Educational Simulations

Educational simulations - literature research by Nico Rutten (www.linkedin.com/in/NicoRutten)

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Educational Simulations by Mind Map: Educational Simulations

1. research of literature by Nico Rutten www.linkedin.com/in/NicoRutten

2. in general

2.1. Simulations ...

2.1.1. help the students to identify relations between components of a system and to learn to control such a system.

2.1.2. establish a cognitive framework or structure to accommodate further learning in a related subject area.

2.1.3. provide an opportunity for reinforcing, integrating and extending previous learned material.

2.1.4. offers the opportunity to ...

2.1.4.1. learn in a relatively realistic problem-solving context

2.1.4.2. practise task performance without stress

2.1.4.3. systematically explore both realistic and hypothetical situations

2.1.4.4. change the time-scale of events

2.1.4.5. interact with simplified versions of the process or system being simulated.

2.1.5. allow students to ...

2.1.5.1. postulate abstract concepts in a more concrete manner

2.1.5.2. convey insight into complicated phenomena and relationships

2.1.5.3. practice lab techniques prior to the actual laboratory experience

2.1.6. engage student interest

2.1.7. provide the learner with an active role in the learning process

2.1.8. help students observe and understand dynamic processes

2.1.9. enhance decision making skills

2.1.10. provide a realistic cause-and-effect environment in which students can quickly, safely and efficiently investigate to learn.

2.2. features that help better learning of difficult concepts of science:

2.2.1. Simulations should be based on real events and data.

2.2.1.1. Too simplified representations may confuse learners,

2.2.1.2. but exact representations on the other hand may make them over complex.

2.2.2. Use of multiple representations, graphs and an opportunity to observe any graphs forming while the experiment is running (in real time).

2.2.3. Facilities to tailor activity to student ability levels and a narrative for students to follow.

3. theory

3.1. Constructivism

3.1.1. constructivism: the view that students construct their knowledge from individual and/or interpersonal experiences and from reasoning about these experiences

3.1.2. constructivistic approach: a strong emphasis is placed on the learner as an active agent in the process of knowledge acquisition

3.1.3. Constructivist learning environments may frustrate students ...

3.1.3.1. who have shallow motivations for academic work ...

3.1.3.1.1. because there is no correct answer

3.1.3.1.2. specific task requirements are not furnished for them

3.1.3.1.3. and there is difficulty quantitatively comparing understanding among students.

3.1.3.2. who have more sophisticated motivations to understand the material ...

3.1.3.2.1. because of the relatively unstructured nature of the constructivist learning environment.

3.2. Cognitive theory

3.2.1. characteristics

3.2.1.1. attention to non-observable processes and knowledge states

3.2.1.2. a detailed study of expertise as compared to novice behaviour

3.2.1.3. revival of research methods such as thinking aloud

3.2.1.4. and the study of complex, real life tasks additional to highly structured artificial tasks in a lab situation.

3.2.2. instructional principles

3.2.2.1. conceptual knowledge

3.2.2.1.1. sequence and choice of models and problems: offering models in a simulation in a specific order, such that relatively ‘easy’ models are offered first and the ‘target’ models last.

3.2.2.1.2. providing explanations

3.2.2.1.3. minimization of error: instead of remediating incomplete knowledge and misconceptions in learners, one can try to prevent these misconceptions from occurring.

3.2.2.2. operational knowledge

3.2.2.2.1. learning takes place in a problem solving context

3.2.2.2.2. model tracing

3.2.2.2.3. immediate feedback

3.2.2.2.4. minimization of working memory load

3.2.2.3. knowledge acquisition skills

3.2.2.3.1. strategies for monitoring comprehension

3.2.2.3.2. the teacher/tutor as ...

3.2.2.3.3. shared responsibility for the task: knowledge acquisition skills seem to be better acquired when working in groups. In groups, learners are ...

3.2.2.3.4. teaching and supporting multiple learning strategies: e.g. learners can choose to follow open ended exploration or to hear an explanation

3.3. Domain models

3.3.1. instructional recommendations:

3.3.1.1. providing multiple views of the same model

3.3.1.1.1. multiple viewpoints or perspectives

3.3.1.1.2. introducing qualitative descriptions of basically numeric relations

3.3.1.1.3. offering information on the epistemological qualities of the model

3.3.1.1.4. presenting a demonstration mode, in which no learner activity is involved, before continuing with a discovery mode

3.3.1.1.5. showing relations in the model in different ways (diagrams, functions)

3.3.1.2. progressive implementation of models

3.3.1.2.1. increasing complexity of models by adding elements and relations

3.3.1.2.2. increasing level of difficulty

3.3.1.2.3. presenting some central, general principles and then proceed by introducing the less central concepts and principles

3.3.1.2.4. start with the recall of prerequisite knowledge

3.3.1.2.5. working with a sub-model in a complex model

3.3.1.3. offering domain information in a more direct instructional way

3.4. Instructional Design theories

3.4.1. instructional features include:

3.4.1.1. sequencing of increasingly complex problems to be solved

3.4.1.2. the availability of a range of help information on request

3.4.1.3. the presence of an expert troubleshooting module which can step in to provide ...

3.4.1.3.1. criticism on learner performance

3.4.1.3.2. hints on the problem nature

3.4.1.3.3. or suggestions on how to proceed.

3.4.1.4. the option of having the expert module demonstrate optimal performance afterwards

3.4.1.5. the use of different ways of depicting the simulated system

3.5. Conversation theory

3.5.1. distinction between two levels of learning:

3.5.1.1. learning about “how”, also called “operation learning”

3.5.1.2. learning about “why”, also known as “comprehension learning”

3.5.2. Full comprehension of a topic means the ability to explain both the how and why.

3.6. Bloom

3.6.1. Bloom’s learning goal tripartition:

3.6.1.1. knowledge-related objectives, related to the acquisition and application of knowledge and understanding

3.6.1.2. attitude-related objectives, concerned with attitudes and feelings which are brought about as a result of some educational process

3.6.1.3. motor skills-related objectives, dealing with the development of manipulative or physical skills

3.7. Gagné and Briggs

3.7.1. Gagné’s types of learned capabilities:

3.7.1.1. intellectual skill, which enables the learner to do something that requires cognitive processing – procedural knowledge

3.7.1.2. cognitive strategy, referring to skill by means of which learners exercise control over their own processes of learning and thinking

3.7.1.3. verbal information, sometimes called declarative knowledge, to imply that its acquisition makes it possible for the learner to declare/state it

3.7.1.4. attitude, involving an acquired internal (motivational) state that influences the learner’s choice of personal action

3.7.1.5. motor skill, which involves refining (muscular) movement in timing and smoothness by the learning that occurs during practice

3.7.2. Gagné and Briggs’ strategy consists of the following phases:

3.7.2.1. gaining attention

3.7.2.2. informing the learner of the objective

3.7.2.3. stimulating recall of prerequisite learning

3.7.2.4. presenting the stimulus material

3.7.2.4.1. overt: the model is presented in one or another way

3.7.2.4.2. covert: the learner has to induce model properties from input/output relations

3.7.2.5. providing learner guidance

3.7.2.6. eliciting the performance

3.7.2.7. providing feedback

3.7.2.8. assessing performance

3.7.2.9. enhancing retention and transfer

3.8. Romiszowski

3.8.1. Romiszowski’s learning goal taxonomy:

3.8.1.1. Knowledge refers to the information stored in the learner’s mind.

3.8.1.1.1. factual knowledge

3.8.1.1.2. conceptual knowledge

3.8.1.2. skill

3.8.1.2.1. Skill refers to ... which are performed in a competent way in order to achieve a goal.

3.8.1.2.2. categories:

3.9. Alessi and Trollip

3.9.1. Alessi and Trollip’s classification of simulation types:

3.9.1.1. physical simulation: allows for the manipulation of physical objects displayed on the screen, giving the student the opportunity to use it or learn about it.

3.9.1.2. procedural simulations: more directly linked to a particular goal, which involves learning about how the simulated machine works, as a means for acquiring the skills and actions needed to operate it.

3.9.1.3. situational simulations: deal with the attitudes and behaviours of people in different situations, rather than with skilled performance.

3.9.1.4. process simulations: learner activity is restricted to initial condition setting, after which the independent development of a process can be observed.

3.10. Component Design theory

3.10.1. two models of instruction:

3.10.1.1. tutorial: information is presented to the student

3.10.1.2. experiential: the learner may directly interact with the subject matter

3.11. Inquiry Teaching theory

3.11.1. instructional actions:

3.11.1.1. varying cases systematically: presenting specific situations to learners should occur in a systematic order

3.11.1.2. forming hypotheses

3.11.1.3. evaluating hypotheses

3.11.1.4. considering alternative predictions

3.11.1.5. entrapping students

3.11.1.6. tracing consequences to a contradiction

3.12. Elaboration theory

3.12.1. Proceed from simple ideas from a theory/procedure and then proceed by presenting more complex information.

3.12.2. In order to integrate knowledge the learner is offered …

3.12.2.1. summarizers: aimed at memorizing knowledge

3.12.2.2. synthesizers: aimed at deeper knowledge

3.12.2.3. analogies: relate new ideas to existing ideas

3.13. Reigeluth and Schwartz

3.13.1. types of learning goals:

3.13.1.1. procedural simulation: used to teach the learner to perform a sequence of steps and/or decisions

3.13.1.2. process simulation: teaches naturally occurring phenomena composed of a specific sequence of events

3.13.1.3. causal simulations: teaches the cause-effect relationship between two or more changes

3.13.2. stages in the instructional/learning process:

3.13.2.1. introduction phase

3.13.2.1.1. the learner is informed about the scenario of the simulation and about the goals

3.13.2.2. acquisition phase

3.13.2.2.1. knowledge is presented to the learner

3.13.2.2.2. The learner must acquire ...

3.13.2.3. application phase

3.13.2.3.1. The acquired knowledge is generalized to a larger set of situations.

3.13.2.3.2. Conceptual and operational knowledge is transformed from a declarative to a compiled form.

3.13.2.4. assessment phase

3.13.2.4.1. The knowledge of the learner is assessed according to some criterion.

3.14. Anderson

3.14.1. Anderson’s stages in the acquisition of a particular skill:

3.14.1.1. declarative stage: skill-independent productions use declarative representations relevant to the skill to guide behaviour

3.14.1.2. knowledge compilation stage: the system goes from this interpretive application of declarative knowledge to procedures (productions) that apply the knowledge directly.

3.14.1.3. tuning stage: the ‘compiled’ knowledge is refined and consolidated through further practice

3.15. Gredler

3.15.1. types of simulations:

3.15.1.1. experiential simulations

3.15.1.1.1. provide students with a psychological reality in which students play roles within that reality ...

3.15.1.1.2. types:

3.15.1.2. symbolic simulations

3.15.1.2.1. are dynamic in nature and represents the behaviour of a system, or phenomenon, on a set of interacting processes.

3.15.1.2.2. The students’ role in symbolic simulation is that of principal investigator.

3.16. Klahr and Dunbar

3.16.1. theory of scientific discovery as dual search (SDDS)

3.16.1.1. describes the scientific discovery process as a search process in an ...

3.16.1.1.1. hypothesis space, containing all possible hypotheses about studied system

3.16.1.1.2. experiment space, consisting of all experiments that can be carried out

3.16.1.2. types of learners:

3.16.1.2.1. experimenters: experiments are conducted to identify variables and generate hypotheses

3.16.1.2.2. theorists: searching hypothesis space during advanced knowledge acquisition

3.16.1.3. types of strategies for searching these spaces:

3.16.1.3.1. bottom-up (used by experimenters)

3.16.1.3.2. top-down (used by theorists)

3.17. Kolb

3.17.1. The Kolb Learning Cycle

3.17.1.1. Information can either be obtained through ...

3.17.1.1.1. abstract conceptualization: focuses on ...

3.17.1.1.2. or concrete experience: deals with specific encounters, particularly with people, and is fundamentally less systematic.

3.17.1.2. Information is transformed to knowledge by ...

3.17.1.2.1. active experimentation: learning is accomplished by ...

3.17.1.2.2. or reflective observation: involves ...

3.17.2. Research has shown that ...

3.17.2.1. 20% of knowledge is retained if only abstract conceptualization is used.

3.17.2.2. Knowledge retention climbs to 50% when reflective observation and abstract conceptualization are used,

3.17.2.3. 70% with concrete experience, reflective observation and abstract conceptualization

3.17.2.4. and 90% with all four modes.

4. computer simulation

4.1. computer simulation: a program that constrains a model of a system or a process.

4.2. Intelligent Tutoring System (ITS)

4.2.1. a computer simulation which is expanded by implementing teaching functions

4.2.2. types:

4.2.2.1. expert module

4.2.2.1.1. implementation of the rules about how to control the simulated system

4.2.2.2. diagnosis and student module

4.2.2.2.1. an on-line judgement of the learner’s behaviour

4.2.2.3. tutor module

4.2.2.3.1. adaptive tutorial rules which are the basis for linking specific learner behaviour and instructional interventions

4.3. In order to create an instructional computer simulation, one must ...

4.3.1. create a simulation model

4.3.2. create a learner interface to the simulation

4.3.3. create an instructional design of the environment

4.3.4. create instructional interventions

4.3.5. integrate the parts of the environment to a complete system

4.4. mismatch

4.4.1. On the one hand, most teachers lack expertise and time for creating these environments themselves.

4.4.2. On the other hand, off-the-shelf simulations often do not match the requirements of a specific teacher.

5. learning support

5.1. categories

5.1.1. providing background-knowledge

5.1.2. helping learners to make hypotheses

5.1.3. helping learners to conduct experiments

5.1.3.1. Learners often show inefficient behaviors when conducting experiments:

5.1.3.1.1. manipulating variables that are irrelevant to the hypotheses

5.1.3.1.2. being unable to utilize all the information that is relevant to the experiments

5.1.3.1.3. tending to design identical experiments

5.1.3.1.4. focusing on obtaining the required results rather than understanding the concept model

5.1.3.1.5. focusing on entertainment factors rather than on obtaining a deeper understanding of the concept model

5.1.4. helping learners to interpret data

5.1.5. helping learners to regulate the learning process

5.2. types

5.2.1. Interpretative support that helps learners with ...

5.2.1.1. knowledge access and activation

5.2.1.2. the generation of appropriate hypotheses

5.2.1.3. and the construction of coherent understandings.

5.2.2. Experimental support that scaffolds learners in ...

5.2.2.1. the systematic and logical design of scientific experiments

5.2.2.2. the prediction and observation of outcomes

5.2.2.3. and the drawing of reasonable conclusions.

5.2.3. Reflective support that ...

5.2.3.1. increases learners’ self-awareness of the learning processes

5.2.3.2. and prompts their reflective abstraction and integration of their discoveries.

5.3. ways in which guidance can be provided:

5.3.1. providing favourable conditions

5.3.2. stimulating learning processes

5.3.2.1. Socratic dialogue in order to have the learner revise his/her thoughts

5.3.2.2. systematic variation of cases

5.3.2.3. providing hints/suggestions on how to perform exploratory learning

5.3.2.4. giving goals or assignments with the simulations

5.3.2.5. fault diagnosis task

5.3.2.6. prodding techniques for encouraging the statement of hypotheses

5.3.2.7. take over processes from the learner in order to enable other processes

5.4. problem-solving strategies:

5.4.1. goal decomposition

5.4.1.1. by explicitly suggesting a goal

5.4.1.2. or by asking leading questions.

5.4.2. reminding the learner of previous solutions

5.4.3. simplification, can help learners who ...

5.4.3.1. get stuck at some point in a complex problem

5.4.3.2. or are intimidated by a complex problem.

5.5. assignments

5.5.1. Assignments can be introduced as a mechanism to help learners in their goal setting behavior.

5.5.2. types of assignments:

5.5.2.1. do-it assignments: give the learner the general assignment to explore the model

5.5.2.2. investigation assignments: ask learner to investigate the relation between two or more given variables

5.5.2.3. explicitation assignments: have an initial state or sets of initial states for the simulation associated with them, the role of the learner being to run the simulation and to observe the impact on the simulation

5.5.2.4. specification assignments: the learner has to predict the values of certain variables when the associated simulation stops

5.5.2.5. optimization assignments: the learner has to vary the simulation’s variables’ values so that the constraints specified by the author are not broken and a target specified by the author is reached.

5.6. heuristics

5.6.1. Providing students with heuristics ...

5.6.1.1. supports performing systematic experiments

5.6.1.2. encourages them to provide evidence for the conclusions they draw

5.6.1.3. can facilitate them in ...

5.6.1.3.1. generating meaning from data

5.6.1.3.2. and making connections among ...

5.7. scaffolding

5.7.1. scaffolding: the process by which assistance is provided that enables learners to succeed in problems that would otherwise be too difficult

5.7.2. The intention is that the support not only assists learners in accomplishing tasks, but also enables them to learn from the experience.

6. conceptual change

6.1. Alternative conceptions have the general characteristics of ...

6.1.1. being poorly articulated

6.1.2. being internally inconsistent

6.1.3. being highly dependent on context

6.1.4. and having tremendous explanatory power in the mind of the student.

6.2. Educational conditions that promote conceptual change:

6.2.1. The student must experience dissatisfaction with an existing conception.

6.2.2. The new conception must be intelligible.

6.2.3. The new conception must be plausible.

6.2.4. The new conception must be fruitful.

6.3. Children often resolve their misconceptions with the goal of ...

6.3.1. receiving a good grade

6.3.2. preserving self-esteem in an intellectually overwhelming situation

6.3.3. or bringing closure to a learning situation at any cost.

7. before/after instruction

7.1. before

7.1.1. Simulation provided before instruction may function as a conceptual model that allows students to better understand and encode the didactically presented information. Students receiving such a model may be better able to recall the presented didactic information and to reason with the principles taught in transfer situations.

7.1.2. instructional role: setting the stage for future learning; simulations can ...

7.1.2.1. provide motivation

7.1.2.2. reveal misconceptions that would inhibit learning

7.1.2.3. provide an organizing cognitive structure for receiving new material

7.1.2.4. and serve as concrete examples of complex, abstract concepts.

7.2. after

7.2.1. Students who receive simulation after didactic instruction may find it difficult to make sense from the model with which to assimilate the instructional information. That being the case, during didactic instruction, they may not be able to encode a given information into cognitive structure as well as students who had had prior simulations.

7.2.2. instructional role: providing an opportunity to apply or integrate newly acquired knowledge; they ...

7.2.2.1. are used to support the acquisition of diagnostic skills or processes

7.2.2.2. and can uncover misconceptions in newly acquired knowledge.

8. multiple representations

8.1. Combining different representations in one interface may have several advantages:

8.1.1. Each representation can show specific aspects of the domain to be learned.

8.1.1.1. Text and pictures are good representations to present the context of a problem.

8.1.1.2. Diagrams are well suited for presenting qualitative information.

8.1.1.3. Graphs, formulas, and numeric representations can be used to show quantitative information.

8.1.2. One representation can constrain the interpretation of another representation.

8.1.2.1. The purpose is not to provide new information, but to support the learners’ reasoning about the less familiar representation.

8.1.3. By translating between representations, learners build abstractions that may lead to a deeper understanding of the domain.

8.2. When learning with multiple representations, learners are faced with four tasks:

8.2.1. They have to understand the syntax of each representation.

8.2.2. They have to understand which parts of the domain are represented.

8.2.3. Learners have to relate the representations to each other if the representations (partially) present the same information.

8.2.3.1. relating: linking the surface features of different representations

8.2.4. Learners have to translate between the representations.

8.2.4.1. translating: having to interpret the similarities and differences of corresponding features of two or more representations

8.3. Problems learners may encounter when learning with multiple representations:

8.3.1. They have difficulties relating different representations.

8.3.1.1. split-attention problem: When learning with separate representations, learners are required to relate disparate sources of information, which may generate a heavy cognitive load that may leave less resources for actual learning.

8.3.2. Novices have problems in translating between representations.

8.4. Ways to make relations between representations explicit for the learner:

8.4.1. physically integrate the representations

8.4.1.1. Multiple representations, when integrated, appear to be one representation showing different domain aspects.

8.4.2. provide the learner with dynamic linking

8.4.2.1. Actions performed on one representation are automatically shown in all other representations. problems with dynamic linking:

8.4.2.1.1. Dynamic linking may discourage reflection on the nature of the translations, leading to a failure by the learner to construct the required understanding.

8.4.2.1.2. With multiple dynamically changing representations, learners need to attend to and relate changes that occur simultaneously in different regions of various representations, which may lead to cognitive overload.

9. scientific discovery learning

9.1. scientific discovery: the processes of mindful coordination between hypothesized theories and evidence collected by experiments

9.2. The content of a domain is not explicitly stated to learners.

9.3. Learners experiment and construct knowledge as ‘scientists’:

9.3.1. They provide the simulation with input.

9.3.2. observe the output

9.3.3. draw their conclusions

9.3.4. and go to the next experiment.

9.4. A technique that has proven useful in eliciting inquiry learning processes is by the use of computer simulations.

9.5. It leads to knowledge that ...

9.5.1. is more intuitive and deeply rooted in a learners’ knowledge base

9.5.1.1. Intuitive knowledge is ...

9.5.1.1.1. hard to verbalize

9.5.1.1.2. immediately available or not available at all

9.5.1.1.3. often relies heavily on visualizations

9.5.1.1.4. and acquired only after inferring knowledge in rich, dynamic situations.

9.5.2. has a more qualitative character.

9.6. approaches

9.6.1. a top-down method or concept-driven way of discovery

9.6.1.1. knowledge plays a central role

9.6.2. a bottom-up approach or data-driven way of discovery

9.6.2.1. features of the environment (e.g. a simulation interface) are of central importance

9.7. processes

9.7.1. transformative processes: the reasoning and decision-making that guide manipulating a computer simulation and extracting information from it:

9.7.1.1. orientation

9.7.1.2. hypothesis generation

9.7.1.2.1. identifying variables

9.7.1.2.2. selecting variables

9.7.1.2.3. and defining the relation that is hypothesized to hold between the selected variables.

9.7.1.3. hypothesis testing

9.7.1.3.1. hypothesis train: a set of consecutive hypotheses concerning one set of variables or related variables

9.7.1.4. and drawing conclusions.

9.7.2. regulative processes: meant to control the discovery learning process on a metacognitive level:

9.7.2.1. monitoring one’s own behavior

9.7.2.2. keeping track of progress

9.7.2.3. and planning in advance what steps to undertake.

9.8. influence on effectiveness

9.8.1. prior domain-specific knowledge

9.8.2. generic knowledge of quantitative and qualitative relations between variables

9.8.3. discovery skill: student’s aptitude at performing and interpreting experiments

9.8.4. and metacognition: the generic ability to regulate discovery learning processes

9.9. problems

9.9.1. for the process of hypothesis generation:

9.9.1.1. Choosing hypotheses that seem “safe” and unsuccessfully transforming data into a hypothesis.

9.9.1.2. The distance between the theoretical variables and the variables that are manipulated in the simulation.

9.9.2. for designing experiments: learners who ...

9.9.2.1. design inconclusive experiments

9.9.2.2. show inefficient experimentation behavior

9.9.2.3. follow a confirmation bias

9.9.2.4. apply an engineering approach instead of a scientific one.

9.9.3. Learners quite often have trouble with the interpretation of data as such.

9.9.4. Students are not very capable of regulating the learning process.

9.10. computer-mediated collaboration

9.10.1. transformative processes

9.10.1.1. learners’ activities in these phases are performed for the sole purpose of yielding knowledge

9.10.1.2. support:

9.10.1.2.1. hypothesis generation

9.10.1.2.2. experiment design

9.10.1.2.3. data interpretation

9.10.2. regulatory processes

9.10.2.1. serve to manage and control the inquiry learning process.

9.10.2.2. support:

9.10.2.2.1. planning

9.10.2.2.2. monitoring

9.10.2.2.3. evaluation

9.10.3. deficiencies

9.10.3.1. Face-to-face dyads attain higher performance scores than students who collaborate online.

9.10.3.2. Face-to-face dyads have more prolonged discussions on learning tasks.

9.10.3.3. Communication in computer-mediated dyads is more directed at ...

9.10.3.3.1. coordinating efforts

9.10.3.3.2. operating the communication tool

9.10.3.3.3. and expressing emotions.

10. epistomology

10.1. epistemic motivation: one’s beliefs toward knowledge and the process of building knowledge

10.2. Epistemologically less mature students ...

10.2.1. believe that knowledge is simple and certain

10.2.2. should find less compatible an approach that emphasized self-exploration and self-construction of knowledge

10.2.3. and achieve best in a more prescribed, confirmatory simulation environment.

10.3. Students with greater epistemological sophistication ...

10.3.1. do better in an exploratory simulation environment.

11. metacognition

11.1. metacognition: both the knowledge about one’s own cognitive processes and the control over these processes

11.2. Metacognition appears to be ...

11.2.1. one of the most important determinants for successful learning in general

11.2.2. and for successful inductive learning with computer simulations in particular.

11.3. metacognitive knowledge: knowledge about the interplay between ...

11.3.1. person characteristics

11.3.2. task characteristics

11.3.3. and the available strategies in a learning situation.

11.4. metacognitive skills: self-regulatory activities actually being performed by a learner in order to structure the problem solving process. operationalizations:

11.4.1. deep orientation

11.4.2. systematic orderliness

11.4.3. accuracy

11.4.4. evaluation

11.4.5. elaboration

12. instruction

12.1. instructional measures: either instructional strategies or actions

12.1.1. providing direct access to domain information

12.1.2. providing learners with assignments (or questions, exercises, or games)

12.1.3. model progression

12.2. instructional principles: common rules of thumb which can be implemented in particular features of the environment

12.3. instructional approach: the overall policy involved

12.3.1. learning by observation

12.3.2. coaching: the student is not guided through the domain in a structured way, but is monitored and corrected by a coach

12.3.3. learning by instruction: the student is guided through the domain in a structured way

12.4. instructional features: both the more passive features of the environment as well as the active measures taken by the instructional agent

13. learning goal

13.1. dimensions on which a particular learning goal can be classified:

13.1.1. kinds of knowledge to be learned:

13.1.1.1. conceptual knowledge: knowledge of principles, concepts and facts related to the (class of) system(s) being simulated

13.1.1.2. operational knowledge: knowledge about sequences of cognitive and/or noncognitive operations (procedures) that can be applied to the (class of) simulated system(s).

13.1.2. formats knowledge can be encoded in:

13.1.2.1. declarative knowledge: represented in a format ...

13.1.2.1.1. that is relatively easy to acquire

13.1.2.1.2. that makes the knowledge relatively easy to report upon

13.1.2.1.3. that makes the knowledge of potential use in an unlimited number of problem contexts

13.1.2.1.4. and that requires interpretation in order to use it in a task.

13.1.2.2. compiled knowledge: represented in a format ...

13.1.2.2.1. that is only obtained after using the knowledge in a problem-solving context

13.1.2.2.2. that makes the knowledge hard to report upon

13.1.2.2.3. that restricts its potential use to a limited number of contexts

13.1.2.2.4. and that can be used in a more automatic, effortless way.

13.1.3. scope of the target knowledge:

13.1.3.1. domain-specific knowledge: specific to the simulation domain at hand

13.1.3.2. generic knowledge: not specific to the simulation domain at hand, but extends to other domains as well.

14. cognitive load

14.1. distinction:

14.1.1. due to the difficulty of the subject material

14.1.2. induced by the instructional context in which the subject material is embedded

14.2. To maximize learning, instructors must minimize cognitive load by ...

14.2.1. limiting the amount of material presented

14.2.2. having a clear organizational structure to the presentation

14.2.3. linking new material to ideas that the audience already knows

14.2.4. and avoiding unfamiliar technical terminology and interesting little digressions.

15. locus of control

15.1. For learners who are anxious and less able and who report an external locus of control, system controlled instruction is more effective.

15.2. For more able, secure learners who report an internal locus of control, learner controlled instruction is more profitable.