How social network analysis can help to measure cohesion in collaborative distance-learning

It has been argued that cohesion plays a central role in collaborative learning. In face-to-face classes, it can be reckoned from several visual or oral cues. In a Learning Management System or CSCL environment, such cues are absent. In this paper, we show that Social Network Analysis concepts, adapted to the collaborative distance-learning context, can help measuring the cohesion of small groups.Working on data extracted from a 10-week distance-learning experiment, we computed cohesion in several ways in order to highlight isolated people, active sub-groups and various roles of the members in the group communication structure. We argue that such processing, embodied in monitoring tools, candisplay global properties both at individual level and at group level and efficiently assist the tutor in following the collaboration within the group. It seems to be more appropriate than the long and detailed textual analysis of messages and the statistical distribution of participants' contributions

LETEC (Learning and Teaching Corpus) Simuligne

Learning and Teaching Corpus of the online educational experiment Simuligne (2001). Its scenario is based on a global simulation for the learning of French as a foreign language. It also includes an intercultural activity, "Interculture", based on the Cultura project. The corpus includes the pedagogical scenario, described in several formats, the research protocol, participant's online interactions and productions (structured in XML), list of participants, licences of use.\ud The LETEC corpus associated (mce.simu.all.all-CP.zip) is organized as an IMS-CP archive. We define a Learning & Teaching Corpus as a structured entity containing all the elements resulting from a communicative on-line learning situation, whose context is described by an educational scenario and a research protocol. The core data collection includes all the interaction data, the productions of the course participants, and the tracks, resulting from the participants’ actions in the learning environment and stored according to the research protocol. In order to be able to be shared, and to respect participant privacy, these data should be anonymised and a license for its use be provided in the corpus. A derived analysis can be linked to a given set of data under consideration, used or computerized for this analysis. An analysis consisting in data annotation/transcription/transformation, accurately connected to its original data, can be merged with the corpus itself, in order for other researchers to compare their own results on a concurrent analysis or to build their complementary analysis upon these results.\ud The definition of a Learning & Teaching Corpus as a whole entity comes from the need of explicit links, between interaction data, context and analyses. This explicit context is crucial for an external researcher to interpret the data and to perform its own analyses.\ud This definition seeks to capture the context of the data stemming from the course in order to allow a researcher to look for, understand and connect this information whether or not he/she was involved in the original course. More details about a LETEC corpus an ist structure at : http://mulce.univ-fcomte.fr/metadata/LETECorpus-en.pd

Who students interact with? A social network analysis perspective on the use of Twitter in language learning

This paper reports student interaction patterns and self-reported results of using Twitter microblogging environment. The study employs longitudinal probabilistic social network analysis (SNA) to identify the patterns and trends of network dynamics. It is building on earlier works that explore associations of student achievement records with the observed network measures. It integrates gender as an additional variable and reports some relation with interaction patterns. Additionally, the paper reports the results of a questionnaire that enables further discussion on the communication patterns

Face-to-face and online interactions - is a task a task?

This study contrasts two different ways of analysing interaction and participation in language learning tutorials: Social network analysis of frequency and QSR analysis of type of interaction. One task from three German beginners' language tutorials (one delivered face-to-face, the other two online) is analysed. A description of the background and method of the study is provided together with some examples of the findings. As this is work in progress, only tentative conclusions can be provided at this stage

Extending validation of tools and analyses in CSCL situations: How to collaborate on interaction analysis?

The Mulce Project aims at developing a server that would allow researchers to share Learning and Teaching Corpora (Letec). Our main goals are: first, to facilitate the work of researchers in the CSCL field by reusing existing corpora instead of creating new experiments, collecting and organizing data, and secondly, to connect these corpora to shared analyses and visualization tools. We also hope that this process of sharing would deepen and widen the validity of research tools and analyses. The limited space of our publications generally does not allow the authors to include their data nor the detailed context of their source experiments, which means that these results cannot be reused nor reproduced

Productive re-use of CSCL data and analytic tools to provide a new perspective on group cohesion

The goals of this paper are twofold: (1) to demonstrate how previously published data can be re-analyzed to gain a new perspective on CSCL dynamics and (2) to propose a new measure of social cohesion that was developed through improvements to existing analytic tools. In this study, we downloaded the Simuligne corpus from the publicly available Mulce repository. We improved the Knowledge Space Visualizer (KSV) to deepen the notion of cohesion by using a dynamic representation of sociograms. The Calico tools have been used and extended to complete this cohesion measure by analyzing lexical markers. These complementary analyses of cohesion, based on clique sizes and communication intensity on the one hand, and lexical markers on the other hand, offer more detailed information on (a) the relationships between participants and (b) the structure and intensity of communication. In particular, the analyses highlight strong convergences that were not visible in the previous analysis

Using indirect blockmodeling for monitoring students roles in collaborative learning networks

Collaborative learning activities have shown to be useful to address educational processes in several contexts. Monitoring these activities is mandatory to determine the quality of the collaboration and learning processes. Recent research works propose using Social Network Analysis techniques to understand students' collaboration learning process during these experiences. Aligned with that, this paper proposes the use of the indirect blockmodeling network analytic technique for monitoring the behaviour of different social roles played by students in collaborative learning scenarios. The usefulness of this technique was evaluated through a study that analysed the students' interaction network in a collaborative learning activity. Particularly, we tried to understand the structure of the interaction network during that process. Preliminary results suggest that indirect blockmodeling is highly useful for inferring and analysing the students' social roles, when the behaviour of roles are clearly different among them. This technique can be used as a monitoring service that can be embedded in collaborative learning applications.Peer ReviewedPostprint (published version

Generating Predictive Models of Learner Community Dynamics

In this paper we present a framework for learner modelling that combines latent semantic analysis and social network analysis of online discourse. The framework is supported by newly developed software, known as the Knowledge, Interaction, and Social Student Modelling Explorer (KISSME), that employs highly interactive visualizations of content-aware interactions among learners. Our goal is to develop, use and refine KISSME to generate and test predictive models of learner interactions to optimise learning

Réutilisation d'un corpus pour une nouvelle analyse des réseaux sociaux grâce à l'adaptation de l'outil KSV

In the field of interaction analysis in computer supported collaborative learning, this paper revisits a previous analysis by applying a new tool on a corpus now available. The goal is to analyze cliques (Social Network Analysis) by traveling along the dimensions of intensity and time, thanks to an adapted version of KSV: Knowledge Space Visualizer. The theory invoked is SNA. This new exploration alows us to suggest new forms of indicators for cohesion and espescially to compare cohesion of different groups. This work also made possible some improvements for KSV that gives it new perspectives.Dans le champ de l'analyse des interactions en situation d'apprentissage collaboratif en ligne, nous revisitons un corpus connu et accessible pour en reprendre l'analyse avec un outil qui offre de nouvelles dimensions d'exploration : l'intensité et le temps. Le cadre d'analyse est ici celui des réseaux sociaux. Cette nouvelle exploration des données nous permet de proposer de nouveaux indicateurs pour comparer la cohésion des groupes au regard de l'intensité de communication. Cette rencontre entre un corpus et un outil a aussi permis d'étendre les capacités de traitement et de visualisation de l'outil KSV, qui lui offrent de nouvelles perspectives