Wouter R. van Joolingen

Scientific Discovery Learning with Computer Simulations of Conceptual Domains

Scientific discovery learning is a highly self-directed and constructivistic form of learning. A computer simulation is a type of computer-based environment that is well suited for discovery learning, the main task of the learner being to infer, through experimentation, characteristics of the model underlying the simulation. In this article we give a review of the observed effectiveness and efficiency of discovev learning in simulation environments together with problems that learners may encounter in discovery learning, and we discuss how simulations may be combined with instructional support in order to overcome these problems.Scientific discovery learning is a highly self-directed and constructivistic form of learning. A computer simulation is a type of computer-based environment that is well suited for discovery learning, the main task of the learner being to infer, through experimentation, characteristics of the model underlying the simulation. In this article we give a review of the observed effectiveness and efficiency of discovery learning in simulation environments together with problems that learners may encounter in discovery learning, and we discuss how simulations may be combined with instructional support in order to overcome these problems.

The learning effects of computer simulations in science education

This article reviews the (quasi)experimental research of the past decade on the learning effects of computer simulations in science education. The focus is on two questions: how use of computer simulations can enhance traditional education, and how computer simulations are best used in order to improve learning processes and outcomes. We report on studies that investigated computer simulations as a replacement of or enhancement to traditional instruction. In particular, we consider the effects of variations in how information is visualized, how instructional support is provided, and how computer simulations are embedded within the lesson scenario. The reviewed literature provides robust evidence that computer simulations can enhance traditional instruction, especially as far as laboratory activities are concerned. However, in most of this research the use of computer simulations has been approached without consideration of the possible impact of teacher support, the lesson scenario, and the computer simulations place within the curriculum.

Co-Lab: research and development of an online learning environment for collaborative scientific discovery learning

There are many design challenges that must be addressed in the development of collaborative scientific discovery learning environments. This contribution presents an overview of how these challenges were addressed within Co-Lab, a collaborative learning environment in which groups of learners can experiment through simulations and remote laboratories, and express acquired understanding in a runnable computer model. Co-Labs architecture is introduced and explicated from the perspective of addressing typical problem areas for students within collaborative discovery learning. From this view the processes of collaboration, inquiry, and modeling are presented with a description of how they have been supported in the past and how they are supported within Co-Labs design and tools. Finally, a research agenda is proposed for collaborative discovery learning with the Co-Lab environment.

Supporting simulation-based learning: the effects of model progression and assignments on definitional and intuitive knowledge

In this study subjects worked with a computer simulation (on the physics domain of oscillation) in which two supportive measures were used: model progression (gradually increasing the simulation model in complexity) and assignments (small exercises). In measuring results of learning from the simulation environments, special attention was given to assessing intuitive knowledge as compared to definitional knowledge. Three experimental conditions were created that differed with respect to the supportive measures available: one group of learners used both model progression and assignments, one group was only supported with model progression, and the third group was provided with neither model progression nor assignments. The results showed a small gain in definitional knowledge for all three conditions. The gain in intuitive knowledge was considerable and differed across the experimental groups in favour of the conditions in which assignments and/or model progression were present.

Supporting hypothesis generation by learners exploring an interactive computer simulation

Computer simulations provide environments enabling exploratory learning. Research has shown that these types of learning environments are promising applications of computer assisted learning but also that they introduce complex learning settings, involving a large number of learning processes. This article reports on an instrument for supporting one of these learning processes: stating hypotheses.The resulting instrument, an hypothesis scratchpad, was designed on the basis of a conceptual representation of the simulation model and tested in an experimental study. In this study three versions of the scratchpad, varying in structure, were compared. It was found that support offered for identifying variables, in the form of a selection list, is relatively successful: students who used this list were better in differentiating different types of variables. For identifying relations, a selection list of relations offered to the students proved unhelpful in finding accurate relations: students using this list stated their hypotheses mainly at a very global level.

An extended dual search space model of scientific discovery learning

This article describes a theory of scientific discovery learning which is an extension of Klahr and Dunbars model of Scientific Discovery as Dual Search (SDDS) model. We present a model capable of describing and understanding scientific discovery learning in complex domains in terms of the SDDS framework. The concepts of hypothesis space and experiment space, central to SDDS, are elaborated and used as a representation of the learners knowledge. Also, we introduce a taxonomy of search operations in hypothesis space which allows us to describe in detail the processes of discovery. Our ideas are tested against data of subjects who comment on the discovery processes of a simulated learner. It is found that the conditions for performance a search operation in hypothesis space include both sufficient knowledge of the search operation itself and reasons for choosing a specific search operation. Furthermore, a number of constraints on the search in hypothesis space is discussed: domain specific and generic prior knowledge, learning goals, and personality factors. We conclude with some recommendations for the design of discovery-based learning environments.

The Difficult Process of Scientific Modelling: An Analysis of Novices' Reasoning During Computer-Based Modelling

Although computer modelling is widely advocated as a way to offer students a deeper understanding of complex phenomena, the process of modelling is rather complex itself and needs scaffolding. In order to offer adequate support, we need a thorough understanding of the reasoning processes students employ and of difficulties they encounter during a modelling task. Therefore, in this study 26 students, working in dyads, were observed while working on a modelling task in the domain of physics. A coding scheme was developed in order to capture the types of reasoning processes used by students. Results indicate that most students had a strong focus on adjusting model parameters to fit the empirical data with little reference to prior knowledge. The successful students differed from the less successful students in using more prior knowledge and in showing more inductive reasoning. These observations lead to suggestions for the design of appropriate scaffolds.

Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain

This article describes a study into the role of heuristic support in facilitating discovery learning through simulation‐based learning. The study compares the use of two such learning environments in the physics domain of collisions. In one learning environment (implicit heuristics) heuristics are only used to provide the learner with guidance derived from heuristics, without presenting the heuristics themselves; in the other (explicit heuristics) the heuristics themselves are also made explicit to the learner. The two learning environments are tested with 46 students from two schools. The results show that learners in both conditions gain domain knowledge from pre‐test to post‐test. Regression analyses show that pre‐test results can predict post‐test results in the implicit heuristics condition but not in the explicit heuristic condition. Process analyses suggest that presenting the heuristics explicitly facilitate more self‐regulation in students.

The effect of external representation on constructing computer models of complex phenomena

Computer modeling – creating executable modelsof science domains – has been recognized as animportant teaching method. Still not much isknown about the factors making modelingenvironments effective in use. We investigatethe effect of different externalrepresentations on the construction of computermodels. Representations can significantlyinfluence the processes of modeling. In orderto find the specific benefits of two differentrepresentations, we compare dyads working on acollaborative modeling task using a text-basedmodel representation, in which correctequations are required to make the model run,with others using a graphical representation,in which the model is built by qualitativelylinking variables. The learners, secondaryschool students and modeling novices, workedwith the representations on a task in thedomain of physics. Results indicate that thetwo representations induce differentbehaviours, which are appropriate for differentphases of the modeling process.

Promoting self directed learning in simulation based discovery learning environments through intelligent support

Providing learners with computer-generated feedback on their learning process in simulation-based discovery environments cannot be based on a detailed model of the learning process due to the “open” character of discovery learning. This paper describes a method for generating adaptive feedback for discovery learning based on an “opportunistic” learning model that takes the current hypothesis of the learner and the experiments performed to test this hypothesis as input. The method was applied in a simulation–based learning environment in the physics domain of collisions. Additionally, the method was compared to an environment in which subjects received predefined feedback on their hypotheses, not taking the experimentation behavior into account. Results showed that overall both groups did not differ on knowledge acquired. A further analysis indicated that, in their learning processes, the learners in the experimental condition built upon their intuitive knowledge base, whereas the learners in the control condition built upon their conceptual knowledge base. In addition, measures of the learning process showed that the subjects in the experimental condition adopted a more inquiry-based learning strategy compared to the subjects in the control condition. We concluded, therefore, that providing learners with adaptive feedback had a different and beneficial effect on the learning process compared to more traditional predefined feedback.