Learning in vivo

Simulation-based learning defined

Learning through simulation may occur in one of two or both ways, namely (a) observation—the simulation serves as illustration of the content to be learned or understood; and (b) direct interaction— learning induced through experience.

Simulation-based learning comprises two parts, namely learning and simulation—the term suggests learning through the use of simulation/s.

• With a simulation one process is imitated by another process (Hartmann, 1996).
• Simulations often focus on the structural aspects of a model rather than the dynamic characteristics, making them relatively static (Hughes, 1999; Grüne-Yanoff & Weirich 2010)
• A simulation is a manifestation of a model, providing a description through a set of intertwined elements (Grüne-Yanoff & Weirich 2010)
• Frasson and Blanchard (2012) concludes after citing the above that simulations entail the action of imitating a model’s dynamics and/or structure with its resulting element; including either static and/or dynamic features.

Frasson and Blanchard (2012) report several features discussed in literature about simulation-based learning opportunities and their classification:

• Realisation—three main groupings of models are differentiated, namely those (a) involving humans; (b) mechanical; and (c) computer enabled.
• Fidelity—the extent to which the model correctly represents—on a continuum from low to high. High fidelity represents more coherently, but do not always provide the best pedagogical opportunities. Low fidelity focus mainly on pedagogically relevant aspects (with fantasy elements possibly added) and as result sometimes more adequate.
• Interactivity—refers to the degree of passive observation versus acting and influence the evolution.
• Immersiveness—is the degree of enabling fidelity and interactivity resulting in accurate real-situational feelings.
• Intelligence—intelligent components often enhances user interaction and allow for various constructive alternatives.

Frasson, C. & Blanchard, E.G. 2012. Simulation-Based Learning. In Seel, N.M. (Ed.) Encyclopedia of the Sciences of Learning. Springer Science+Business Media.

WIL “is not as intuitive as it may seem”

The 12 points below originates from a summary and explanation by Annie Murphy Paul about a recent report on the science of training. She points out that there are right ways and wrong ways (myths) regarding how to design, deliver, and implement training. I adapted the 12 points to work-integrated learning (WIL):

1. It’s what happens before and after WIL that matters. Universities should clearly communicate to students what the WIL is about and how it relates to curriculum of the qualification and the day one competencies expected from graduates.
2. The WIL guidelines should clearly specify what a student need-to-be master versus learning where and how to find information rather than be expected to retain all information in memory.
3. WIL would be more effective when it’s presented as an opportunity of which the benefits to students are emphasised.
4. Decay of skills acquired during WIL may be reduced by giving students subsequent opportunities to practice the skills and/or refresher orientation upon employment.
5. Workplace mentors could increase the motivation of students by making clear the link between what is being taught during WIL and how it will be used in the careers of students.
6. The best WIL occurs through the practice in the workplace and feedback by workplace mentors — the effectiveness of WIL can be increase by making the process more active and engaging for students.
7. Mentors should be made aware that unstructured practice by students without clear objectives; without relevant stimulation; and without appropriate feedback can teach students wrong lessons. Students “will get the most out of practice when they are provided with constructive and timely feedback that identifies what they may be doing wrong and how to fix it”.
8. Superficial exposure during WIL leads to poor retention and poor transfer; whereas deep learning promotes retention and transfer.
9. There is value—both strategically and emotionally—in teaching students to cope with errors made. Workplace mentors should guide students to correct mistakes made which will help students to understand the task in greater depth and will help them deal with errors on the job.
10. Technology and contemporary gadgetry would not necessary improve WIL. However, if implemented appropriately it may add to the effectiveness of WIL.
11. Left to their own devices, students “may not be knowledgeable or motivated enough to make wise decisions about how and what to learn”, sound guidance is important.
12. The “psychological fidelity—how accurately it evokes the feelings and the responses the worker will have on the job” of simulations is more important than physical fidelity a student will encounter as worker on the job.

Paul, A.M. 2012. Learning on the job: myth vs science (How do we really learn to do our jobs?). A post published 2 July 2012 on Wray Herbert's blog We're only Human.

Salas, E.; Tannenbaum, S.I.; Kraiger, K. & Smith-Jentsch, K.A. 2012. The Science of Training and Development in Organizations: What Matters in Practice. Psychological Science in the Public Interest, 13(2) 74–101.

Simulations may achieve the positive impacts and learner motivation of real-world learning

Contextual dichotomy has been found to negatively impact and adversely affect learning. Situated in real-world contexts, on the other hand, has been found to positively impact learning and learner motivation reports Lunce (2006: 37). Although the article referenced below reports on research about simulations in classroom contexts, it contains important pointers that may apply to distance education.

Educational simulations are based on internal model of a real-world systems or phenomena. In order to facilitate learning certain elements, however, may have been simplified or omitted. Lunce (2006: 37) identifies three general types of models on which educational simulations are built:

• Continuous models which are constructed using calculus in order to represent systems with infinite numbers of states.
• Discrete models employing statistics and queuing theory to represent systems with quantitatively discrete states.
• Logical models making use of sets of heuristics, implemented through a high-level computer programming language.

The latter (logical models) are most often used in educational simulations; whereas continuous and discrete models are most often found in scientific and engineering simulations, says Lunce (2006: 37) who groups (p. 38) educational simulations into four categories:

• Physical simulations allow the learner to manipulate variables in an open-ended scenario and observe the results, for example weather patterns in which the student can manipulate certain parameters and observe the outcome.
• Iterative simulations allow for focussing on discovery learning not readily observable in real-time, for example, phenomena from biology, geology or economics; by providing the student with opportunities to conduct scientific research, build and test hypothesis and observe the results.
• Procedural simulations give students the opportunity to manipulate simulated objects with the goal of mastering the skills required to correctly and accurately manipulate physical objects in a real-world setting with the goal of preparing the student for working in a real-world setting.
• Situational simulations model for example human behaviour as a vehicle to allow students to explore different options and decision paths. Situational simulations often employ role playing and are usually designed to be run several times with each participant playing different roles in various iterations. Situational simulations tend to be the most difficult to design due to the open-ended design and complexity of modelling human behaviour and attitudes of individuals or groups in different settings.

The notion fidelity of simulation refers to the extent of accuracy with which the simulation models the real-world system or phenomena. It further refers to the realism of learner interaction facilitated by the simulation as well as the type and frequency of feedback provided. “A well designed simulation can maintain a high degree of fidelity while abstracting or omitting distracting elements that would otherwise be present in a real-world situation”, indicates Lunce (2006: 38).

Lunce L. M. 2006. Simulations: Bringing the benefits of situated learning to the traditional classroom. Journal of Applied Educational Technology, 3(1), 37-45, Spring/Summer, 2006. Accessed via Internet on 18 May 2012 from: http://www.eduquery.com/jaet/JAET3-1_Lunce.pdf.

Replacing natural experiments by computer simulations

Simulations as viable alternative to doing experiments on natural systems, offer anchor points to integrate scientific discovery learning or inquiry learning with instructional support in educational settings (for example distance) where for a number of reasons actual experiments may not be feasible, because (Van Joolingen & De Jong, 2003: 1):

• the natural system may not be accessible due to expense of the measuring equipment required or non-availability
• of safety hazards, such as radioactive samples, toxical chemical substances or populations of certain bacteria
• performing the experiments may be unethical, for example in the case of experiments with livestock
• time scale of experiments on natural phenomena may not fit with the educational timeframe— some experiments take days, months or even years to complete or may happen so fast that it is hard to visualize the processes the learning is about
• experiments may consume rare resources

“Simulations are computer programs containing an executable model of a natural or other system, capable of computing the behavior of the modeled system by means of dedicated algorithms and presenting the results of these computations to the user” (Van Joolingen & De Jong, 2003:2).

However, caution Van Joolingen and De Jong (2003: 3-4) the creation of computer simulations embedded in instruction is out of reach of most lecturers; and off-the-shelf simulations often do not match the specific teaching requirements. Creating simulations require specific skills for the various steps in the process:

1. Creating the model that would drive the simulation—Capability to do this requires expertise of either a programming language or a special simulation language; expertise in the domain simulated and knowledge of the target language; and in the case of a general purpose programming language one must also create the algorithm to simulate the model.
2. Creating the learner interface to the simulation—Including visual representation, using graphics representing domain elements, graphs, numbers, etc. Capability to create interfaces requires expertise of the available tools as well as to create the domain specific graphics within the interface.
3. Creating the instructional design of the environment from the perspective of the learner— Instructional design expertise, such as which activities to be performed; the goals of such activities; what information/explanations would be needed at specific moments (discipline knowledge); knowing/awareness of frequently occurring misconceptions; and which instructional interventions should form part of the simulation?
4. Integrate the parts of the simulation environment to a complete system in order to create a coherent integrated simulation— This means that all parts need to co-operate and that the interactions between simulation model, learner interface and all instructional interventions should be smooth and stable. This step requires programming skills in order to create an efficient and transparent environment.

The remainder of the referenced article discusses SimQuest—a simulation authoring system developed as free and open software by University of Twente for the designing and creating of simulation-based learning environments for discovery learning—downloadable for free.

Van Joolingen, W.R., & De Jong, T. (2003). SimQuest, authoring educational simulations. In: T. Murray, S. Blessing, S. Ainsworth. Authoring Tools for Advanced Technology Learning Environments: Toward cost-effective adaptive, interactive, and intelligent educational software. pp. 1-31. Dordrecht: Kluwer. Accessed from Internet 18 May 2012 from: http://halshs.archives-ouvertes.fr/docs/00/19/06/76/PDF/SimQuest-VanJoolingenDeJong.pdf

Simulations in context

Simulations have the power to challenge and alter the beliefs, as well as to promote flexibility with regard to decision making and management styles, argue Wolfe and Keys (1997). They emphasise the value of sensitivity to cultural diversity and appreciation for different values and perspectives, particularly in foreign global situations. “Unlike games, where everyone has a role of player with a duty to try to win, simulations require ‘professional behaviour, including professional ethics”, assert Jones (1995: 7). Simulations in group-settings offer experiential and active learning opportunities that are reasonably realistic and psychologically safe learning situations in that in these contexts psychological assumptions can be challenged; perspectives broadened; and critical reflection simulated (Wolfe & Keys, 1997).

A simulation should not attempt to reproduce reality, says Jones (1995: 9-10). The more an attempt is made to reproduce the real world with all the intricate complexities, he cautions, the more unworkable the simulations becomes. He puts forward four essential characteristics, namely:

• Simulation participants each have functional roles to fulfil, including the authority
• Given adequate information about the setting, problem or issue is essential — participants should not be required to invent the key facts (however improvisations and using imagination is encouraged); if participants are required, it is not a well designed simulation
• The lecturer does not play an active part in the carrying out the required action — a simulation is “a NON-TAUGHT event” [emphasis added], during which mistakes are “inevitable and desirable” — best characterised by “what actually goes on in the minds of the participants” (p. 12)
• Simulations are concluded with debriefing and reflection in order to appraise what happened and consolidate the learning

Jones (1995: 14) emphasises a clear differentiation of terminology and caution that wrong word-choices could cause simulations to go terribly wrong, because (wrong) words lead to (wrong) expectations which in turn lead to (inappropriate) behaviour:

Appropriate	Inappropriate
Simulation, activity, event Participant Facilitator (or organiser) Behaviour, function, profession Role (functional) Real-world responsible behaviour Real-world ethics Professional conduct	Game drama, role play, exercise Player, actor, puzzler, trainee, student [Lecturer, tutor], teacher, trainer, instructor Player, acting, staging, puzzling Role (acting a part) Winning (losing) the game Point scoring, having fun Performing the exercise (game, etc)

Jones, K. 1995. Simulations, a handbook for teachers and trainers. 3^rd edition. London: Kogan Page.

Wolfe, J & Keys, JB (eds). 1997. Business simulations, games and experiential learning in international business education. New York: International Business Press.

Simulations for Education; Virtual Worlds and Games

In seeking literature about simulations I find many mentioning games in the same breath, as well as simulation-games. Clark Aldrich (2009) observes that many practitioners have been struck by the paradox of sensing an overlap between virtual worlds, games, and simulations; yet knowing that they are not synonymous. He offers a succinct depiction:

The three often look similar, yet the differences are profound says Aldrich (2009):

• Games are fun, engaging activities usually used purely for entertainment, but they may also allow people to gain exposure to a particular set of tools, motions, or ideas.
• Simulations use rigorously structured scenarios carefully designed to develop specific competencies that can be directly transferred into the real world.
• Virtual worlds are multiplayer (and often massively multiplayer), three-dimensional, persistent social environments with easy-to-access building capabilities. They share with games and simulations the three-dimensional environment, but they do not have the focus on a particular goal, such as advancing to the next level or successfully navigating the scenario.

Aldrich, C. 2009. Virtual Worlds, Simulations, and Games for Education: A Unifying View. Innovate—a former official journal of online education of the Nova Southeastern University's Fischler School of Education and Human Services (FSEHS), Volume 5, Issue 5 (penultimate), June/July 2009. Retrieved electronically 23 October 2011 from: http://innovateonline.info/pdf/vol5_issue5/Virtual_Worlds,_Simulations,_and_Games_for_Education-__A_Unifying_View.pdf