Ton de Jong

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.

Cognitive Structures of Good and Poor Novice Problem Solvers in Physics.

The way knowledge is organized in memory is generally expected to relate to the degree of success in problem solving. In the present study, we investigated whether good novice problem solvers have their knowledge arranged around problem types to a greater extent than poor problem solvers have. In the subject of physics (electricity and magnetism), 12 problem types were distinguished according to their underlying physics principles. For each problem type, a set of elements of knowledge containing characteristics of the problem situation, declarative knowledge, and procedural knowledge was constructed. All of the resulting 65 elements were printed on cards, and first-year university students in physics ( N = 47) were asked to sort these cards into coherent piles shortly after they had taken an examination on electricity and magnetism. Essentially, good novice problem solvers sorted the cards according to problem types; the sorting by the poor problem solvers seemed to be determined to a greater extent by the surface characteristics of the elements. We concluded than an organization of knowledge around problem types might be highly conducive to good performance in problem solving by novice problem solvers.

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.

Instructional environments for simulations.

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. nHowever, 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. nAn 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. nCognitive 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. nA 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. nMost 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. nA 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).

Knowledge of problem situations in physics: A comparison of good and poor novice problem solvers

In this study we examined models of problem situations in the memory of good and poor novice students. Subjects were shown very briefly descriptions of physics problems, and after each exposure they were asked to reconstruct the given problem. The short exposure time forces students to rely on models of problem situations in memory for giving reconstructions. Presentation of situations, and reconstructions asked for, varied in modality (words, figures, or combinations). For a number of situations subjects were asked, after they had given a reconstruction, to write down information they thought necessary for solving the problems. Results showed that all students reconstructed important information better than less important information, so both good and poor students seem to have models of problem situations at their disposal. There were, however, also differences between the two groups. First, good students gave a better reconstruction of the question than weak students did. Second, when subjects were requested to change modality in reconstruction (from figure to words or vice versa), good students tended to reconstruct important information better than the weak students. Finally, good students outperformed the weak group in generating information concerning the solution of the problem

Characteristics of simulations for instructional settings

This paper discusses the internal characteristics of simulations. The major part of it is concerned with models and their relation with the domain. Some central concepts regarding modelling and simulation are defined. These include concepts regarding: n- the structure and characteristics of the model; n- the relationship to the system that is being modelled; n- the interaction of the learner or other agents with the model. nA classification of model types is presented, accompanied by a first idea on the representation of the several types of models. The classification includes the distinction between qualitative and quantitative models. Models can further be classified into dynamic and static models, determined by the time dependency of the model. The basic elements of any simulation model are the state of the model, describing the properties of the system that is modelled, and a set of rules determining the possible development of the model state. State space is the collection of all possible states. n nIn quantitative models the basic elements of the state are variables, which can be dependent or independent. Dependent variables are variables of which the value is determined by the independent variables. The model rules are equations, determining the development of the values of the variables. Quantitative models are classified into discrete and continuous models, depending on the structure of the state space. Qualitative models have a state space consisting of propositions about the modelled system. In this case, the model rules have a more descriptive character. n nA brief discussion of the relationship between the model and the corresponding real system is given. Three types of real systems are distinguished: physical, artificial and abstract. The main criterion for a distinction between these types of systems is the possibility of constructing a model that describes the system completely (a base model). n nThe interaction of the learner with models and simulations is described by introducing the concepts of interaction and scenario. The interaction describes the sequence of operations that are performed upon the model, the scenario includes the interaction and the agents who take part in the interaction. n nClassifications of instructional simulation environments (often just called: instructional (or educational) simulations) are discussed. The usefulness and features of these classifications are investigated. Many of the existing classifications do not distinguish very well between relevant aspects of simulation learning environment. n nThree sections describe the relationship between the internal characteristics of simulations and the four themes introduced in de Jong (this volume): domain models, learning goals, learning processes and learner activity. Because simulation models are discussed extensively in the first section of this paper, the section on domain and simulation models gives an overview of domain aspects that are not explicitly referred to in the model. Here, an additional knowledge base, called the cognitive model will be introduced. For each type of learning goal the relation with the domain model or scenario is elaborated. The relationship between learning processes and learner activity and domain models is discussed by relating the possible types of learner activity with the model and scenario elements, resulting in demands for the structure of the model or scenario.

Computer assisted learning in higher education in The Netherlands: a review of findings

This article reports some of the major results of a national survey on the use of computer assisted learning (CAL) in Dutch higher education in 1991. As a response to a call for participation, descriptions of 442 different CAL programs that were used in Dutch higher education were received. Most popular usage is in mathematics and sciences, medicine and engineering. The most popular forms of CAL are simulations followed by tutorial applications. In the sciences, emphasis is on simulation, whereas for economics and law, tutorials are most popular, and in humanities we find a large number of drills. It is also remarkable to find a high percentage of combinations of drills and simulations in economics and medicine. There is a greater trend towards providing the learner with more self-control. This reflects the general trend in instructional design to put more responsibility in the hands of the learner (as in the `constructivist approach?). General programming languages and authoring languages are used in about the same proportion as development tools, but simulations are mostly created through general programming languages, and tutorials with the use of authoring languages. By an overwhelming majority, programs have been developed for MS-DOS environments.

Aspects of computer simulations in an instructional context

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 goals 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.

Modelling domain knowledge for intelligent simulation learning environments

Computer simulations are an often applied and promising form of CAL. A main characteristic of computer simulations is that the domain knowledge is represented in amodel. This model contains all necessary information to calculate the behaviour of the simulation in terms of variables and parameters and a set of rules or constraints which determine the changes to the values of the variables. In order to increase the learning effects of computer simulations additional support and guidance should be offered to the learner. This means that simulations should be embedded into a supportive environment, which we will call an Intelligent Simulation Learning Environment (ISLE). One of the basic components of the ISLE should be a formalised representation of the domain. In this paper the structure of this domain representation and its authoring will be discussed. It is argued that the simulation model is a necessary but certainly not sufficient source of information for building a domain representation for an ISLE. Besides a behavioural description as given by the simulation model (the term runnable model will be used) also a cognitive description of the domain is needed. This cognitive model forms the basis for a number of functions to be performed in an ISLE, like diagnosis, instruction and support. The current paper presents a framework which can be used to formalise the cognitive model. In particular the component of the cognitive model which contains a conceptual representation of the domain, the conceptual model will be discussed. An important element of the framework presented is a relation typology which describes the interrelationships between relations that are used for the construction of a cognitive model. This typology will be an important knowledge source for an ISLE and can support the author with constructing the conceptual model.