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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/533

Title: Virtual math teams: quantitative analysis
Authors: Cakir, Murat Perit
Shumar, Wesley
Strijbos, Jan-Willem
Xhafa, Fatos
Zhou, Nan
Toledo, Ramon Prudencio S.
Sarmiento, Johann W.
Keywords: Mathematics education;Computer mediated communication;Computer supported cooperative work
Issue Date: 9-Sep-2005
Series/Report no.: IST Research Day posters 2005;no. 412M
Abstract: The Virtual Math Teams (VMT) project is an NSF-funded research program aimed at investigating the innovative use of online collaborative environments to support effective K-12 mathematics learning. In general, collaborative learning is a phenomenon which has still not been fully understood. Earlier efforts in collaborative learning research include studies of various variables that aim to investigate whether and under what circumstances collaborative learning is more effective than individual learning. However, the highly dynamic nature of collaborative learning environments hinders the task of designing traditional experimental studies that focus on the impact of individual variables on learning. This situation motivates the need for more detailed, micro-level analysis of interaction in the context of computer-supported collaborative learning environments. At the VMT project we currently employ quantitative methods to conduct a multilevel analysis of micro features involved in our context. For this purpose, we conducted numerous online experiments and formed a dataset of collaborative sessions. A multi-dimensional coding scheme has been designed and applied on a selected corpus of transcripts in an effort to statistically analyze many aspects of this dataset. A computational model based on our coding scheme has been developed to aid our exploratory work. Our initial efforts involve compiling descriptive statistics for each group and clustering sessions in terms of the way participants organize their problem solving activities. In the light of our initial findings a statistical model is being developed to further reveal/analyze patterns of interaction in the context of virtual math teams.
URI: http://hdl.handle.net/1860/533
Appears in Collections:Research Day Posters (IST)

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