Analysing Frequent Sequential Patterns of Collaborative Learning Activity Around an Interactive Tabletop. Nominee for Best Paper Award

Electronic traces of activity have the potential to be an invaluable source to understand the strategies followed by groups of learners working collaboratively around a tabletop. However, in tabletop and other co-located learning settings, high amounts of unconstrained actions can be performed by different students simultaneously. This paper introduces a data mining approach that exploits the log traces of a problem-solving tabletop application to extract patterns of activity in order to shed light on the strategies followed by groups of learners. The objective of the data mining task is to discover which frequent sequences of actions differentiate high achieving from low achieving groups. An important challenge is to interpret the raw log traces, taking the user identification into account, and pre-process this data to make it suitable for mining and discovering meaningful patterns of interaction. We explore two methods for mining sequential patterns. We compare these two methods by evaluating the information that they each discover about the strategies followed by the high and low achieving groups. Our key contributions include the design of an approach to find frequent sequential patterns from multiuser co-located settings, the evaluation of the two methods, and the analysis of the results obtained from the sequential pattern mining.

[1]  Yoshinori Sagisaka,et al.  Variable-order N-gram generation by word-class splitting and consecutive word grouping , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[2]  D. Leat,et al.  Brains on the Table: Diagnostic and formative assessment through observation , 2000 .

[3]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[4]  Howard J. Hamilton,et al.  Methods for Mining Frequent Sequential Patterns , 2003, Canadian Conference on AI.

[5]  Elena Gaudioso,et al.  Mining Student Data To Characterize Similar Behavior Groups In Unstructured Collaboration Spaces , 2004 .

[6]  Alan M. Lesgold,et al.  Modeling the process of collaborative learning , 2007 .

[7]  H. U. Hoppe,et al.  The Role of Technology in CSCL , 2007 .

[8]  Carolyn Penstein Rosé,et al.  Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments , 2009, EDM.

[9]  Jimmy Frerejean,et al.  Using process mining to identify models of group decision making in chat data , 2009, CSCL.

[10]  Judy Kay,et al.  Clustering and Sequential Pattern Mining of Online Collaborative Learning Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[11]  Thanasis Daradoumis,et al.  Knowledge extraction and representation of collaborative activity through ontology-based and Social Network Analysis technologies , 2009, Int. J. Bus. Intell. Data Min..

[12]  Margaret M. Burnett,et al.  Mining problem-solving strategies from HCI data , 2010, TCHI.

[13]  Ahmed Kharrufa,et al.  Digital tabletops and collaborative learning , 2010 .

[14]  Andrew Olney,et al.  Mining Collaborative Patterns in Tutorial Dialogues , 2010, EDM 2010.

[15]  Patrick Olivier,et al.  Digital mysteries: designing for learning at the tabletop , 2010, ITS '10.

[16]  Cindy E. Hmelo-Silver,et al.  An overview of CSCL methodologies , 2010, ICLS.

[17]  Judy Kay,et al.  Modelling symmetry of activity as an indicator of collocated group collaboration , 2011, UMAP'11.

[18]  Jesus Boticario,et al.  Application of machine learning techniques to analyse student interactions and improve the collaboration process , 2011, Expert Syst. Appl..

[19]  Judy Kay,et al.  Modelling and Identifying Collaborative Situations in a Collocated Multi-display Groupware Setting , 2011, AIED.