International audience

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.

International audience

The aim of the research described in this paper is to construct learner skill profiles. The profiles represent the proficiency in knowledge described by an ontology. For this, we identify three needs: a need for a knowledge reference system that organizes knowledge, for identification of the learner knowledge used in the exercises and for a diagnosis model of knowledge. We describe how we propose to respond to each need. In particular, we propose the calculation of three types of mastery values for the knowledge. The diagnosis model we suggest takes these assessments into account in order to make a profile of each learner.

In this paper we present a general framework to describe a trace-based learner modelling process. This framework includes three knowledge models: the first model is an explicit representation of observations about learner's interactions with a TEL-system, the second model describes the structure and elements describing the Learner Model and the last model describes the main knowledge elements types that could be required to calculate and infer learner profile elements (leaner individual features).