This article presents a methodology for advancing Learning Analytics by automatically reconstructing the organisational structure of Moodle courses leveraging platform logs and backup data. Using an extensive, pseudonymised dataset gathered from twelve undergraduate courses, we illustrate how student activity records can be enriched with structural information. This methodology integrates xAPI statements with parsed XML data from Moodle course backups to create directed graph representations that capture both hierarchical and sequential relationships among sections and activities. By introducing graph-based structural modelling into Learning Analytics practice, we provide new tools to facilitate replicable and context-aware analytics, enabling deeper insights into the relationship between course structure and learning behaviours, as well as actionable enhancement of digital course design.

Videos are a powerful media to learn activities through guided physical training such as surgery, especially when they are produced following human learning models and not as "how-to" videos. However, their success greatly depends on how they are integrated into the extensive curricula of domains where learning occurs through guided practice. In this work, we investigate the impact of integrating video as a learning tool into the learning curricula of surgery. We created a pedagogical video on surgical hysterectomy through a model based on the Conceptual Fields theory (Vergnaud) and performed two rounds of interviews with seven medical residents, who watched the video freely during their residency in gynecology-obstetrics as they trained with experts. We find that videos can complement guided physical training, as they can provide the rationale behind expert action, something that is difficult to explicit during guided training. Still, their linear and static nature limits their integration as true adjuncts. We discuss our vision of moving towards interactive videos created with an ontological approach, developed in a workshop with four expert surgeons, which involves the ability to navigate through levels of information and layers of representations, so that experts can represent information to learners according to pedagogical models that complement their complex and extensive learning curricula.

Robot-Assisted Laparoscopic Surgery (RALS) is now prevalent in operating rooms. This situation requires future surgeons to learn Classic Laparoscopic Surgery (CLS) and RALS simultaneously. Therefore, along with the investigation of the differences in performance between the two techniques, it is essential to study the impact of training in RALS on the skills mastered in CLS. In this article, we study comanipulated RALS (Co-RALS), one of the two designs for RALS, where the human and the robot share the execution of the task. We use a rarely used in Human–Robot Interaction measuring tool: gaze tracking and time recording to measure for the acquisition of skills in CLS when training in Co-RALS or in CLS and time recording to compare the learning curves between Co-RALS and CLS. These metrics allow us to observe differences in Co-RALS and CLS. Training in Co-RALS develops slightly better but not significantly better hand–eye coordination skills and significantly better timewise performance compared with training in CLS alone. Co-RALS enhances timewise performance in laparoscopic surgery on specific types of tasks that require precision rather than depth perception skills compared with CLS. The results obtained enable us to further define the Human–Robot Interaction quality in Co-RALS.

International audience

Students' questions are essential to help teachers in assessing their understanding and adapting their pedagogy. However, in a flipped classroom context where many questions are asked online to be addressed in class, selecting questions can be difficult for teachers. To help them in this task, we present here three alternative ways of organizing questions: one based on pedagogical needs, one based on estimated students' profiles and one mixing both approaches. Results of a survey filled by 37 teachers in a flipped classroom pedagogy show no consensus over a single organization. A cluster analysis based on teachers' flipped classroom experience allowed us to distinguish two profiles, but they were not associated with any particular question organization preference. Qualitative results suggest the need for different organizations may rely more on a pedagogical philosophy and advocates for differentiated dashboards.

Going beyond mere forum posts categorization is key to understand why some students struggle and eventually fail in MOOCs. We propose here an extension of a coding scheme and present the design of the associated automatic annotation tools to tag students' questions in their forum posts. Working of four sessions of the same MOOC, we cluster students' questions and show how the obtained clusters are consistent across all sessions and can be sometimes correlated with students' success in the MOOC. Moreover, it helps us better understand the nature of questions asked by successful vs. unsuccessful students.

Automatic analysis of learners' questions can be used to improve their level and help teachers in addressing them. We investigated questions (N=6457) asked before the class by 1st year medicine/pharmacy students on an online platform, used by professors to prepare their on-site Q&A session. Our long-term objectives are to help professors in categorizing those questions, and to provide students with feedback on the quality of their questions. To do so, first we manually categorized students' questions, which led to a taxonomy then used for an automatic annotation of the whole corpus. We identified students' characteristics from the typology of questions they asked using K-Means algorithm over four courses. The students were clustered by the proportion of each question asked in each dimension of the taxonomy. Then, we characterized the clusters by attributes not used for clustering such as the students' grade, the attendance, the number and popularity of questions asked. Two similar clusters always appeared: a cluster (A), made of students with grades lower than average, attending less to classes, asking a low number of questions but which are popular; and a cluster (D), made of students with higher grades, high attendance, asking more questions which are less popular. This work demonstrates the validity and the usefulness of our taxonomy, and shows the relevance of this classification to identify different students' profiles.

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

This paper presents DOP8: a Data Mining Iterative Cycle that improves the classical data life cycle. While the latter only combines the data production and data analysis phases, DOP8 also integrates the analysis operators life cycle. In this cycle, data life cycle and operators life cycle processing meet in the data analysis step. This paper also presents a reification of DOP8 in a new computing platform: UnderTracks. The latter provides a flexibility on storing and sharing data, operators and analysis processes. Undertracks is compared with three types of platform 'Storage platform', 'Analysis platform' and 'Storage and Analysis platform'. Several real TEL analysis scenarios are present into the platform, (1) to test Undertracks flexibility on storing data and operators and (2) to test Undertracks flexibility on designing analysis processes.

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).

In this paper we present an approach, based on exploiting and modeling empirical knowledge, for design an adaptive intelligent tutoring system in medical education. We present our learning knowledge design constraints and their related computer representations. We conclude with the possibilities of our approach and their perspectives.