Analyzing collaboration and interaction in learning environments to form learner groups

We present a method to form learner groups in collaborative environments.The method analyses the collaboration among learners by means of variables.These variables are used to form homogenous and/or heterogeneous learner groups.The criteria (homogeneous/heterogeneous) and variables are defined by the teacher.The method is automatically performed by a software tool. An important number of academic tasks should be solved collaboratively by groups of learners. The Computer-Supported Collaborative Learning (CSCL) systems support this collaboration by means of shared workspaces and tools that enable communication and coordination between learners. Successful collaboration and interaction can depend on the criteria followed when forming the groups of learners. This paper proposes a method that analyses the collaboration and interaction between learners using a set of indicators or variables about how they solve academic tasks. Then, the concept of data depth is used as a measurement of the closeness of the analysis indicators' values for a learner with respect to the values that the same indicators take for the other learners. Finally, the data depth is used to form new groups of learners whose analysis indicators take similar or different values. Thus, the method enables teachers to form homogeneous and heterogeneous groups according to their preferences. This group formation process is carried out automatically by a software tool. This paper presents two case studies in which the method is applied to form groups of learners who solve academic tasks in different domains (computer programming and data mining).

[1]  Marian Matthews,et al.  Gifted Students Talk about Cooperative Learning. , 1992 .

[2]  David W. Johnson,et al.  Cooperation and Competition: Theory and Research , 1989 .

[3]  H. Oja.  Descriptive Statistics for Multivariate Distributions , 1983 .

[4]  Allyson Hadwin,et al.  Measurement and assessment in computer-supported collaborative learning , 2010, Comput. Hum. Behav..

[5]  Robert J. Sternberg,et al.  Thinking styles: Theory and assessment at the interface between intelligence and personality. , 1994 .

[6]  Crescencio Bravo,et al.  An ontological approach to automating collaboration and interaction analysis in groupware systems , 2013, Knowl. Based Syst..

[7]  Jesús Gallardo,et al.  A groupware system to support collaborative programming: Design and experiences , 2013, J. Syst. Softw..

[8]  Chuen-Tsai Sun,et al.  DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful small learning groups , 2007, Comput. Hum. Behav..

[9]  Jesus Boticario,et al.  A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks , 2009, EDM.

[10]  Georgios Kahrimanis,et al.  Interaction Analysis as a Tool for Supporting Collaboration: An Overview , 2011, Technology-Enhanced Systems and Tools for Collaborative Learning Scaffolding.

[11]  Lasse Lipponen,et al.  Exploring foundations for computer-supported collaborative learning , 2002, CSCL.

[12]  R. Serfling,et al.  General notions of statistical depth function , 2000 .

[13]  Regina Y. Liu,et al.  Multivariate analysis by data depth: descriptive statistics, graphics and inference, (with discussion and a rejoinder by Liu and Singh) , 1999 .

[14]  P. Kirschner,et al.  Social Aspects of CSCL Environments: A Research Framework , 2013 .

[15]  Sunarti Samsudin,et al.  Cooperative Learning: Heterogeneous Vs Homogeneous Grouping , 2006 .

[16]  Mia Hubert,et al.  Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications by Liu, R., Serfling, R., and Souvaine, D. L. , 2008 .

[17]  E. Day,et al.  SOCIAL LOAFING AND GROUP DEVELOPMENT : WHEN " I " COMES LAST , 2009 .

[18]  Martin Muehlenbrock.  Formation of Learning Groups by using Learner Profiles and Context Information , 2005, AIED.

[19]  Feng-Hsu Wang,et al.  Composing High-Heterogeneous and High-Interaction Groups in Collaborative Learning with Particle Swarm Optimization , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[20]  Regina Y. Liu.  On a Notion of Data Depth Based on Random Simplices , 1990 .

[21]  Leen-Kiat Soh,et al.  Improving Group Selection and Assessment in an Asynchronous Collaborative Writing Application , 2010, Int. J. Artif. Intell. Educ..

[22]  Noreen M. Webb,et al.  Verbal Interaction and Learning in Peer-Directed Groups. , 1985 .

[23]  Pierre Dillenbourg,et al.  The Evolution of Research on Computer-Supported Collaborative Learning , 2009 .

[24]  Crescencio Bravo,et al.  A framework for process-solution analysis in collaborative learning environments , 2008, Int. J. Hum. Comput. Stud..

[25]  Pierre Dillenbourg,et al.  An Ambient Awareness Tool for Supporting Supervised Collaborative Problem Solving , 2012, IEEE Transactions on Learning Technologies.

[26]  A. Faris,et al.  The Impact of Homogeneous vs. Heterogeneous Collaborative Learning Groups in Multicultural Classes on the Achievement and Attitudes of Nine Graders towards Learning Science. , 2009 .

[27]  Alicia Nieto-Reyes,et al.  The random Tukey depth , 2007, Comput. Stat. Data Anal..

[28]  M. West.  Sparkling Fountains or Stagnant Ponds: An Integrative Model of Creativity and Innovation Implementation in Work Groups , 2002 .

[29]  John Baer.  Grouping and Achievement in Cooperative Learning , 2003 .

[30]  Kyparissia Papanikolaou,et al.  Investigation of Group Formation using Low Complexity Algorithms , 2007 .