W.R. van Joolingen

Issues in computer supported inquiry learning in science

Current views on science learning state that this should not involve learning just about the established results of science, including well-established theories such as Newtonian mechanics or the evolution of species as well as important empirical discoveries such as Young’s double slit experiment or the structure of DNA. Instead science learning should also focus on the processes and methods used by scientists to achieve such results. One obvious way to bring students into contact with the scientific way of working is to have them engage in the processes of scientific inquiry themselves, by offering them environments and tasks that allow them to carry out the processes of science: orientation, stating hypotheses, experimentation, creating models and theories, and evaluation (de Jong 2006a). Involving students in the processes of science brings them into the closest possible contact with the nature of scientific understanding, including its strengths, problems and limitations (Dunbar 1999). This is the main claim of inquiry learning: engaging learners in scientific processes helps them build a personal knowledge base that is scientific, in the sense that they can use this knowledge to predict and explain what they observe in the natural world. For about the last 20 years, computers have been used to create environments that engage learners in scientific inquiry activities. The virtue of the computer is that it allows the scaling down of inquiry tasks to a manageable size for learners who are inexperienced with inquiry processes. There are several ways in which computers can help create challenging and manageable environments for inquiry learning: • Replacing the natural world by a computer simulation can help make available on a wide scale the phenomena to be investigated. Moreover, the simulation may be simplified and/or emphasize certain aspects of the domain that can help learners observe critical features of the domain (van Joolingen & de Jong 1991a; de Jong & van Joolingen 1998; de Jong 2006a). • The computer can offer tools that support the inquiry processes, such as tools to analyse or visualize data, tools that help learners state hypotheses and tools that help learners manage the learning process (van Joolingen 1999; Linn et al. 2004a; Quintana et al. 2004; de Jong 2006b). • The computer can support collaboration between learners, allowing them to communicate, share data, results and ideas, and discuss consequences for the knowledge that is under construction (Okada & Simon 1997; van Joolingen et al. 2005). • Computer-based modeling tools allow learners to express their theories in models that can be simulated. In this way learners can use their theories operationally, confronting themselves with the consequences of their ideas (Hestenes 1987; Schecker 1993; Jackson et al. 1996; Fretz et al. 2002; Zhang et al. 2002; Schwarz & White 2005).

Self-directed learning in simulation-based discovery environments

SIMQUEST is an authoring system for designing and creating simulation-based learning environments. The special character of SIMQUEST learning environments is that they include cognitive support for learners which means that they provide learners with support in the discovery process. In SIMQUEST learning environments, a balance is sought between direct guidance of the learning process and sufficient freedom for learners to regulate the learning process themselves. This paper describes the basic mechanisms of the SIMQUEST learning and authoring environments. The functionality authors have in providing the learner with guidance and some of the experiences on how authors use these opportunities and learners employ the cognitive support are reported.

A Broker Architecture for Integration of Heterogeneous Applications for Inquiry Learning

In the context of the CIEL activity within the European network of excellence kaleidoscope, a generic software architecture has been developed to integrate various heterogeneous applications for inquiry learning, including the definition of relevant data formats and interfaces. The general aim of this architecture is to provide a common software platform for various learning applications, facilitating interoperability by means of quasi-synchronous data exchange. Dynamically created learning objects comply with the LOM standard, thus enabling the re-use of these product in follow-up learning activities.

CIEL, architectures for collaborative inquiry and experiential learning

CIEL aims at integrating tools and scenarios for open types of learning in which learners engage in larger, realistic tasks, such as scientific inquiry or engineering. In these kinds of learning, a pivotal place is taken by the products that learners create during their work, such as artifacts, models, hypotheses, sets of data etc. By redefining the concept of learning object to also include these kinds of products, and by defining standards and an ontology for these kinds of learning objects, CIEL enables semantic and technical integration of different learning environments, opening the possibility for supporting larger, more realistic scenarios for collaborative inquiry and experiential learning. A first proof of concept in the domain of sampling is described.