Regulative support for collaborative scientific inquiry learning S. Manlove, A.W. Lazonder & T. de Jong Department of Instructional Technology, University of Twente, Enschede, The Netherlands Abstract This study examined whether online tool support for regulation promotes student learning during collaborative inquiry in a computer simulation-based learning environment. Sixty-one students worked in small groups to conduct a scientific inquiry with fluid dynamics. Groups in the experimental condition received a support tool with regulatory guidelines; control groups were given a version of this tool from which these instructions were removed. Results showed facilitative effects for the fully specified support tool on learning outcomes and initial planning. Qualitative data elucidated how regulative guidelines enhanced learning and suggests ways to further improve regulative processes within collaborative inquiry learning settings. Keywords collaboration, inquiry learning, science teaching, self-regulation. Introduction The National Research Council advocates methods of science education that enable students to construct scientific understanding through an iterative process of theory building, criticism, and refinement based on their own questions, hypotheses, and data analysis ac- tivities (Bransford et al. 2002). These notions of learning science coincide with the tenets of collabora- tive inquiry learning. This didactical approach de- scribes science learning as students working in groups to perform experiments and build computer models to induce, express, and refine scientific knowledge. Recent technological advances have increased the possibilities to mediate these learning processes with electronic environments, tools, and resources. Learn- ing within these environments is generally assumed to lead to a deeper and more meaningful understanding, because students process scientific content in an ac- tive, constructive, and authentic way. However, a re- view by De Jong and Van Joolingen (1998) showed that the effectiveness of inquiry learning is challenged by intrinsic problems many students have with this mode of learning. For example, students often have difficulty formulating testable hypotheses, designing conclusive experiments, and attending to compatible data. Within modelling, students often fail to engage in dynamic iterations between examining output and revising models (Hogan & Thomas 2001). These problems are usually addressed by cognitive tools: support structures which aim to compensate for stu- dents’ knowledge or skill deficiencies. Examples of effective support tools include proposition tables to help generate hypotheses (De Jong 2006), adaptive advice for extrapolating knowledge from simulations (Leutner 1993), and model progression to assist stu- dents in dealing with the complexity of simulations (Swaak et al. 1998). Recent overviews of cognitive tools for inquiry learning are given by De Jong (2006) and Linn et al. (2004). Another class of problems pertains to the students’ ability to regulate their own learning. Collaborative inquiry learning typically requires high degrees of cognitive regulation, in that students have to plan a Correspondence: Sarah Manlove, Department of Behavioral Sciences, Instructional Technology, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands. E-mail: s.a.manlove@utwente.nl Accepted: 30 December 2005 & 2006 The Authors. Journal compilation & 2006 Blackwell Publishing Ltd Journal of Computer Assisted Learning 22, pp87–98 87 Original article