Learning in vivo

Enabling distant laboratory learning via mobile technologies

“Online laboratories are necessary for distance or mobile learning for the same reason laboratory practices are important in traditional educational settings” reports Auer, Edwards and Zutin (2011: 229). They observe that although there appear to be general agreement that laboratory experience is necessary; there is little consensus about what the basic objectives thereof are. They further report (p. 223) that during the four years 1993 to 1997 only 6.5 per cent of the articles published in the Journal of Engineering Education used laboratory as keyword; and during the subsequent four years 1998 to 2002 even less, namely 5.2 per cent. Little has been published about what laboratory experience is expected to accomplish, with no specific objectives or outcomes stated.

Auer, Edwards and Zutin (2011: 222) assert that “synchronous active interaction with experiments and problem-solving helps individual or collaborative learners directly acquire applicable knowledge that can be used in practical situations, which is why pedagogical theory and practice considers laboratory experimentation an essential part of the educational process, particularly in the sciences and engineering”. Online access to remote laboratories through Internet is gaining importance. The domain of Online Engineering is an interdisciplinary field comprising engineering, computing and telematics catering for live interactive engineering activities such as “programming, design, control, observation, measuring, sensing, and maintenance” to both remote and localised users (p. 222).

Auer, Edwards and Zutin (2011: 224) differentiate three kinds of engineering laboratories:

• The development laboratory is used by practicing engineers to obtain experimental data in order to answer specific questions and/or to design and develop products. Furthermore to ascertain if a specific design performs as it is intended.
• The research laboratory serves to seek broader and new additions to knowledge to generalise and systemise.
• The educational or instructional laboratory serves to allow undergraduates to learn what they as graduates would be assumed to understand and know.

Web-based laboratories were introduced in 1996 by universities in undergraduate engineering education. The field of remote engineering since matured; the number of remote laboratories dramatically increased; and the range of disciplines in which remote laboratories used expanded. The need for remote access to experimentation and for flexibility with regard to time and place of laboratory access are powerful motivators — “Anytime and anyplace learning is presently revolutionising learning in the workplace” (p. 226). Developments of mobile technology further accelerated advancements.

Auer, M.E.; Edwards, A & Zutin, D.G. 2011. Online laboratories in interactive mobile learning environments, pp. 119-248. In N Pachler, C Pimmer & J Seipold (Eds). 2011. Work-based Mobile Learning — concepts and cases. Oxford: Peter Lang.

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.

Sensory information type and processing preference

In order to optimise learning through simulated experiences, the Felder-Silverman Learning Style Model (FSLSM) is recommended. People exhibit different ways they prefer to learn and by incorporating different learning styles in teaching plans learning is made easier. Graf, Viola, Leo and Kinshuk (2007) observe that FSLSM is often used in the design of technology-enhanced learning. The learning styles models of Kolb (1984) and Honey and Mumford (1982)—in contrast to Felder and Silverman (1988)—classify learners into a few groups. FSLSM describes learning styles in more detail, distinguishing four dimensions depicted below (Sidhu, 2010: 23):

Graf, Viola, Leo and Kinshuk (2007) further points out that FSLSM is based on tendencies. They point out that although learners with a strong preference for certain learning style may experience difficulty if the teaching style does not match their preferred learning style; indeed sometimes behave differently. They further convey that educational researchers in recent years investigated how incorporating learning style models impact technology-enhanced learning and found that FSLSM is highly appropriate for hypermedia courseware. It has been found that when students do an online course that matched their preferred learning style they achieved significantly better results compared to students who took a course that did not match.

The first dimension—sensory/intuitive—Students that prefer learning facts and concrete learning material have a sensing learning style. Such students prefer standard approaches in solving problems; to relate the learned material to the real world; and tend to be more patient with details. They are generally realistic, sensible and practical. Intuitive learners, by contrast, prefer learning abstract learning material, such as the underlying theories and their meanings. Such students tend to be innovative and creative as well as more able to discover possibilities and relationships.

The second dimension—visual/verbal—Students with a visual preference learn and retain best what they have seen, for example pictures, diagrams, and flow-charts. Verbal learners derive more from textual representations, both written and spoken.

The third dimension—active/reflective—addresses different ways of processing information. Some students learn best by actively engaging; trying out and by applying the learning material. They further tend to prefer communicating and by working in groups where they can converse. Other students, in contrast, prefer to reflect and think about learning material. They prefer working alone or possibly in a small group or with a good buddy.

The fourth dimension—sequential/global—is about characterising students according to their understanding. Those students that learn in small incremental steps in linear progression are sequential learners. They prefer following logical step-by-step paths towards solutions and are interested in details. Global learners, however, use holistic thinking processes learning in large leaps. Such students tend to absorb learning material almost randomly, not necessary seeing connections initially but suddenly grasp the whole picture. They are capable to solve complex problems; to find connections between dissimilar areas; and to put things together in novel ways. However, they can seldom explain how they did it. Global learners prefer overviews and broad knowledge.

Sidhu (2010: 23) poses four questions:

1. What type of sensory information is most perceived?
2. What type of information does the student preferentially perceive?
3. How does the student prefer to process information?
4. How does the student characteristically progress towards understanding?

Graf, S.; Viola, S.R.; Leo, T & Kinshuk. 2007. In-Depth Analysis of the Felder-Silverman Learning Style Dimensions. Journal of Research on Technology in Education, Fall 2007, 40(1). Accessed on Internet 31 May 2012 from: http://wit.at/people/graf/publications/graf_viola_kinshuk_leo_JRTEjournal.pdf

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

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