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

Metodologías de análisis de los big data en las plataformas educativas

La proliferación de nuevas plataformas educativas por Internet y el avance de la educación online ha abierto nuevas posibilidades de análisis debido al gran volumen de datos generados y almacenados en los servidores. Los usuarios dejan trazas de su actividad, y esta actividad posibilita nuevos análisis del comportamiento de estudiantes y de los contenidos compartidos, difícilmente realizables en la educación cara a cara tradicional. Este trabajo aporta un resumen de las diversas metodologías aplicables a los grandes volúmenes de datos generados por las plataformas educativas, clasificables dentro de los Big Data, así como los diversos campos en los que podrían aplicarse y las mejoras que podrían introducir en el desarrollo de las propias herramientasConsejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía (Proyecto de Excelencia referencia P12-SEJ-328)

Using data analytics for collaboration patterns in distributed software team simulations: the role of dashboards in visualizing global software development patterns

This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of various types of information in relation to Global Software Development (GSD). Due to the nature of this work, and the need for continuous pilot studies, simulations of distributed software teams have been created with the participation of learners from a number of institutions. This paper discusses two pilot studies with the participation of six institutions from two different countries

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

Using data analytics for collaboration patterns in distributed software team simulations: the role of dashboards in visualizing global software development patterns

This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of various types of information in relation to Global Software Development (GSD). Due to the nature of this work, and the need for continuous pilot studies, simulations of distributed software teams have been created with the participation of learners from a number of institutions. This paper discusses two pilot studies with the participation of six institutions from two different countries

Unraveling idea development in discourse trajectories

Conference Theme: The Future of LearningShort Paper Session: SP 6.7With the present paper we want to shed light onto an issue that is central within the knowledge building theory but only little studied – the development of ideas in collaborative learning discourse. Starting from the construction of a network of explicit and implicit relations between ideas, we apply a scientometric method to tackle the temporality of collaborative processes based on the structure of successive ideas. The resulting discourse trajectories are shown to give a holistic and also a detailed view on how knowledge advances when their interpretation is combined with a qualitative analysis of the content of the ideas and their relations. The weighted relevance of relations between ideas enables the identification of sub-topics in the discourse, important ideas, and influence or uptake events.postprintThe 10th International Conference of the Learning Sciences (ICLS 2012), Sydney, Australia, 2-6 July 2012. In ICLS 2012 Proceedings, 2012, v. 2, p. 162-16

Social network analysis for technology-enhanced learning: review and future directions

Sie, R. L. L., Ullmann, T. D., Rajagopal, K., Cela, K., Bitter-Rijpkema, M., & Sloep, P. B. (2012). Social network analysis for technology-enhanced learning: review and future directions. International Journal of Technology Enhanced Learning, 4(3/4), 172-190.By nature, learning is social. The interactions by which we learn from others inherently form a network of relationships among people, but also between people and resources. This paper gives an overview of the potential social network analysis (SNA) may have for social learning. It starts with an overview of the history of social learning and how SNA may be of value. The core of the paper outlines the state-of-art of SNA for technology-enhanced learning (TEL), by means of four possible types of SNA applications: visualisation, analysis, simulation, and interventions. In an outlook, future directions of SNA research for TEL are provided

Sosiaalinen verkostoanalyysi opetuksessa – verkkoympäristöstä luokkahuoneeseen

Group and peer learning can be studied with social network analysis. This enables the automatic quantitative analysis of student collaboration in digital learning environments. However, there are significant challenges in data collection and analysis of classroom environments. The article gives first brief overview of the theory of social network analysis and data collection methods, and then presents concepts of how to apply the future possibilities of Internet of Things and smart devices in data collection. Additionally, a case study of applying social network analysis in the analysis of student collaboration is presented.Ryhmätyö- ja vertaisopiskelua voi tutkia sosiaalisella verkostoanalyysillä. Tämä mahdollistaa opiskelijoiden ryhmätoiminnan automaattisen kvantitatiivisen analyysin digitaalisissa oppimisympäristöissä, mutta luokkahuoneympäristössä tapahtuvan opiskelun analysoinnissa ja tiedonkeruussa on huomattavia haasteita. Artikkelissa luodaan katsaus verkostoanalyysin taustalla olevaan teoriaan ja tiedonkeruumenetelmiin sekä pohditaan esineiden internetin ja älylaitteiden mahdollisuuksia tulevaisuuden tiedonkeräyksessä. Artikkelissa esitetään lisäksi esimerkkitapaus verkostoanalyysin soveltamisesta opiskelijoiden yhteistoiminnan analyysiin.&nbsp

Framework for analyzing online asynchronous discussion by integrating content analysis and social network analysis

Online Asynchronous Discussion (OAD) is a powerful way to conduct online conversation and a significant component of online learning. Unfortunately, existing Learning Management System (LMS) that generally provides online discussion cannot afford a comprehensive evaluation on the content of the transcripts and the level of interaction among participants. Therefore, this research explores the analysis process of OAD qualitatively and quantitatively. The work focuses on Content Analysis (CA) and Social Network Analysis (SNA), two popular methods employed by educators and researchers to analyze online discussion in e-learning environment. Although these two methods are well established, the techniques remain manual. Furthermore, presently, these two methods of analysis are conducted and studied independently. Hence, this research proposes a new framework integrating CA with SNA called CASNA, which provides comprehensive information of the result, and automation of the processes. CASNA is applied and embedded in LMS (Moodle) to validate the proposed framework. This research also introduces sentence as the unit of interaction instead of message to assess the level of participation among students. In addition, in order to qualitatively analyze the online discussion, two text classifiers; the Support Vector Machine (SVM) and the Back-propagation Neural Network (BPNN) approaches are employed to categorize the sentences based on Soller’s model and the results are compared. The evaluation of these two classifiers is done based on precision, accuracy, recall and F-Measure. The result shows that SVM outperform BPNN in terms of precision and accuracy; falls behind BPNN in terms of recall and F-Measure. This research also discusses the use of network indicators of SNA. Adjacency matrix, graph theory and network analysis techniques are applied to quantitatively define the network interactions among participants. This framework takes advantage of the strength of each method and offers dynamic analysis of the textual messages. It is expected to be more informative to educators as well as researchers in measuring the quality and quantity of OAD

Social Network Analysis Used for Modelling Collaboration in Distance Learning Groups

Abstract. We describe a situation of distance learning based on collaborative production occurring within groups over a significant time span. For such a situation, we suggest giving priority to monitoring and not to guiding systems. We also argue that we need models which are easily computable in order to deal with the heterogeneous and the large scale amount of data related to interactions, i.e. models relying on theoretical assumptions which characterise the structures of groups and of interactions. Social Network Analysis is a good candidate we applied to our experiment in order to compute communication graphs and cohesion factors in groups. This application represents an essential part of a system which would enable tutors to detect a problem or a slowdown of group interaction.