Abstract
Many universities are using computer-supported collaborative-inquiry-learning (CSCiL) environments to develop their students' skills in collaboration, problem solving, and critical thinking. Diverse states of discourse during CSCiL occur in sequences, but we do not yet fully understand which patterns are beneficial to learning and when exactly to foster them. This study used transition-rate analysis, entropy-analysis, and sequential pattern mining to analyze the chat message of 144 students of two-year colleges. The participants worked on tasks related to Ohm's Law in a simulation-based collaborative-inquiry-learning environment. The results revealed that students in groups who completed tasks successfully tended to ensure that everyone in their group had a shared understanding of the relationship between the variables before they moved on to the next step. In contrast, students in groups who did not complete tasks successfully were more likely to regulate the process without reaching a shared understanding.Gaoxia Zhu is a PhD candidate and research assistant in the Institute for Knowledge Innovation & Technology, Ontario Institute for Studies in Education (OISE), University of Toronto with background in Educational Technology and Curriculum Studies. Her research interests include Knowledge Building, learning analytics, and CSCL.Wanli Xing is an Assistant Professor in Instructional Technology at Texas Tech University, USA with background in learning sciences, statistics, computer science and mathematical modelling. His research interests are educational data mining, learning analytics, and CSCL.Vitaliy Popov is a research associate in Jacobs Institute for Innovation in Education at University of San Diego. Dr. Popov areas of interest include: computer supported collaborative learning, mobile learning, learning sciences, learning analytics, and technology-enhanced learning across cultures.
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