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Capitalisation of Analysis Processes : Enabling Reproducibility, Openess and Adaptability thanks to Narration

Abstract : Analysis processes of learning traces, used to gain important pedagogical insights, are yet to be easily shared and reused. They face what is commonly called a reproducibility crisis. From our observations, we identify two important factors that may be the cause of this crisis: technical constraints due to runnable necessities, and context dependencies. Moreover, the meaning of the reproducibility itself is ambiguous and a source of misunderstanding. In this paper, we present an ontological framework dedicated to taking full advantage of already implemented educational analyses. This framework shifts the actual paradigm of analysis processes by representing them from a narrative point of view, instead of a technical one. This enables a formal description of analysis processes with high-level concepts. We show how this description is performed, and how it can help analysts. The goal is to empower both expert and non-expert analysis stakeholders with the possibility to be involved in the elaboration of analysis processes and their reuse in different contexts, by improving both human and machine understanding of these analyses. This possibility is known as the capitalisation of analysis processes of learning traces.
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Alexis Lebis, Marie Lefevre, Vanda Luengo, Nathalie Guin. Capitalisation of Analysis Processes : Enabling Reproducibility, Openess and Adaptability thanks to Narration. LAK '18 - 8th International Conference on Learning Analytics and Knowledge, Mar 2018, Sydney, Australia. pp.245-254, ⟨10.1145/3170358.3170408⟩. ⟨hal-01714184⟩



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