Learning in vivo

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.

Each credit equals 10 notional hours of learning

There is an argument that an average student should not be required to devote more than 10 hours for each credit. A 12 credit work-integrated learning module, it is argued, should therefore take 120 hours, if not:

• The curriculum is wrong
• The assignment is incorrect
• The WIL assignment is too long
• The assignment is not good enough

The argument disregards the time it takes for a student to settle in and to be orientated. The argument does not take into consideration the learning derived from various alternatives in real life. Neither does the argument take into consideration the reality that often a volume of mundane activities are required for an achievement. Few work places would be on the ready awaiting a student’s focussed exposure. In most real life situations work goes on and students learning from being present and making a contribution of some kind.

The advice from the bearer of the news is “make peace with it and move on…”

Patricia Anne Greening remarked on Facebook: "It also ignores such factors as outside distractions, poorly prepared or poorly presented material; and the state of the learner's health, and mental condition (e.g. sleepy, undernourished; stressed)."

Hi Thomas:

I do not agree that one credit equals 10 hours of learning.  Some years ago in Australia, there was an attempt by the government to enforce a similar rule and in the end they gave up for the reasons outlined on your blog.  It does not make sense.  I am not sure what can be learned in a short time in any work place.  The other issue is that if you focus on hours and credits, then you miss the learning bit.  What are the learning outcomes expected in work place learning?  How long does it take to achieve that learning?  What is the evidence for that learning?  And how long does it take to integrate the learning in the workplace with academic learning?

Cheers,

Lesley

______________________________
Lesley Cooper
Acting Principal/Vice-President
Wilfrid Laurier University
Brantford Campus
73 George Street
Brantford, Ontario   N3T 2Y3
CANADA

Hi Lesley, Thomas and others,

One of the issues for work placements papers is that it does not neatly fall into the ‘typical paper template’.  The typical point baring paper template is a one size fits all model – which may work well for academic taught papers but does not for placement papers, but nevertheless that is the default model.  We have the same situation here at Waikato, where our placement papers are 20 points (supposedly 200 hours of learning) whilst students are at least 400 hours in the workplace.  We found it best to avoid drawing direct links to points and hours in the workplace – and most people recognise that placements are different.  

However, if forced in situation of drawing links to hours on placement and credit points, the first question to ask is, ‘is every hour in the workplace a quality learning hour?  We would argue not.  Probably initially it is, and perhaps also at the end of the placement, especially when engaging in placement report writing.  However, there will be fair chunks of time where it probably isn’t – or rather where there will be ‘dilute learning’ or where the learning is ‘spread out’.   So perhaps it is best to craft an argument that links points to hours of quality learning rather than hours in the workplace.  The later will allow for the longer placements needed for a better overall learning experience for the students, whilst the former is too restrictive and assumes that learning always occurs.

kz

Dr Karsten Zegwaard The University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand

Attributes and learning-styles of students affect their learning

Sidhu (2010: 16) identifies the following attributes that may hinder or improve learning:

• Basic background knowledge of the subject matter / discipline / field of study
• Prior academic performance impacts on a student’s performance
• Degree of exposure to contemporary educational technologies — the use of computer-based learning is enabled if students already acquired PC usage skills
• The impact of the learning styles of students have been extensively researched

Depicted below are Kolb’s as well as Honey and Mumfords learning style models, some of the most well known models:

http://www.businessballs.com/kolblearningstyles.htm

http://www.nonformality.org/2006/04/excuse-misuse-or-abuse/

Kolb’s experiential learning styles are in black, Honey and Mumford’s in red.

http://pmj.bmj.com/content/83/982/518.full

http://rphrm.curtin.edu.au/2000/issue2/outcomes.html

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