Learning in vivo

Eight categories of assessing work-integrated learning

Sonia Ferns and Keri Moore (2012) indicate that assessment of fieldwork generally comprise three broad elements, namely (a) actual workplace performance; (b) a record of the experience (diary or journal); and (c) a reflection on the work-based experience. However, they mapped eight categories of assessment in use for assessing student outcomes from fieldwork placements:

• Case Studies research on cases: applying disciplinary knowledge in a professional context, roles, ethics, and responsibilities, systems, processes and lateral and higher order thinking, debates. Can be inter-professional or discipline specific.
• Reflection/portfolio: Reflection of work place experience and collection of evidence.
• Simulation: Institutional based assessment intended to replicate the workplace environment e.g., role play including Objective Structured Clinical Examinations (OSCE), peer and self-assessment.
• Knowledge test: Testing recall of facts such as online quizzes or multiple choice quizzes (MCQ).
• Performance - real-time in situ: Practical assessment of work place performance - detailed understanding of disciplinary systems, roles, ethics, and responsibilities, systems, processes and lateral thinking, proficiency in basic disciplinary skills, communication and core disciplinary knowledge.
• Oral Presentations: oral test - detailed understanding of disciplinary systems, roles, ethics, and responsibilities, systems, processes and lateral thinking.
• Written task: Essays or reports (other than case studies) higher order thinking.
• Industry-based case study: Investigation of actual work place scenarios.

Their research findings include, among others, some interesting graphs about frequencies of use of the above mentioned assessment types; and of graduate attributes assessed.

Ferns, S. & Moore, K. 2012. Assessing student outcomes in fieldwork placements: An overview of current practice. Asia-Pacific Journal of Cooperative Education, 13(4), 207-224.

E-portfolios and assessment via mobile technologies

The South African Council for Higher Education (CHE) imperatives with regard to programme (qualification) accreditation include, among others:

• That the programme must bring about an understanding within students about the specifics of the occupation for which they are being trained.
• That the students should master the techniques and skills required for a specific profession or occupation.
• And where feasible that work-based learning and placement in a work-based environment should form an integral part of the curriculum.

The CHE imperatives with regard to work-integrated learning further include (1) a mentoring system that enables the student to recognise her/his strengths and weaknesses in his/her work, to develop existing and new abilities, and to gain knowledge of work practices; as well as (2) regular and systematic monitoring. Mobile devices may well enable both these.

However, Coulby, Laxton, Boomer and Davies (2011) caution that without a sound pedagogical rationale for the use of mobile devices; technical and infrastructural realities may very possibly reduce student engagement.

Higher educational health and social care curricula often comprise a blend of classroom instruction (in residential education); simulations and work-based placement/s. The latter provide for learning opportunities and the assessment of competencies and professional attributes that cannot be cannot be replicated in the former two. The authentic nature of work-based placement/s enables holistic preparation; preparing graduates for professional practice, including the culture and practices of the particular community of practice. Primary care community clinics are utilised for placements in teaching. (Coulby, et al citing Billet, 2001; Lave & Wenger, 1991; Vygotsky, 1978).

Although work-based placements are regarded essential; Coulby, et al realise that these learning and teaching environments are challenged by practical and sometimes complex issues such as:

• Unreliable or restricted access to PCs and IT infrastructure; resulting in a lack of access to contemporary information; learning materials and social networks.
• Students experiencing uncertainty due to inadequate feedback on their performance resulting in unpreparedness; as well as insufficient monitoring, mentoring and tutoring (pastoral care) due to inadequate resources.

A potential solution explored is the ‘Assessment and Learning in Practical Settings’ (ALPS) programme, which employed mobile technologies. Common competencies, representing ‘readiness’ for professional practice were identified and developed into mobile assessment tools—which enable and encourage students to seek a spectrum of assessment opportunities such as from peers in the health and social care professions; from service users (patients); and self-reflection. The ‘ALPS’ assessments are completed are automatically captured online in e-portfolios; which in turn enable tutor and student review and commentary—in this way future practice of students are shaped and high-level scaffolding feasible. The University of Leeds, for example, has several hundred students in six different discipline/qualification cohorts.

Coulby, C; Laxton, J; Boomer, S & Davies, N. 2011. Mobile technology and assessment: A case study from the ALPS (Assessment and Learning in Practical Settings) programme, pp. 51-70. In N Pachler, C Pimmer & J Seipold (Eds). 2011. Work-based Mobile Learning — concepts and cases. Oxford: Peter Lang.

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.

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.

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.

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.

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

User-computer interface for simulations

Interface design concerns the practical information a multimedia-author needs in order to produce usable and well-designed interfaces, states Sidhu (2010: 30). In addition to the outcomes a learning package aims to achieve through, for example, tasks; the developing team need to consider several interacting concerns. Although several user interface standards exists—for example ISO, CEC/CENELEC and BSI—unfortunately some developers do not make use of a common standard. Human Computer Interaction (HCI) is a discipline “concerned with the design, evaluation and implementation of interactive computing systems for human” usage and “user interface is an interface developed to enable HCI”—or differently stated “a communication channel between the user and the functional elements of the computer” (Sidhu, 2010: 31) illustrated by Plass (1998, in Sidhu) as follow:

The illustration comprises two functions. At the bottom right, the user input is allocated (centre) to the application (left). At the top left, the functionality flows (centre) to the user (right). In the case of text-based HCIs only written verbal communication is used, whereas with visual HCIs manipulation of visual objects, verbal and auditory interaction is involved.

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

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.

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.

Does experience-taking offer a means of simulated learning?

Encounters with fictional characters offer the potential exposure “a diverse array of personalities, perspectives, events, outcomes, and realizations” remark Geoff Kaufman and Lisa Libby (2012: 1). Literature allows the imaginary transportation to other places; to other times; into characters with personality traits different from us; or the engagement in actions or the holding of ideas that are being aspired to. Biographies and fictional tales allow for the experiencing of life journeys of other people, which in turn may be an eye opener to the minds, struggles, achievements of individuals form diverse backgrounds. However, what is described here by no means happen automatically. On the contrary, it is required that the reader move beyond being a mere reader or spectator. It requires connecting to characters to such an extent that one step “into their proverbial shoes and experience the story from their perspective” (2012: 2), essentially to image becoming the characters while being in the world of the narrative. Kaufman and Libby (2012: 1) identify this as experience-taking and define the concept as “the imaginative process of spontaneously assuming the identity of a character in a narrative and simulating that character’s thoughts, emotions, behaviors, goals, and traits as if they were one’s own”. They undertook research to explore which factors facilitate and which impede experience-taking. They also demonstrate the influential impact experience-taking may have on the self-concepts, goals and actions of engaged readers. They further differentiate experience-taking from perspective-taking.

Kaufman and Libby (citing Oatley 1999, 2012: 2) suggests that experience-taking occurs when “readers simulate the events of a narrative as though they were a particular character in the story world, adopting the character’s mindset and perspective as the story progresses rather than orienting themselves as an observer or evaluator of the character”. In order to succeed in experience-taking, the reader need to set aside key aspects of own identity—“such as their beliefs, memories, personality traits and ingroup affiliations—and instead assume the identity of a protagonist, accepting the character’s decisions, outcomes, and reactions as their own” (Kaufman & Libby, 2012: 2).

It is suggested, based on the prediction and research findings of Kaufman and Libby (2012), that the better a simulation is capable to evoke experience-taking and to enable the participant to mimic the character’s subjective experience, the better is the potential of adapting self-concept, attitudes, and behaviour.

Kaufman, G. F.; Libby, L. K. 2012. Changing beliefs and behavior through experience-taking. Journal of Personality and Social Psychology, Mar 26.

Visit also: 'Losing Yourself' in a Fictional Character Can Affect Your Real Life

Oatley, K. 1999. Meetings of minds: Dialogue, sympathy, and identification, in reading fiction. Poetics, 26, 439–454. doi:10.1016/S0304-422X(99)00011-X

Drafting experiential education procedures for a university

Those who have been involved in a policy and/or procedure creation process at a university will know it is not an easy task. Substantial literature reviews (which I blogged about during the recent past) that informed a first draft had been undertaken. This draft (Download Procedural Manual on Experiential Learning) has been distributed by e-mail to key role players within the university for critique and comment. The commentary received has been incorporated and a second draft made institutionally available on Intranet and called for commentary through the university's daily e-news mail (Download 2nd Draft of Procedural Manual on Experiential Learning).

At this point a meeting with a senior educational-support line manager resulted in further review and supplementation. A third draft -- Download 3rd Draft of Procedural Manual on Experiential Learning has been produced, which served as discussion document with several key stakeholders. Following thorough consultation and incorporation of input, contributions, commentary, critique and recommended structural changes the process has since arrived at a sixth draft -- Download 6th draft towards a Procedural Manual on Experiential Learning. The sixth draft got formally tabled at a committee within the structure and accepted in principle - subject to a few changes. This new version (Download Procedural Manual on Experiential Learning DRAFT 2-1 - apologies for the hyperlinks to Intranet pages that readers would not have access to) is now the official 2nd draft, for which a powerpoint presentation (Download Procedural Manual on Experiential Learning) had been prepared in order to present it at a next level internal committee.

Ultimately a Procedural Manual for Experiential Learning had been presented to Senate 13 June 2012 and approved, whereafter it was placed on Intranet with the University Council approved Policy for Experiential Learning.

Online classroom support pre-service teacher education

Teaching is complex, requiring in excess of 3000 decisions each day in the classroom, reports Ferry and Kervin (2007). This poses serious challenges to pre-service teacher education, aggravated by the theoretical education often fail to prepare students for the practicalities of the classroom. Simulated (virtual) classrooms are seen as a means to prepare students for their future role as teachers.

Ferry and Kervin (2007: 190) reports that a team of five University of Wollongong lecturers and two research students work together for a year to produce a funding proposal. A further six months got devoted to plan the development of the simulation. The development of the simulation comprises a number of stages/processes:

Step	Description		Duration	Budget
1	Planning the simulation — the development of: Inputs and outputs to guide the user Story to guide user		Nine (9) months	$5 000 for teaching relief
2	Using the teacher-created script to shape prototype version 1 of the software: Physical classroom organisation Classroom management Teaching and learning The development and incorporation of embedded tool ‘thinking space’		Twelve (12) months	project manager, teaching relief, programming & graphic design
3	Trial of the simulation software		Two (2) months	$10 000 project manager, teaching relief & programming
4	Refining the software		Nine (9) months	$60 000 project manager, programming & graphic design
5	Authenticating the virtual kindergarden experiences		Six (6) months	$10 000 project manager
6	Trial of the software		Three (3) months	$5 000 project manager, teaching relief & programming
		Totals:	18 & 41 months	$190 000

Stella^TM had been chosen during the planning phase as architecture for the simulation— “STELLA® offers a practical way to dynamically visualize and communicate how complex systems and ideas really work” (ISEE Systems, Inc.). The project-team further started the ‘story’—the teacher narrative for the simulation with the following basic flow:

Teacher organises the day		Tasks before the class starts		Learning & teaching experiences		End of literacy L&T

Two distinct cycle typess (management; plus teaching & learning) were identified to incorporate into the STELLA® software:

Management cycles	Teaching and learning cycles
1. The organisation of the classroom	5. Sequencing episode
2. The start of the day	6. Modelled reading episode
3. The late arrival of a student	7. Modelled writing episode
4. Random decisions	8. Retell of a familiar story episode

Ferry, B & Kervin, L. 2007. Developing an online classroom simulation to support a pre-service teacher education program. In D Gibson; C Aldrich & M Prensky. 2007. Games and simulations in online learning: research and development frameworks (pp 189 – 205). Hershey: Information Science Publishing.

ISEE Systems, Inc. STELLA Systems Thinking for Education and Research. Download STELLA_productsheet

Three modes of experiential education

The Best Practices for Legal Education (2007) publication of the Clinical Legal Education Association (CLEA) represents the fruit of 25 years of rich dialogue among American law teachers, lawyers, and judges, regarding the education of lawyers. In the foreword is stated that the American Bar Association convened a 1984 conference ‘Legal Education and the Profession: Approaching the 21st Century’. The foreword further states that in 1987 Justice Rosalie Wahl of the Minnesota Supreme Court and Chair of the ABA Section of Legal Education and Admissions to the Bar rhetorically asked:

Have we really tried in law school to determine what skills, what attitudes, what character traits, what quality of mind are required of lawyers? Are we adequately educating students through the content and methodology of our present law school curriculums to perform effectively as lawyers after graduation?

The purpose of this elaborate prologue is to convey the credible nature of the three modes of experiential education identified in Chapter 5: Best Practices for Experiential Courses (pp 121 – 152, note that the page numbers of individual online chapters differ from the full publication) of the CLEA Best Practices for Legal Education (2007: 122), namely:

• In-house law clinics offering students opportunities to represent clients or perform other professional roles under the supervision of members of the faculty
• Externships — where students represent clients or perform other professional roles under the supervision of practicing lawyers or they observe or assist practicing lawyers or judges in their work
• Simulation-based courses granting students hypothetical situations to assume professional roles and perform law-related tasks

These three modes, although many different terms are used, are common in many disciplines, for example:

• Practicals, laboratory work, applied field experiences facilitated and managed by lecturers
• Work-integrated/experiential/work-based learning in the occupational field under supervision of an experienced practitioner
• Simulated learning providing experience in hypothetical situations

Brayne, Duncan and Grimes (1998: 244) address externships as a means for law students to obtain a credit “working in legal settings”. It is interesting to note that “in the United States, students may not receive financial compensation for work which warrants educational credit”. Brayne, Duncan and Grimes (1998) identify both advantages and disadvantages with regard to externships. Potential advantages include “lower costs, variety of opportunities, work experience, service to the community, and flexibility”. As far as disadvantages are concerned, the main one identified is the potential “lack of control over the quality of the educational experience”.

Brayne, H; Duncan, N & Grimes, R. 1998. Clinical legal education: active learning in your law school. London: Blackstone.

Stuckey, R & Others. 2007. Best Practices for Legal Education. United States: Clinical Legal Education Association. Retrieved 2 November 2011 from: http://www.cleaweb.org/best-practices -- note the hyperlink to the best practices blog at the bottom of the web page.

What are experiential programmes?

The Clinical Legal Education Association (CLEA) Best Practices for Legal Education (2007: 165, note that the page numbers of individual online chapters differ from the full publication) defines experiential programmes as those “rely on experiential education as a significant or primary method of instruction”. The experiences of students in the roles of lawyers, in simulations, as well as “their observations of practicing lawyers and judges” play an important role. The integration of theory and practice through “combining academic inquiry with actual experience” is regarded as experiential education.

Learning is not education, because learning happens as part of life through experiences, observations and eavesdropping. Education comprises learning opportunities that were designed; are managed; and may involve guided experience. Although law students may derive valuable learning experiences from part-time work experiences in legal settings, the CLEA Best Practices for Legal Education (2007: 165) argues cannot be regarded “experiential education because the learning in such environments is not necessarily accompanied by academic inquiry”.

Stuckey, R & Others. 2007. Best Practices for Legal Education. United States: Clinical Legal Education Association. Retrieved 2 November 2011 from: http://www.cleaweb.org/best-practices

Doing law as means to learning about law

In the developed, technocratic world people—especially younger generations—became accustomed to virtual realities on PC screens. Interactive PC games offer excitement, insight and fulfilment. Clinical simulations could offer similar stimulus, engagement observation how law works. Brayne, Duncan and Grimes (1998: 173) state aptly “doing law as means to learning about law is different from learning the law by reading it”. Reconstruction of legal incidents through the vehicle of simulations; and the presentation of social problems as law ‘cases’; are but two examples. A legal problem on paper does not contain the uncertainties, emotions and interactions that form part of the real-world realities of, for example, the attorney’s office, the police station or the courtroom.

Brayne, Duncan and Grimes (1998: 174) observes that “simulation is commonly regarded as a valuable part of clinical teaching, but one which is the poor relation of the real-client clinic”, because simulation lacks the “startling and awakening encounters … of coping with people” present in legal practice “who have a need of legal expertise”. On the other hand, simulations can provide opportunities not readily available and can invoke the emotions and flow of adrenalin associated with daily client work.

In undergraduate legal education, simulations would inculcate appropriate behaviours in addition to the realisation that there is more to lawyering than mastery of legal knowledge. Simulations further offer students safe opportunities to experiment and to learn from mistakes.

Brayne, Duncan and Grimes (1998: 183) emphasise that simulation can never be an alternative to real client work, but within a spectrum of teaching methods simulations could contribute to effective law learning.

Brayne, H; Duncan, N & Grimes, R. 1998. Clinical legal education: active learning in your law school. London: Blackstone.

Simulations are interactive abstractions of real-life

Akilli (2007: 4) defines a simulation “as an interactive abstraction or simplification of some real life … or any attempt to imitate a real or imaginary environment or system”.

Although a substantial body of literature accumulated about simulations, Akilli (2007: 5) observes that it is still vague if games and/or simulations influence the learning of students in a positive way. Some literature does, however, describe the effects on:

• Discovery learning strategies
• Problem solving skills
• Computer usage skills
• Intellectual, visual and motor skills of students
• The engagement and interactivity of students
• Motivation to take responsibility for own learning

Evidence suggests, according to Akilli (2007: 6) “the use of games as instructional tools” in 3000 B.C. in China. Despite re-entry of use in the educational context in 1950s; games and simulations only became part of formal instructional design in the 1970s. However, it is reported that instructional designers encounter two problems:

• Absence of available comprehensive design paradigms — addressed through the publication referenced
• Lack of well-designed research findings — existing are mainly perceived reactions of students

Akilli, GK. 2007. Games and simulations: a new approach in education? In D Gibson; C Aldrich & M Prensky. 2007. Games and simulations in online learning: research and development frameworks (pp 1 – 20). Hershey: Information Science Publishing.