Dr Armin Weinberger, a Professor of Department of Educational Technology, Saarland University, Germany is in NIE to deliver a talk entitled "CSCL Scripts for Divergence-Convergence Transitions".
Do learners in Computer-Supported Collaborative Learning (CSCL) environments share and converge with respect to their knowledge by learning together? Sharing knowledge is a central concept in the area of CSCL, suffering, however, from two problems. First, research on knowledge convergence currently suffers from a lack of systematic conceptualization and thus, operationalization, of the convergence construct. Second, studies show, that cognitive convergence in terms of sharing knowledge after collaboration is typically surprisingly low. When and how is knowledge convergence then the "engine” of collaborative learning?
In this talk, the first problem will be addressed by conceptualizing and operationalizing different types of knowledge convergence before, during, and after a CSCL experience. With respect to the second problem the instructional approach of collaboration scripts will be presented. Collaboration scripts are instructional interventions that specify and cluster learning activities, organize them in roles and assign and sequence these roles in groups. Results of an empirical study will be presented showing how a specific script fosters knowledge divergence during and knowledge convergence after a CSCL session.
Time: 3:15 – 4:15 pm
Ms Katharina Westermann, a Research Associate cum Ph.D Student from the Department of Educational Technology, Ruhr-Universität Bochum, Germany follows with a talk entitled "Collaborative Learning with Multiple Student-Generated Representations: The Balance of Providing and Withholding Support Structures".
The benefits of learning with more than one representation have been broadly recognized in educational psychology (Ainsworth, 2006) and mathematics education (Cuoco & Curcio, 2001), but these benefits do not unfold automatically. One possible approach to promoting learning with multiple representations is to integrate collaborative learning (Rummel & Braun, 2009). Interesting findings on collaborative learning with multiple representations have been presented by the work on productive failure (Kapur, 2009). This work suggests that students can profit from generating multiple representations during collaborative problem-solving prior to instruction. Building on the work on productive failure and on the literature on collaborative learning, we conducted a study a study to answer the following questions: What type of support do students need when they learn with self-generated representations in small groups? Does learning with multiple student-generated representations also foster learning in direct instruction settings?