Learning in vivo

Role of mental models in simulations

The Latin of simulation (simulō) means to imitate/pretend, which may have a negative connotation in that simulations might be regarded as false/unreal representations. Plato’s conception of mimesis (μίμησις, Greek term for simulation) had been an imperfect copy or fictitious replica of reality. In contrast, Aristotle notion of mimesis had been a “means to know nature through representations which can be valid and acceptable” (Landriscina, 2012). This, Landriscina points out, is an important shift in emphasis “from imitation to representation” where mental models are key to simulations.

In model-based instruction the underlying model assume a significant function, namely the identification of similarities and differences between the model and the phenomenon it represents. The process results in transforming ideas; confuse and approximate; resulting in more precise and rigorous comprehension. This, Landriscina says, also “happens when a student compares his own mental model of a system with that of a simulation”. He illustrates this with Plato’s example from Politics (Statesman) of cognitive role of the models, or paradeigmata (παραδείγματι):

one of the characters of the dialogue refers to the model of the weaver, where the technique of the politician is compared to that of the weaver who weaves fibers of different nature to create a single fabric

Senge (1990, cited by Landriscina) describes mental models as “deeply ingrained assumptions, generalizations, or even pictures and images that influence how we understand the world and how we take action”. Seel (2003, cited by Landriscina) examined the relation between mental model and education and formulated a teaching and learning theory: Model-Centered Learning and Instruction. Seel differentiated mental-; conceptual-; and design and instructional models:

• Mental model, the internal and subjective model of a system
• Conceptual model, which is objective and shared by a scientific community
• Design and instructional model, used for building the user interface and the learning tasks

To serve as simulation model, the conceptual model must be stated in mathematical terms (rules and equations) in order to be developed as computer programmes. In order to create simulated learning; a mental model of the phenomenon must be formed, studied and use as a basis for prediction, inference, and explanation. Landriscina (2012) cautions by means of Senge’s (1991) words that “simulations with thousands of variables and complex arrays of details can actually distract us [students] from seeing patterns and major interrelationships” and result in misconceptions by students. However, with “black-box simulation models”, for example SimCity® the student interacts with the various scenarios and applications without observing the rules of the game the creators programmed.

Landriscina, F. 2012. Simulation and Learning: The Role of Mental Models. In Seel, N.M. (Ed.) Encyclopedia of the Sciences of Learning. Springer Science + Business Media.

Synchronous online laboratory learning

Active laboratory experiment-interaction and problem-solving are considered essential parts of science and engineering education, because they enable direct acquisition of applicable knowledge for use in practical situations. Synchronous experimentation offers immediate feedback, rather than delayed. “Online (Remote) laboratories” report Auer, Edwards and Zutin (2011: 222) “available via internet” are therefore gaining importance. They define Online Engineering “as an interdisciplinary field utilising the areas of engineering, computing and telematics, where specific engineering activities like programming, design, control, observation, measuring, sensing, and maintenance are provided to both remote and local users in a live interactive setting over a distributed, physically dispersed network (for example: internet or intranet)”. They add that “high bandwidth internet connections world-wide and other derivative capabilities in the areas of real-time communication, control, teleconferencing, video streaming and others have made multi-site collaborative work, utilising state-of-the art equipment in remote laboratories across the globe a current reality”.

According to Auer, Edwards and Zutin (2011: 226) there are already a number of remote laboratories in operation and that the range of disciplines being catered for is growing as universities collaborate internationally. The portability and advancement of mobile technology further serve as powerful motivator for growth of online laboratory experiences.

Auer, M.E.; Edwards, A. & Zutin, D.G. 2011. Online laboratories in interactive learning environments, pp. 219-248. In N. Pachler; C. Pimmer & J. Seipold (eds). 2011. Work-Based Mobile Learning— Concepts and Cases. Oxford: Peter Lang.

Are computer games constructive tools to foster learning?

Konstantin Mitgutsch (2012: 572) shares “insights into the experience-based process of learning and its recursive motion, structures, and patterns”—“the recursive process of experiencing unknown situations and of restructuring former experiences”. He starts (p. 571) by asking the blog title question and by indicating it “reaches back to the roots of the institutionalization of education in general”. He continues to indicate that “ludologists, educators, psychologists, anthropologists, and philosophers” in the 18^th and 19^th centuries recognised and promoted the potential of playing. He adds that “players achieve an enormous amount of information through playing digital games”.

Digital games have been capturing academic interest since the early 1980s. Initially the dominant focus of research had been on risks and environmental effects. In contrast the focus of contemporary research shifted to the “positive and beneficial aspects of playing”—“A frequently applied term used to describe learning in games is digital game-based learning (DGBL)” (Mitgutsch, 2012: 572-3). He quotes the Federation of American Scientists (2006: 3) believes that “computer games are a constructive tool to foster learning”:

The success of complex video games demonstrates games can teach higher-order thinking skills such as strategic thinking, interpretative analysis, problem solving, plan formulation and execution, and adaptation to rapid change.

Although the PC-games industry develop and sell games that foster learning, it must be kept in mind that the focus is on profits and market preferences, rather than on an educational rationale. Mitgutsch (2012: 574-5) summarises literature that focus on reasons why computer games engage learners as well as several aspects that support the learning process.

Learning “is often reduced to an act of acquiring” while it is often overlooked “that every learning process starts with a particular experience of something”—people cannot necessary “choose to make a particular experience like” ordering from a catalogue; instead experience happens (Mitgutsch, 2012: 575). John Dewey in 1916 pointed out that learning from experience entails making backward and forward connections between what is done/tried and what is gained as result—it is not a linear act of acquiring; but rather “a circular process of suffering, doing, experimenting, transferring, and playing”. Games offer exploratory territories and therefore serve as powerful learning enablers.

Mitgutsch, K. 2012. Learning through play – a delicate matter: experience-based recursive learning in computer games, Chapter 36, pp. 571 - 584. [Retrieved 25 June 2012 from: http://www.springerlink.com/content/h2534141vvm55m32/fulltext.pdf] in J. Fromme & A. Unger (eds). 2012. Computer Games and New Media Cultures—A Handbook of Digital Games Studies. Dordrecht: Springer Science and Business Media. Retrieved 25 June 2012 from: http://www.springerlink.com/content/978-94-007-2776-2/#section=1083651&page=1

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