Publications

Abstract

Computer simulations in an instructional context can be characterized according to four aspects (themes): simulation models, learning goals, learning processes and learner activity. The present paper provides an outline of these four themes. The main classification criterion for simulation models is quantitative vs. qualitative models. For quantitative models a further subdivision can be made by classifying the independent and dependent variables as continuous or discrete. A second criterion is whether one of the independent variables is time, thus distinguishing dynamic and static models. Qualitative models on the other hand use propositions about non-quantitative properties of a system or they describe quantitative aspects in a qualitative way. Related to the underlying model is the interaction with it. When this interaction has a normative counterpart in the real world we call it a procedure. The second theme of learning with computer simulation concerns learning goals. A learning goal is principally classified along three dimensions, which specify different aspects of the knowledge involved. The first dimension, knowledge category, indicates that a learning goal can address principles, concepts and/or facts (conceptual knowledge) or procedures (performance sequences). The second dimension, knowledge representation, captures the fact that knowledge can be represented in a more declarative (articulate, explicit), or in a more compiled (implicit) format, each one having its own advantages and drawbacks. The third dimension, knowledge scope, involves the learning goal's relation with the simulation domain; knowledge can be specific to a particular domain, or generalizable over classes of domains (generic). A more or less separate type of learning goal refers to knowledge acquisition skills that are pertinent to learning in an exploratory environment. Learning processes constitute the third theme. Learning processes are defined as cognitive actions of the learner. Learning processes can be classified using a multilevel scheme. The first (highest) of these levels gives four main categories: orientation, hypothesis generation, testing and evaluation. Examples of more specific processes are model exploration and output interpretation. The fourth theme of learning with computer simulations is learner activity. Learner activity is defined as the ‘physical’ interaction of the learner with the simulations (as opposed to the mental interaction that was described in the learning processes). Five main categories of learner activity are distinguished: defining experimental settings (variables, parameters etc.), interaction process choices (deciding a next step), collecting data, choice of data presentation and metacontrol over the simulation.

Abstract

The use of computer simulations in education and training can have substantial advantages over other approaches. In comparison with alternatives such as textbooks, lectures, and tutorial courseware, a simulation-based approach offers the opportunity to learn in a relatively realistic problem-solving context, to practise task performance without stress, to systematically explore both realistic and hypothetical situations, to change the time-scale of events, and to interact with simplified versions of the process or system being simulated. However, learners are often unable to cope with the freedom offered by, and the complexity of, a simulation. As a result many of them resort to an unsystematic, unproductive mode of exploration. There is evidence that simulation-based learning can be improved if the learner is supported while working with the simulation. Constructing such an instructional environment around simulations seems to run counter to the freedom the learner is allowed to in ‘stand alone’ simulations. The present article explores instructional measures that allow for an optimal freedom for the learner. An extensive discussion of learning goals brings two main types of learning goals to the fore: conceptual knowledge and operational knowledge. A third type of learning goal refers to the knowledge acquisition (exploratory learning) process. Cognitive theory has implications for the design of instructional environments around simulations. Most of these implications are quite general, but they can also be related to the three types of learning goals. For conceptual knowledge the sequence and choice of models and problems is important, as is providing the learner with explanations and minimization of error. For operational knowledge cognitive theory recommends learning to take place in a problem solving context, the explicit tracing of the behaviour of the learner, providing immediate feedback and minimization of working memory load. For knowledge acquisition goals, it is recommended that the tutor takes the role of a model and coach, and that learning takes place together with a companion. A second source of inspiration for designing instructional environments can be found in Instructional Design Theories. Reviewing these shows that interacting with a simulation can be a part of a more comprehensive instructional strategy, in which for example also prerequisite knowledge is taught. Moreover, information present in a simulation can also be represented in a more structural or static way and these two forms of presentation provoked to perform specific learning processes and learner activities by tutor controlled variations in the simulation, and by tutor initiated prodding techniques. And finally, instructional design theories showed that complex models and procedures can be taught by starting with central and simple elements of these models and procedures and subsequently presenting more complex models and procedures. Most of the recent simulation-based intelligent tutoring systems involve troubleshooting of complex technical systems. Learners are supposed to acquire knowledge of particular system principles, of troubleshooting procedures, or of both. Commonly encountered instructional features include (a) the sequencing of increasingly complex problems to be solved, (b) the availability of a range of help information on request, (c) the presence of an expert troubleshooting module which can step in to provide criticism on learner performance, hints on the problem nature, or suggestions on how to proceed, (d) the option of having the expert module demonstrate optimal performance afterwards, and (e) the use of different ways of depicting the simulated system. A selection of findings is summarized by placing them under the four themes we think to be characteristic of learning with computer simulations (see de Jong, this volume).

Abstract

Dichoptic mixtures of equiluminous components of different wavelengths were matched with a binocularly presented "monocular" mixture of appropriate chosen amounts of the same colour components. Stimuli were chosen from the region of 490-630 nm. Although satisfactory colour matches could be obtained, dichoptic mixtures differed from normal mixtures to a considerable extent. Midspectral stimuli tended to be more dominant in the dichoptic mixtures than either short or long wavelength stimuli. An attempt was made to describe the relation between monocular and dichoptic mixtures with one function containing a wavelength variable and an eye dominance parameter.

Abstract

A combination of Southern blot analysis on a panel of tumor-derived somatic cell hybrids and fluorescence in situ hybridization techniques was used to map YACs, cosmids and DNA markers from the Xp11.2 region relative to the X chromosome breakpoint of the renal cell carcinoma-associated t(X;1)(p11;q21). The position of the breakpoint could be determined as follows: Xcen-OATL2-DXS146-DXS255-SYP-t(X;1)-TFE 3-OATL1-Xpter. Fluorescence in situ hybridization experiments using TFE3-containing YACs and cosmids revealed split signals indicating that the corresponding DNA inserts span the breakpoint region. Subsequent Southern blot analysis showed that a 2.3-kb EcoRI fragment which is present in all TFE3 cosmids identified, hybridizes to aberrant restriction fragments in three independent t(X;1)-positive renal cell carcinoma DNAs. The breakpoints in these tumors are not the same, but map within a region of approximately 6.5 kb. Through preparative gel electrophoresis an (X;1) chimaeric 4.4-kb EcoRI fragment could be isolated which encompasses the breakpoint region present on der(X). Preliminary characterization of this fragment revealed the presence of a 150-bp region with a strong homology to the 5' end of the mouse TFE3 cDNA in the X-chromosome part, and a 48-bp segment in the chromosome 1-derived part identical to the 5' end of a known EST (accession number R93849). These observations suggest that a fusion gene is formed between the two corresponding genes in t(X;1)(p11;q21)-positive papillary renal cell carcinomas.