An integrated approach for analysing and assessing the performance of virtual learning groups

Collaborative distance learning involves a variety of elements and factors that have to be considered and measured in order to analyse and assess group and individual performance more effectively and objectively. This paper presents an approach that integrates qualitative, social network analysis (SNA) and quantitative techniques for evaluating online collaborative learning interactions. Integration of various different data sources, tools and techniques provides a more complete and robust framework for group modelling and guarantees a more efficient evaluation of group effectiveness and individual competence. Our research relies on the analysis of a real, long-term, complex collaborative experience, which is initially evaluated in terms of principled criteria and a basic qualitative process. At the end of the experience, the coded student interactions are further analysed through the SNA technique to assess participatory aspects, identify the most effective groups and the most prominent actors. Finally, the approach is contrasted and completed through a statistical technique which sheds more light on the results obtained that far. The proposal draws a well-founded line toward the development of a principled framework for the monitoring and analysis of group interaction and group scaffolding which can be considered a major issue towards the actual application of the CSCL proposals to real classrooms.Peer ReviewedPostprint (author's final draft

Desktop multimedia environments to support collaborative distance learning

Desktop multimedia conferencing, when two or more persons can communicate among themselves via personal computers with the opportunity to see and hear one another as well as communicate via text messages while working with commonly available stored resources, appears to have important applications to the support of collaborative learning. In this paper we explore this potential in three ways: (a) through an analysis of particular learner needs when learning and working collaboratively with others outside of face-to-face situations; (b) through an analysis of different forms of conferencing environments, including desktop multimedia environments, relative to their effectiveness in terms of meeting learner needs for distributed collaboration; and (c) through reporting the results of a formative evaluation of a prototype desktop multimedia conferencing system developed especially for the support of collaborative learning. Via these analyses, suggestions are offered relating to the functionalities of desktop multimedia conferencing systems for the support of collaborative learning, reflecting new developments in both the technologies available for such systems and in our awareness of learner needs when working collaboratively with one other outside of face-to-face situations

Social networks and performance in distributed learning communities

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance students' success and performance. We used a monitoring system for social network data gathering. Results from correlation analyses showed that students' social network characteristics are related to their performancePostprint (published version

Integration of Exploration and Search: A Case Study of the M3 Model

International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics

Transferable neural networks for enhanced sampling of protein dynamics

Variational auto-encoder frameworks have demonstrated success in reducing complex nonlinear dynamics in molecular simulation to a single non-linear embedding. In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification that allows us to rapidly perform sampling in multiple related systems. We first demonstrate our method is able to describe the effects of force field changes in capped alanine dipeptide after learning a model using AMBER99. We further provide a simple extension to variational dynamics encoders that allows the model to be trained in a more efficient manner on larger systems by encoding the outputs of a linear transformation using time-structure based independent component analysis (tICA). Using this technique, we show how such a model trained for one protein, the WW domain, can efficiently be transferred to perform enhanced sampling on a related mutant protein, the GTT mutation. This method shows promise for its ability to rapidly sample related systems using a single transferable collective variable and is generally applicable to sets of related simulations, enabling us to probe the effects of variation in increasingly large systems of biophysical interest.Comment: 20 pages, 10 figure