| Summary: | The study of online learning, whether aimed at understanding this form of situated human learning, at evaluating relevant pedagogical scenarios and settings or at improving technological environments, requires the availability of interaction data from all participants in the learning situations. However, usually data are either inaccessible, or of limited access to those who were not involved in the original project. Moreover data are fragmented, therefore decontextualized with respect to the original teaching/learning settings. Sometimes they are buried in a proprietary format within the technological environment. The consequence is that research lacks a scientific basis. In the literature comparisons are often attempted between objects that are ill-defined and may in fact be different. The processes of scientific enquiry, such as re-analyzing, replicating, verifying, refuting or extending the original findings, are therefore disabled. To address this anomaly, we suggest to create and disseminate a new type of corpus, a contextualized learner corpus, entitled "LEarning and TEaching Corpus" (Letec). Such corpora include not only the data that correspond to output of learner activity in online courses, but also their context. Sharing Letec corpora within the research community implies that: (1) corpora are formatted and structured according to a new model which is compatible with existing standards for corpora and for learning design specifications; (2) corpora are placed on a server offering cross-platform compatibility and free access; (3) an ethics policy is formulated as well as copyright-licences. This paper presents the answers brought by our Mulce project from a theoretical and methodological standpoint. We give examples extracted from two learning and teaching corpora (Simuligne and Copéas). We show how data structured accordingly to the Mulce format can be transformed and processed by analysis tools available in different research communities. The article sheds light on the potential of such tools for the study of polyfocalisation and multimodal writing. It concludes with the scientific benefits researchers may expect at an institutional level when collecting and structuring data.
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