Learning in vivo

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

Instructional design for simulation

Literally, instructional design means “a set of events that facilitate learning” (instructional) and “a creative model” (design)—it includes a number of systematic processes to plan and create situations and/or events that would enhance the learning of individuals. Underlying instructional design are learning theory paradigms, based on various schools of thought, for example behaviourist—representing those believing that learning results in changed behaviour; cognitive—the proponents who believe adding of new concepts and ideas result in learning; and constructivist—those that believe that “learners construct knowledge for themselves” (Sidhu, 2010: 12-14).

There are hundreds of instructional design models. However, Reigeluth (1999, cited by Sidhu, 2010: 14) indicates that four major characteristics are common to instructional design, namely:

• “Design orientation,
• Identification of methods of instruction and situations,
• Methods of instruction that can be broken into more detail components methods, and
• Choice of Probalistic Methods”

Design theory is important because it renders “a vision of instruction early in the design process”—an ends of “how learners would be different as result of” the learning. The ultimate user plays a significant role, advocates Banathy (1991, cited by Sidhu, 2010: 14) who calls for a “much greater use of the notion of user-design”. Theory further provides guidance to instructional design at three levels according to Reigeluth (1999, cited by Sidhu, 2010: 14 and emphasis added):

• “Methods that best facilitate learning under different situations,
• Learning tool features that best allow an array of alternative methods to be made available to learners, [and]
• Systems features that best allow an instructional design team to design quality learning tools.”

Sidhu (2010: 14-15) prefers the ADDIE Model of design: Analysis, Design, Development, Implementation and Evaluation.

Sidhu, M.S. 2010. Technology-assisted problem solving for engineering education: interactive multimedia applications. Hershey: Engineering Science Reference.

Virtual Reality—visualisation and interactivity enable simulation

The delivery of educational experiences by means of computer started in the 1950s. The use of computer aided learning (CAL) gained popularity with the growth in use of personal computers (PCs). In the 1980s a paradigm shift from text to CAL commenced. Rapid developments of user-friendly technology enable integration of multimedia, interactivity with virtual realities and simulations. The term virtual reality (VR), says Sidhu (2010: 2), describes “a range of computer-based systems in which a user can explore hardware or software generated ‘micro world’ (artificial environments) that allow close resemblance to reality.” The modelling of objects and interactive behaviours within virtual environments is feasible. As is “integrating position-tracking human-computer interaction devices, and performing numerically intensive computations for real-time navigation.”

Sidhu, M.S. 2010. Technology-assisted problem solving for engineering education: interactive multimedia applications. Hershey: Engineering Science Reference.