Analysis-based Learning on Multiple Levels of Mental Domain Representation

Plötzner, Rolf; Spada, Hans

doi:10.1007/978-3-642-77228-3_5

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…Generic knowledge test. Generic knowledge can be defined as the knowledge needed to recognize and work with numerical and graphically depicted relations between two or more variables (Ploetzner & Spada, 1992). Knowledge about relations is needed to state hypotheses about relations between variables and to interpret the results of experiments.…”

Section: Knowledge Testsmentioning

confidence: 99%

The relation between prior knowledge and students' collaborative discovery learning processes

2005

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In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and ''testability'' of a series of domain-specific propositions, a detailed description of the ''knowledge configuration'' for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories ''hypotheses generation'' and ''experimentation.'' Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development. ß 2005 Wiley Periodicals, Inc. J

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Section: Knowledge Testsmentioning

confidence: 99%

The relation between prior knowledge and students' collaborative discovery learning processes

2005

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“…A third case, where not all variables are given but have to be constructed by the learner, can also be distinguished. 1 A second characteristic of complex domains is that relations may be formulated at different levels of precision (see van van Joolingen & de Jong, 1993;Plötzner & Spada, 1992), meaning that the evaluation of a hypothesized rule or set of rules is dependent on the precision needed.…”

Section: An Extended Model Of Discovery Learningmentioning

confidence: 99%

“…Qualitative statements about a domain are less precise than quantitative ones, but may be useful for understanding a domain. Plötzner, Spada, Stumpf & Opwis (1992;Plötzner & Spada, 1992) also introduce an ordering in precision for relations. They distinguish three levels: a qualitative relational, quantitative relational and quantitative numerical.…”

Section: Hypothesis Spacementioning

confidence: 99%

“…On the basis of the result of this experiment the first hypothesis is rejected and replaced by another, which is correct. After a second experiment this hypothesis is accepted (by marking it as "true" on the hypothesis scratchpad) and specified to a quantitative relational level (in terms of Plötzner et al, 1992). After some more experiments, Figure 5.…”

Section: The Discovery Environmentmentioning

confidence: 99%

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Joolingen

Jong

1997

Instructional Science

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This article describes a theory of scientific discovery learning which is an extension of Klahr and Dunbar's 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 learner's 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.

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“…Model progression has been applied in systems such as QUEST (White & Fredriksen, 1989;, NEWTON (Teodoro, 1992), and DIBI (PlOtzner & Spada, 1992). Model progression is used to avoid a confrontation of learners with a model that is too complex with respect to their prior knowledge.…”

Section: Model Progressionmentioning

confidence: 99%

SMISLE: System for Multimedia Integrated Simulation Learning Environments

Jong

Joolingen

Scott

et al. 1994

Design and Production of Multimedia and Simulation-Based Learning Material

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The SMISLE project (System for Multimedia Integrated Simulation Learning Environments) has two main objectives. First, it aims to defme exploratory learning environments based on simulations that incorporate instructional support for learners in such a way that effective and efficient learning will result. Second, it aims to provide authors of these simulation learning environments with an authoring toolkit that not only presents technical, but also conceptual support. Providing support to the learner can be done in many different ways. The project started with an inventory of potential instructional support measures and selected four types of measures that now have been implemented: progressive model implementation, assignments, explanations, and hypothesis scratchpads. The simulation environments that incorporate these support measures are designed around five different models each carrying a specific function. The runnable model is an efficient representation of the domain that will make the simulation run; the cognitive model is the representation of the domain that is tailored to learning and instruction; the instructional model incorporates the instructional support; the learner model keeps track of knowledge and characteristics of the learner; and the interface model decides upon the appearance of the simulation environment to the student. Together these models form the resulting application for the learner, which is called a MISLE (Multimedia Integrated Simulation Learning Environment). The main task of an author is to create the different models in the MISLE, with the exception of the runnable model which is automatically generated from the cognitive model. Creating the different models essentially means that an author has to select, specialise and instantiate generic building blocks (that can be regarded as generic templates) that are offered in libraries of building blocks. For each of the models there is a separate library of building blocks and a set of dedicated editors for specialising and instantiating the building blocks. Additionally, authors are guided through the authoring process by a methodology and they have access to instructional advice which provides them with ideas on which instructional support measures to apply. 133 T. de long and L. Sarti (eds.), Design and Production of

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Analysis-based Learning on Multiple Levels of Mental Domain Representation

Cited by 9 publications

References 20 publications

The relation between prior knowledge and students' collaborative discovery learning processes

The relation between prior knowledge and students' collaborative discovery learning processes

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SMISLE: System for Multimedia Integrated Simulation Learning Environments

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