augmented prompting mechanisms, and motivation-based behavioral decision control.<n>The paper outlines future development directions of role-playing agents, including personality evolution modeling, multi-agent collaborative narrative, multimodal immersive interaction, and integration with cognitive neuroscience.
  arXiv  Detail & Related papers  (2026-01-15T07:08:20Z)
• AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant [6.874351093155318]
  VITA (Virtual Teaching Assistants) is an adaptive distributed learning platform that embeds a large language model (LLM)-powered bot (BotCaptain)<n>The paper describes an end-to-end data pipeline that transforms chat logs into Experience API (xAPI) statements, instructor dashboards that surface outliers for just-in-time intervention.<n>Future work will refine the platform's adaptive intelligence and examine applicability across varied educational settings.
  arXiv  Detail & Related papers  (2025-09-17T11:27:45Z)
• Contextualized Representation Learning for Effective Human-Object Interaction Detection [17.242400169885453]
  Human-Object Interaction (HOI) detection aims to simultaneously localize human-object pairs and recognize their interactions.<n>We introduce a Contextualized Representation Learning that integrates both affordance-guided reasoning and contextual prompts.<n>Our proposed method demonstrates superior performance on both the HICO-Det and V-COCO datasets in most scenarios.
  arXiv  Detail & Related papers  (2025-09-16T08:03:16Z)
• How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective [64.00022624183781]
  Large language models (LLMs) can assess relevance and support information retrieval (IR) tasks.<n>We investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability.
  arXiv  Detail & Related papers  (2025-04-10T16:14:55Z)
• Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision [49.073964142139495]
  We systematically review the applications and advancements of multimodal fusion methods and vision-language models.<n>For semantic scene understanding tasks, we categorize fusion approaches into encoder-decoder frameworks, attention-based architectures, and graph neural networks.<n>We identify key challenges in current research, including cross-modal alignment, efficient fusion, real-time deployment, and domain adaptation.
  arXiv  Detail & Related papers  (2025-04-03T10:53:07Z)
• The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models [40.128112851978116]
  We study how different prompting methods affect the geometry of representations in language models.<n>Our analysis highlights the critical role of input distribution samples and label semantics in few-shot in-context learning.<n>Our work contributes to the theoretical understanding of large language models and lays the groundwork for developing more effective, representation-aware prompting strategies.
  arXiv  Detail & Related papers  (2025-02-11T23:09:50Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Structured Like a Language Model: Analysing AI as an Automated Subject [0.0]
  We argue the intentional fictional projection of subjectivity onto large language models can yield an alternate frame through which AI behaviour can be analysed. We trace a brief history of language models, culminating in the releases of systems that realise state-of-the-art natural language processing performance. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.
  arXiv  Detail & Related papers  (2022-12-08T21:58:43Z)
• Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions [0.0]
  We propose a novel interaction framework called Semantic Interactive Learning for the text domain. We frame the problem of incorporating constructive and contextual feedback into the learner as a task to find an architecture that enables more semantic alignment between humans and machines. We introduce a technique called SemanticPush that is effective for translating conceptual corrections of humans to non-extrapolating training examples.
  arXiv  Detail & Related papers  (2022-09-07T08:13:45Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.