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Although learning indicators are now properly studied and published, it is still very difficult to manage them freely within most distance learning platforms. As all activity indicators need to collect and analyze properly traces of the learning activity, we propose to use these traces as a starting point for a platform independent Trace Based-Indicator Management System (TB-IMS). This approach allows learning indicators to be created and reused in such a way that there is no need to modify the computer code of the learning platform. This paper presents the underlying theory and how this theory is implemented in a first TB-IMS. This TB-IMS is illustrated through an actual learning situation based on a Moodle platform. This approach is compared with similar attempts to manage learning indicators properly and is available for use with any other learning platform, provided the TB-IMS can access the learning platform traces.

This article contains the three contributions of the mini-symposium about MOOCs (Massive Open Online Courses) organized by Violaine Louvet (Institut Camille Jordan, CNRS). The first contribution is from Alain Mille from LIRIS, about the main research questions on this new way of learning. The second one is from Marc Buffat, LMFA. He reviews some open source Web2.0 computational environments which are used in a new MOOC on scientific computing. Marco Picasso, EPFL, is the author of the last contribution where he discusses the development of a MOOC in Numerical Analysis, before the MOOC, the course during the MOOC and the results. Many thanks to the authors who give a very interesting overview of the subject.

This paper addresses the issue of assisting observation and analysis of learners' behaviour. Our goal is to propose models/tools to facilitate these tasks for the trainer. We propose an approach based on the exploitation traces. The trace represents learners' practices within learning environments. The principle is to transform traces of low abstraction level to build higher-level information that reflects the learner's behaviour. Our work is part of a project in partnership with the EDF Group aiming to assist trainers in observation and analysis of trainee operators for driving activities on nuclear power plant full-scale simulator. We developed a platform D3KODE that implements our models. D3KODE allows storage, processing and interactive visualisation of traces, and has been experimented with EDF Group experts, trainers and trainees. The result demonstrated D3KODE helped the trainers to confirm/validate more easily realisations and no-realisations of educational objectives trainees and facilitated the exchanges between tutors and trainees. [ABSTRACT FROM AUTHOR]

The interdisciplinary research presented in this article is part of the study of collective human activities supported by technical devices. We begin by stating the objectives of the study and the type of activity observed, i.e. joint mediated writing. Then, we describe our procedure by (i) justifying our methodological choices, which are grounded in a psycho-ergonomic approach of ethnographical inspiration, (ii) detailing the setup of the study and (iii) describing the modes used to present the observables. Next, we present our analysis of a session, while revealing some intermediate results related to the properties of computer traces of interaction and their use in the course of the session. We end with a discussion of the results and limitations of the study followed by the conclusion, which opens up some pathways for designing ‘tracing systems’ capable of supporting joint mediated activities. [ABSTRACT FROM PUBLISHER]

Absrtact Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non-complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process. [ABSTRACT FROM AUTHOR]

We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach. [ABSTRACT FROM AUTHOR]

In small and medium-sized enterprises (SMEs), knowledge management systems cannot be considered in the same way as in large companies. In this paper, it is argued that knowledge management can be achieved within SMEs by the creation of a knowledge-sharing culture. In this context, the aim is not to formalise specific technical job know-how, but rather to formalise simple contextual knowledge descriptors associated with the technical objects handled by actors. A prototype software tool, called KALIS, has been developed to foster collaboration within teams of professionals. The framework of the tool is built to support information and knowledge-sharing between engineers. The tool is based on knowledge repository principles and on three kinds of workspaces associated with a showroom concept to improve awareness and information retrieval. The paper describes the integration of this approach in a mechanical engineering office following a three-month deployment period. It also details observations about collaborative behaviour and the impact of the approach on knowledge construction and sharing. [ABSTRACT FROM AUTHOR]