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Educational Simulations

research of literature by Nico Rutten: www.linkedin.com/in/nicorutten

situating

implicit/explicit

information availability

articulation

Rieber, L. P. (1996). "Seriously Considering Play: Designing Interactive Learning Environments Based on the Blending of Microworlds, Simulations, and Games." Educational Technology Research and Development 44(2): 43-58.

Researchers have stressed the importance of anchoring, or "situating," learning in authentic situations (Brown, Collins & Duguid, 1989; Choi & Hannafin, 1995; Cognition and Technology Group at Vanderbilt, 1990). One benefit is that learners become engaged by the material, invoking a state of "mindfulness," in which learners employ nonautomatic, effortful, and metacognitively guided processes (Salomon, Perkins & Globerson, 1991). Learning in "mindful" ways results in knowledge that is considered meaningful and useful, as compared to the "inert" knowledge that results from decontextualized learning strategies (such as traditional classroom worksheets).

learning tools

functionalities

Berry and Broadbent (1987) found that providing information at the moment it is immediately needed by the learner is much more effective than providing all information needed before interaction with a simulation.

de Jong, T. and W. R. van Joolingen (1998). "Scientific Discovery Learning with Computer Simulations of Conceptual Domains." Review of Educational Research 68(2): 179-201.

Summarizing and assuming the generalizability of the present results to other computer-simulation games, if the goal of discovery learning is to promote the acquisition of game-exceeding verbal knowledge related to concepts, facts, rules and principles of the simulated domain of reality, then it is very useful to make pieces of information, which are implicitly available in the system, explicit through appropriate instructional support during system exploration.

why/what

Leutner, D. (1993). "Guided Discovery Learning with Computer-Based Simulation Games: Effects of Adaptive and Non-Adaptive Instructional Support." Learning and Instruction 3(2): 113-32.

Vreman-de Olde, G. C. and T. Jong de (2006). "Scaffolding learners in designing investigation assignments for a computer simulation." Journal of Computer Assisted Learning 22(1): 63-73.

If students succeed in articulating the knowledge presented in the assignment, this leads to a reflection process that has generally proven to be beneficial for learning in the context of inquiry learning (Land & Zembal-Saul 2003; Zhang et al. 2004; Moreno & Mayer 2005).

This advice is consistent with Fyle’s (2003) suggestions that to enhance conceptual change by using simulations we need to provide: (1) functionality that allow learners to compare their existing conceptions or misconceptions with the ideal or correct conceptions of a particular domain, (2) help resources that provide detailed explanatory support that helps learners understand particular concepts or sections within the domain being studied, (3) functionality that provides learners with the opportunity to have multiple explanatory representations of different aspects of the domain presented to them, and (4) provide functionality that provides adaptively to levels of expertise.

Blake, C. and E. Scanlon (2007). "Reconsidering Simulations in Science Education at a Distance: Features of Effective Use." Journal of Computer Assisted Learning 23(6): 491-502.

Jong, T. d., W. R. v. Joolingen, et al. (1998). "Self-directed learning in simulation-based discovery environments." Journal of Computer Assisted Learning 14(3): 235-246.

The learning philosophy focuses on alleviating learning problems by introducing cognitive support for the learner which consists of several ‘learning tools’. Learners may ask for small exercises (so-called assignments) that help them plan their actions and that can point them to specific phenomena; while experimenting learners can ask for background information in the form of definitions, relations to the real world etc. (this can be any kind of multi-media material); the simulation model can be presented to the learner in small steps that increase the model in complexity (so-called model progression); learners have tools that help them to monitor what they have been doing in a simulation session, that help them replay simulation sessions, compare outcome series, and make sound interpretations of the data; and, finally, also learners will have tools that help them to compose and check hypotheses.

Wieman, C. and K. Perkins (2005). "Transforming Physics Education." Physics Today 58(11): 36-41.

To move a student toward expert competence, the instructor must focus on the development of the student’s mental organizational structure by addressing the “why” and not just the “what” of the subject.

cognitive load

prediction

To maximize learning, instructors must minimize cognitive load by limiting the amount of material presented, having a clear organizational structure to the presentation, linking new material to ideas that the audience already knows, and avoiding unfamiliar technical terminology and interesting little digressions.

Wieman, C. and K. Perkins (2005). "Transforming Physics Education." Physics Today 58(11): 36-41.

retention

Windschitl, M. and T. Andre (1996). Using Computer Simulations To Enhance Conceptual Change: The Roles of Constructivist Instruction and Student Epistemological Beliefs.

Prediction and testing encourages “internal discourse” to take place in the mind of the student (Perkins & Simmons, 1988); only by stimulating hypothesis testing on the part of the learner can computer based instruction offer the possibility of conceptual conflict (Osborne & Squires, 1987).

Gokhale, A. A. (1996). "Effectiveness of Computer Simulation for Enhancing Higher Order Thinking." Journal of Industrial Teacher Education 33(4): 36-46.

Menn (1993) evaluated the impact of different instructional media on student retention of subject matter. It was found that students remember only 10% of what they read; 20% of what they hear; 30%, if they see visuals related to what they are hearing; 50%, if they watch someone do something while explaining it; but almost 90%, if they do the job themselves even if only as a simulation.

anticipation

mental translation

hypothesis menu

Asking students to anticipate the results of a simulation before interacting with it appears to be an effective instructional technique.

Lane, D. M., & Peres, S. C. (2006). Interactive Simulations in the Teaching of Statistics: Promise and Pitfalls. In A. Rossman and B. Chance (Eds.), Proceedings of the Seventh International Conference on Teaching Statistics. [CD-ROM]. Voorburg, The Netherlands: International Statistical Institute.

van der Meij, J. and T. de Jong (2006). "Supporting students' learning with multiple representations in a dynamic simulation-based learning environment." Learning and Instruction 16(3): 199-212.

Like, for example, Petre et al. (1998), we believe that having to make mental translations between representations is a good way to acquire deeper knowledge in a domain.

planning

Chang, K.-E., Y.-L. Chen, et al. (2008). "Effects of Learning Support in Simulation-Based Physics Learning." Computers & Education 51(4): 1486-1498.

The provision of a hypothesis menu allows learners to form their own hypotheses by choosing appropriate variables, correlations, and conditions (Shute & Glaser, 1990; van Joolingen & de Jong, 1991).

prompting

simulation

Planning support takes away decisions from learners and in this way helps them in managing the learning process.

de Jong, T. and W. R. van Joolingen (1998). "Scientific Discovery Learning with Computer Simulations of Conceptual Domains." Review of Educational Research 68(2): 179-201.

questions

Lin and Lehman’s (1999) study of different prompt types found that prompting learners to justify their reasoning directed learner’s attention more to understanding ‘‘when, why, and how to employ experiment design principles and strategies’’ (p. 837). This in turn enabled better transfer of their understanding to novel problems.

Joolingen van, W. R., T. Jong de, et al. (2005). "Co-Lab: research and development of an on-line learning environment for collaborative scientific discovery learning." Computers in Human Behaviour 21(4): 671-688.

teaching

Lane, D. M., & Peres, S. C. (2006). Interactive Simulations in the Teaching of Statistics: Promise and Pitfalls. In A. Rossman and B. Chance (Eds.), Proceedings of the Seventh International Conference on Teaching Statistics. [CD-ROM]. Voorburg, The Netherlands: International Statistical Institute.

One method of structuring shown to be effective is asking students to use a simulation to ascertain answers to specific questions posed beforehand (de Jong, Hartel, Swaak, and van Joolingen 1996).

monitoring

dynamic linking

model progression

integration of instruction

assignments

de Jong, T. and W. R. van Joolingen (1998). "Scientific Discovery Learning with Computer Simulations of Conceptual Domains." Review of Educational Research 68(2): 179-201.

Support for monitoring one’s own discovery process can be given by overviews of what has been done in the simulation environment.

de Jong, T. and W. R. van Joolingen (1998). "Scientific Discovery Learning with Computer Simulations of Conceptual Domains." Review of Educational Research 68(2): 179-201.

The basic idea behind model progression is that presenting the learner with the full complexity of the simulation at once may be too overwhelming. In model progression the model is introduced gradually, step by step.

•van der Meij, J. and T. de Jong (2006). "Supporting students' learning with multiple representations in a dynamic simulation-based learning environment." Learning and Instruction 16(3): 199-212.

Dynamic linking may discourage reflection on the nature of the translations, leading to a failure by the learner to construct the required understanding (p. 133). Another problem with dynamic linking might be that 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 (see Lowe, 1999).

van der Meij, J. and T. de Jong (2006). "Supporting students' learning with multiple representations in a dynamic simulation-based learning environment." Learning and Instruction 16(3): 199-212.

This is in accordance with Chandler and Sweller (1991), who found that integrating instruction led to better learning results than separate instruction, as long as the materials chosen were unintelligible without mental integration.

Vreman-de Olde, G. C. and T. Jong de (2006). "Scaffolding learners in designing investigation assignments for a computer simulation." Journal of Computer Assisted Learning 22(1): 63-73.

Overall, providing students with assignments together with a simulation has a positive influence on learning outcomes (De Jong & Van Joolingen 1998).

other support

providing evidence

games

fun elements

sequence freedom

All these features are mentioned by de Jong and van Joolingen (1998) when they discuss how simulations may be combined with instructional support to overcome difficulties that learners may encounter: direct access to domain knowledge, support for hypothesis generation, support for the design of experiments, support for making predictions and support for regulative learning processes.

Blake, C. and E. Scanlon (2007). "Reconsidering Simulations in Science Education at a Distance: Features of Effective Use." Journal of Computer Assisted Learning 23(6): 491-502.

Providing students with heuristics supports them in performing systematic experiments (Veermans 2003; Zhang et al. 2004). Encouraging students to provide evidence for the conclusions they draw (Keys et al. 1999) can facilitate students in generating meaning from data and making connections among procedures, data, evidence, and claims.

Vreman-de Olde, G. C. and T. Jong de (2006). "Scaffolding learners in designing investigation assignments for a computer simulation." Journal of Computer Assisted Learning 22(1): 63-73.

Tabak et al. (1996) have added such questions with the aim of setting goals in a biological simulation. White (1984; 1993) helped learners to set goals in a simulation by introducing games that ask learners to reach a specific state of the simulation. In SIMQUEST learning environments the mechanism of assignments has been used to help learners in their goal setting behaviour.

Jong, T. d., W. R. v. Joolingen, et al. (1998). "Self-directed learning in simulation-based discovery environments." Journal of Computer Assisted Learning 14(3): 235-246.

The first main finding of this study is that giving subjects freedom in choosing their own sequence through the environment or forcing them to complete all assignments at one level of model progression before proceeding to another level appears not to make much difference.

Swaak, D. J. and P. d. T. Jong de (2001). "Learner vs. System Control in Using Online Support for Simulation-based Discovery Learning." Learning environments research 4(3): 217-241.

Learning that is fun appears to be more effective (Lepper and Cordova, 1992). Also, Quinn (1994, 1997) argues that for games to benefit educational practice and learning they need to combine fun elements with aspects of instructional design and system design that include motivational, learning and interactive components. According to Malone (1981a, b) three elements (fantasy, curiosity and challenge) contribute to the fun in games.

Amory, A., K. Naicker, et al. (1999). "The Use of Computer Games as an Educational Tool: Identification of Appropriate Game Types and Game Elements." British Journal of Educational Technology 30(4): 311-21.

heuristics

variables that can have an impact on the learning-effect of computer simulations in an educational setting

animation/text

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before/after

Heuristics can vary in the range of situations in which they can be applied, they can depend more or less on the domain, and can also be more or less general.

Veermans, K., W. van Joolingen, et al. (2006). "Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain." International Journal of Science Education 28(4): 341-361.

consultation freedom

Lane, D. M., & Peres, S. C. (2006). Interactive Simulations in the Teaching of Statistics: Promise and Pitfalls. In A. Rossman and B. Chance (Eds.), Proceedings of the Seventh International Conference on Teaching Statistics. [CD-ROM]. Voorburg, The Netherlands: International Statistical Institute.

Several studies have found that using animation to demonstrate how to operate computer software allows the user to adopt a passive learning style which results in good immediate performance but poor performance a week later (Atlas, Cornett, Lane, and Napier, 1996; Palmiter and Elkerton, 1991). Instructions presented in text are more difficult and lead to poorer immediate performance but better performance later.

Leutner, D. (1993). "Guided Discovery Learning with Computer-Based Simulation Games: Effects of Adaptive and Non-Adaptive Instructional Support." Learning and Instruction 3(2): 113-32.

Background information on system variables seems to be especially useful when the learner, after reading it the first time, can at any time refer to it and use it to solve specific problem situations. Contrary to that, adaptive advice and pretutorial information demand less personal initiative because they are automatically supplied by the system and the student cannot consult the information exactly when required for solving a certain problem situation.

According to Orbach [8] and Thomas and Boysen [9], simulations can fulfil two important instructional roles: 1) setting the stage for future learning and 2) providing an opportunity to apply or integrate newly acquired knowledge. As a stage setting activity, a simulation is used prior to formal instruction; wheras, as an application or integration activity, it is used after the instruction has been given.

Brant, G. and et al. (1991). "Which Comes First the Simulation or the Lecture?" Journal of Educational Computing Research 7(4): 469-81.

implicit/explicit

format

In instruction, heuristics can be made explicit to the learner or kept implicit.

Veermans, K., W. van Joolingen, et al. (2006). "Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain." International Journal of Science Education 28(4): 341-361.

The hypotheses list aims, like the what-if tests, at the organisational level of relations between specified variables. The level of detail of the two tests can be considered identical. However, the two measures contrast with respect to the demand they place on the verbal skills of the learners, on the explicitness required, and on the format used (selected response vs. constructed response, computer-administered vs. paper & pencil).

Swaak, J. and T. de Jong (2001). "Discovery Simulations and the Assessment of Intuitive Knowledge." Journal of Computer Assisted Learning 17(3): 284-94.

scaffolding

four factors

locus of control

collaboration

Chang, K.-E., Y.-L. Chen, et al. (2008). "Effects of Learning Support in Simulation-Based Physics Learning." Computers & Education 51(4): 1486-1498.

Therefore, even though the use of learning support restricts the explorations performed by learners, the scaffolding it provides improves their performance in simulation-based learning. However, if the provided learning support completely prevents free explorations, converting them into step-by-step experiments, the effectiveness of learning support would be reduced.

Hulshof, C. D. and T. de Jong (2006). "Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation." Interactive Learning Environments 14(1): 79-94.

At least four factors have been shown to influence the effectiveness of scientific discovery learning: prior domain-specific knowledge, generic knowledge of quantitative and qualitative relations between variables, discovery skill, and metacognition (De Jong et al., 2005).

Joolingen van, W. R., T. Jong de, et al. (2005). "Co-Lab: research and development of an on-line learning environment for collaborative scientific discovery learning." Computers in Human Behaviour 21(4): 671-688.

Collaboration increases the likelihood that learners engage in the type of talk that supports learning, such as asking and answering of questions, reasoning and conflict resolution.

The ‘locus of control’ issue (concerning whether it should be the learner who is in control, or the system) has been a main topic in the development of intelligent tutoring systems (ITSs). [...] A general finding has been that, for learners who are anxious and less able and who report an external locus of control, system controlled instruction is more effective. For more able, secure learners who report an internal locus of control, learner controlled instruction is more profitable (e.g. Lohman, 1986).

Swaak, D. J. and P. d. T. Jong de (2001). "Learner vs. System Control in Using Online Support for Simulation-based Discovery Learning." Learning environments research 4(3): 217-241.

abstract reasoning abilities

metacognitive skillfulness

It was also found in our study that different learning models do not have different effects on learners with different abstract reasoning abilities, and that simulation-based learning is more beneficial to learners with higher abstract reasoning abilities.

Chang, K.-E., Y.-L. Chen, et al. (2008). "Effects of Learning Support in Simulation-Based Physics Learning." Computers & Education 51(4): 1486-1498.

Veenman, M. V. J., F. J. Prins, et al. (2002). "Initial inductive learning in a complex computer simulated environment: the role of metacognitive skills and intellectual ability." Computers in Human Behavior 18: 327-341.

This study shows that during initial inductive learning with a complex computer simulation learners draw heavily on their metacognitive skillfulness, which results mainly in qualitative knowledge. Consequently, complex computer-simulated learning environments are only appropriate for novice learners with high metacognitive skillfulness.

student

intellectual ability

epistemological sophistication

Results of a series of experiments of Veenman (Veenman, 1993; Veenman & Elshout, 1995, 1999; Veenman et al., 1997) showed that, besides metacognitive skillfulness, intellectual ability is an important determinant of successful inductive learning using computer simulations.

Veenman, M. V. J., F. J. Prins, et al. (2002). "Initial inductive learning in a complex computer simulated environment: the role of metacognitive skills and intellectual ability." Computers in Human Behavior 18: 327-341.

Windschitl, M. and T. Andre (1996). Using Computer Simulations To Enhance Conceptual Change: The Roles of Constructivist Instruction and Student Epistemological Beliefs.

Students with greater epistemological sophistication did better in the exploratory simulation environment, while students with less sophisticated beliefs about knowledge and learning achieved best in the more prescribed, confirmatory simulation environment.

other differences

Akpan, J. P. (2001). "Issues Associated with Inserting Computer Simulations into Biology Instruction: A Review of the Literature." Electronic Journal of Science Education 5(3).

Do simulations work equally effectively for the different genders, for students with different personality, metacognitive or cognitive style characteristics, for students from minority groups, or for students from different cultures?

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