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Beyond the engineering pedagogy: engineering the pedagogy, modelling Kolb’s learning cycle

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This is a conference paper it was presented at AaeE 2008 and it is also available at: http://www.events.rockhamptoninfo.com.au/author-index Experiential Learning is a modern radical approach of conducting education. Kolb’s four stages experiential learning model have been well received since it was proposed during mid 1980’s. In this paper, we approach the analysis of Kolb’s Cycle from an engineering point of view, where we develop a mathematical model of the learning curve when Kolb’s experiential learning cycle is use. Furthermore, we analyse the characteristics of the derived model for example, learning stability and learning robustness. We conclude with set of important characteristics of Kolb’s cycle that we could clearly explore after utilizing the control engineering tools. The most important characters are accommodating the uncertainties of the students learning ability. This paper is one of the few trials traced in the pedagogical literature where control engineering methods are applied for studying pedagogical process. Published
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Abdulwahed, Nagy & Blanchard, Beyond The Engineering Pedagogy: Engineering The Pedagogy,
Modelling Kolb’s Learning Cycle
Proceedings of the 2008 AaeE Conference, Yeppoon, Copyright © Mahmoud Abdulwahed, Zoltan K Nagy,
Richard E Blanchard 2008
1
Beyond The Engineering Pedagogy: Engineering The
Pedagogy, Modelling Kolb’s Learning Cycle
Mahmoud Abdulwahed
Loughborough University, Loughborough, UK
m.abdulwahed@lboro.ac.uk
Zoltan K Nagy
Loughborough University, Loughborough, UK
z.k.nagy@lboro.ac.uk
Richard Blanchard
Loughborough University, Loughborough, UK
r.e.blanchard@lboro.ac.uk
Abstract: Experiential Learning is a modern radical approach of conducting education.
Kolb’s four stages experiential learning model have been well received since it was
proposed during mid 1980’s. In this paper, we approach the analysis of Kolb’s Cycle
from an engineering point of view, where we develop a mathematical model of the
learning curve when Kolb’s experiential learning cycle is use. Furthermore, we analyse
the characteristics of the derived model for example, learning stability and learning
robustness. We conclude with set of important characteristics of Kolb’s cycle that we
could clearly explore after utilizing the control engineering tools. The most important
characters are accommodating the uncertainties of the students learning ability. This
paper is one of the few trials traced in the pedagogical literature where control
engineering methods are applied for studying pedagogical process.
Introduction
Constructivist pedagogy is a paradigm that perceives learning as a process of constructing knowledge
by individuals themselves rather than passively pouring information in their minds by the teacher
(Brown et al 1989; Steffe et al 1995). In constructivism, learning is a continuous journey of meanings
searching and knowledge construction. Since constructivism emphasizes the individual’s important
role in knowledge construction, its strategies in teaching are often called student-centered instruction.
The psychological works of Piaget left a significant contribution to the constructivist pedagogy, he
asserted that learning takes place by active construction of knowledge rather than passive reception of
knowledge (Piaget 1978). The main pillar of constructivist pedagogy methods is self experience of
learning. The role of experience in learning has been investigated by many and has been found to have
a dramatic impact. Farrell and Hesketh suggest that students typically recall about 20% of what they
hear, while if they hear and see something done, they may recall closer to 50% of the experience, if
they actually do something, such as conducting an experiment or solving analytical problem, they are
likely to recall as much as 90% (Farrell et al 2000). Experiential learning (EL) has gained increasing
interest in the education field during the last thirty years, especially in the United States. This period
has witnessed the birth of many experiential learning models. One of the most important experiential
learning model was proposed by David Kolb mid 1980s (Kolb 1984). Kolb, in his 6000+ times cited
book (Kolb 1984) has built on Dewey’s theory of education (Dewey 1938) and Lewin’s field theory in
social psychology (Lewin 1942). These works had lead Kolb to develop a four stages learning model
in which learning should involve the following phases: “Concrete Experience;” “Reflective
Observation; “Abstract Conceptualization; and “Active Experimentation”. The model is generically
called Kolb’s experiential learning cycle.
Control Engineering provides a valuable framework, theory, and the tools for modelling physical and
technical systems, analysing their dynamical behaviour, and controlling the system for achieving a set
of desired objectives. Control system methods have been recently and successfully extended to non-
Abdulwahed, Nagy & Blanchard, Beyond The Engineering Pedagogy: Engineering The Pedagogy,
Modelling Kolb’s Learning Cycle
Proceedings of the 2008 AaeE Conference, Yeppoon, Copyright © Mahmoud Abdulwahed, Zoltan K Nagy,
Richard E Blanchard 2008
2
conventional fields such as biology, economy, and finance. However, they have been seldom used to
develop models of pedagogical processes. The key elements of control system engineering are the
systematic perception of the process elements and their couplings, the goal oriented objectives of the
process and the necessity of measurements and feedback for the purpose of successful achievement of
the set objectives. In comparison with pedagogical processes, we find analogy in the following
pedagogical elements:
- Learning Objectives (Goal oriented objectives/Reference Signals/Regulatory Control).
- Formative and Summative Assessment (Measurements/Feedback).
- Learning is a dynamic process in general (Dynamical Control Systems).
We think that pedagogical processes can be looked at from control engineering point of view. The
latter would offer us a comprehensive theoretical framework and tools for analysing the former.
Kolb’s Experiential Learning Cycle
Kolb suggested that learners must be able to immerse
in new experiences (CE), they should have reflective
skills and multiple views of observation (RO), they
must be able to conceptualize the observations and the
experiences by integrating them into theories (AC),
and finally they must be able to use these theories for
making decisions and solving problems (AE). Hence,
effective learners should have four types of abilities;
Concrete Experience Ability (CE); Reflective
Observation Ability (RO); Abstract Conceptualization
Ability (AC); Active Experimentation Ability
(AE).The optimal learning takes place when an
adequate balance of these four characters is carried
out. The combination of the previous four stages is
called the Kolb cycle of experiential learning and is
shown schematically in Figure 1.
Kolb proposed that these are the stages of creating
knowledge by transformation into abstracts through experience. Learning requires that individuals first
should detect, depict, or grasp knowledge, and then a phase of construction should take place to
complete the learning process. This construction is a transformation of the grasped knowledge into the
mental model through experiencing this knowledge.
The vertical axis in Kolb’s cycle represents the knowledge grasping dimension, or prehension
dimension, by which knowledge can be grasped through Apprehension (the concrete experience
extreme) or by Comprehension (the abstract conceptualization extreme), or by mix of both. The
horizontal axis represents the knowledge transformation or knowledge construction dimension. The
construction can be done via Intention (the reflective observation extreme), or via Extension (Active
Experimentation). Kolb’s hypothesis of the two dimensional nature of knowledge building, the
prehension dimension and the transformation dimension, was drawn from convergent evidences from
philosophy, psychology, and physiology. Literature prior to this hypothesis, did not distinguish
between grasping and transformation, combining them in one axis. Hybrid combination of the
previous elementary modes in the learning process would produces higher and deeper learning levels.
Control Engineering Model of Kolb’s Cycle
Kolb has derived his model based on Lewin’s social and pedagogical works (Lewin 1951). Lewin
indeed had borrowed the control engineering concepts such as reference signals, measurements, and
feedback to develop a four stages model of learning that became later a core basis of Kolb’s
Abstract
Conceptualization
Concrete
Experience
Reflective
Observation
Figure 1: Kolb Experiential Learning Cycle
Active
Experimentation
Abdulwahed, Nagy & Blanchard, Beyond The Engineering Pedagogy: Engineering The Pedagogy,
Modelling Kolb’s Learning Cycle
Proceedings of the 2008 AaeE Conference, Yeppoon, Copyright © Mahmoud Abdulwahed, Zoltan K Nagy,
Richard E Blanchard 2008
3
experiential learning theory (Kolb 1984). Lewin considered information feedback an essential element
in building a continuous process of goal-directed learning actions. Lewin and his followers firmly
believed that much of an individuals and organizations deficits could be traced to a lack of feedback
processes. Kolb has emphasized many times that learning should be considered as a continuous
PROCESS grounded in experience. He defines learning as a process of constructing knowledge. In
this paper, a return to Lewin and Kolb’s utilization of feedback concepts from engineering is proposed
with a purpose of mathematically analysing the dynamical behaviour and presenting the advantages of
Kolb’s experiential learning cycle.
In the modelling course, it is very important to emphasize on the control engineering principle of
simplifying the target process in a much simpler model than reality. In many times, the system
behaviour is approximated to a linear one. In modelling, we consider the most important aspect that
the model is trying to analyse neglecting the other system characters. These main guidelines have
proved to be successful in modelling technical control systems, in many times due to the nature of
feedback loops which can accommodate model uncertainties. We draw on these principles when we
target modeling Kolb’s cycle, hence, simplifying and aggregating many characters into a simpler
linear character.
Mapping Kolb’s Learning Stages into an Engineering Model
Kolb’s cycle has four main stages, CE, RO, AC, and AE. The CE stage is a reported to be a place of
stimulation and attention towards the intended learning outcome (Bailey et al 2004), it represents the
exposition to new knowledge or experiences. The concrete experience plays the main role of
contextualizing the learning objectives and filtering them among the whole set of other information
and sensed variables by the students during the learning phase. It represents the first experience of new
knowledge to be learned. The stimulation and the first experience CE stage leads to reflection upon in
a form of the question “Why?” for understanding the experienced concrete status. This reflection will
transform the perceived knowledge in the CE stage into the conceptual abstraction AC in the learner
mind. As the abstracts were implemented in mind, the active experimentation AE can be triggered to
test the abstracts. This experimentation will lead to the subsequent phase of knowledge construction
via extension and hence to new concrete experience situations of higher order. Therefore a successive
cycle of learning will be initiated until the whole set of learning objectives (new knowledge) is
achieved. Figures 2 and 3 show a translation of Kolb learning process into engineering and
mathematical models.
The “Input” signal represents the learning objectives (new knowledge to be learned) which is
corrupted with noise, this noise presents any external informative, sensitive, or cognitive distortion
around the intended learning objectives. The CE would work as a filter contextualizing the learner into
a filtered set of learning objective he or she faces for the first time. Once the learner is exposed to this
new knowledge via concrete experience, a reflection phase would construct a new conceptual mental
models (or abstracts). The process of constructing new models would be associated with active
experimentation AE of this new models by the learner. The reflection and active experimentation
would lead to assimilating new models in the learner mind and hence accumulating newer knowledge
(Piaget 1977). This construction phase is modeled mathematically via a constructor (or
Knowledge
Transformation/
Construction
Actually learned
knowledge
Comparison
Knowledge
to be learned
Observation
Contextualization
Figure 2: Mapping Kolb’s Cycle into Engineering Model.
Abdulwahed, Nagy & Blanchard, Beyond The Engineering Pedagogy: Engineering The Pedagogy,
Modelling Kolb’s Learning Cycle
Proceedings of the 2008 AaeE Conference, Yeppoon, Copyright © Mahmoud Abdulwahed, Zoltan K Nagy,
Richard E Blanchard 2008
4
integrator/accumulator). The integrator action represents mathematically any accumulating
phenomena such as tank filling or capacity charging. The signal “Output” in Figure 3 represents the
new constructed abstracts of knowledge in the learner mind, i.e. what have really been learnt so far. As
soon as the learner has constructed new knowledge (i.e. learned something new), the observation
phase (measurement and feedback) will compare the new level with the learning objectives set. If
there is still something to be learned, the learner will find him/herself exposed to a new higher order
concrete experience, hence a new knowledge construction (accumulation/integration) phase will take
place. The loop keeps on running until the whole set of learning objectives are met.
The model shown in Figure 3 can be written in state space as follows:
1dx x Rf
dt a
Yx
=−+
=
(1)
Where x is the internal state presenting the accumulated knowledge level, Rf is the filtered reference
signal or input representing the set of intended learning outcomes, Y is the actually learned or
constructed knowledge.
Kolb’s Model is Stable and Able in Achieving The Learning Outcome Despite
Disturbances
One interesting point that the mathematical model of Kolb’s cycle reveals is the stable nature of
learning process when Kolb’s model is admitted in learning. The model derived in (1) represents a
first order integrator with feedback, this model is stable. Stability means that Kolb’s model can bring
the learning outcomes to the set point defined in the input as shown in Figure 4 (Left). Hence, the
learner will be able to reach the learning objectives set when learning involves balanced contribution
of Kolb’s learning stages.
a
s
Output
+
Input
Feedback/Measurement
Low Pass Filter
Contextualization
Figure 3: Mapping Kolb’s Cycle into Mathematical Model.
-
Rf Y
Figure 4: Simulation of Accommodating Learning Disturbances (Right) .
Abdulwahed, Nagy & Blanchard, Beyond The Engineering Pedagogy: Engineering The Pedagogy,
Modelling Kolb’s Learning Cycle
Proceedings of the 2008 AaeE Conference, Yeppoon, Copyright © Mahmoud Abdulwahed, Zoltan K Nagy,
Richard E Blanchard 2008
5
Figure 7: Learning Dynamics, Nominal vs.
Weak Learner
Furthermore, The model in (1) is able to reject constant disturbances. In mapping this to the learning
process, it means that when the learner is exposed to a disturbance during the learning period such as
loss of some learning outcomes, or non validity of some learning outcomes due to a change in
circumstances; the learner will be able to overcome these and bring the learning outcomes again to the
learning objectives set. Figure 4 (Right) shows simulation of a Kolb based learning process where a
loss of 30% of the achieved learning outcomes occurred after one learning time unit, however, the
learner could recover this loss completely and could bring the learning outcomes again to the set of
objectives. Figure 5 shows a schematic diagram of learning process with disturbance.
Kolb’s Learning Model Assists in Accommodating Learning Uncertainties
The closed feedback loop shown in Figure 3 has an inherent robustness characteristic against model
uncertainty; the uncertain model version of system given by (1) can be written as follows:
1()
dx xxr
dt a
= − +∆ +
(2)
Yx=
Where
x
represent the uncertainty. Figure 6 shows the block diagram of the Kolb based learning
process with learner uncertainty.
The model uncertainty may represent an uncertainty in one student’s ability of learning,.a weaker
student than the average can be modeled with an uncertainty term.
To show the robust characteristic of Kolb model, we simulate the nominal student learning model
Constructor
Figure 5: Disturbance Rejection Character
Filtered
Input
Disturbance
s
a
Figure 6: Kolb’s Engineering Model with
Uncertainty
Rf Y
Feedback
Uncertainty
Abdulwahed, Nagy & Blanchard, Beyond The Engineering Pedagogy: Engineering The Pedagogy,
Modelling Kolb’s Learning Cycle
Proceedings of the 2008 AaeE Conference, Yeppoon, Copyright © Mahmoud Abdulwahed, Zoltan K Nagy,
Richard E Blanchard 2008
6
given by equations (1) and compare it with the simulation of a weaker student by setting
0.5xx∆=
in
the uncertain model given by (2) (i.e. the weak student has about half learning capabilities of the
nominal students). Figure 7 shows the simulations results; we notice that in spite of the half capability
of the weaker student, she/he is able finally to reach the learning objectives. This enhanced
performance is mainly due to the feedback loop and the continuous work on bridging the gap between
the learning outcome and the learning objectives.
Conclusion:
In this paper, a mathematical model of Kolb’s experiential learning cycle has been developed, the
model is one of the few pedagogical models that are built with the assistance of control engineering
techniques. The study of the model revealed two important characters of Kolb cycle, first of all it is
stable and guarantees reaching the learning objectives. Secondly, it is robust and can accommodate
learner learning weakness through continuous process of the feedback loop repetition. This paper adds
an engineering quantitative evidence to the supportive pedagogical literature of Kolb’s experiential
learning theory.
References:
Baiely M, Chambers J, 2004. Using The Experiential Learning Model to Transform an Engineering
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Brown S, Collins A, Duguid P 1989. “Situated Cognition and the Culture of Learning”. Educational
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Dewey J. (1938/1997). Experience and education. Macmillan.
Farrell S, Hesketh, R P 2000. An Inductive Approach to Teaching Heat and Mass Transfer.
Proceedings of the 2000 American Society for Engineering Education Annual Conference &
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Kolb DA 1984. Experiential learning: experience as the source of learning and development. Prentice-
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Lewin K 1951, Field Theory in Social Sciences. New York: Harper & Row.
Piaget J 1978. The Development of Thought: Equilibration of Cognitive Structures. Blackwell.
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Tennant M. Psychology and Adult Learning. Second Edition. London: Routledge, 1997.
Copyright statement
Copyright © 2008 Mahmoud Abdulwahed, Zoltan K Nagy, and Richard Blanchard: The authors assign to AaeE and educational
non-profit institutions a non-exclusive licence to use this document for personal use and in courses of instruction provided that
the article is used in full and this copyright statement is reproduced. The authors also grant a non-exclusive licence to AaeE to
publish this document in full on the World Wide Web (prime sites and mirrors) on CD-ROM and in printed form within the AaeE
2008 conference proceedings. Any other usage is prohibited without the express permission of the authors.
Chapter
In this chapter, we highlight a number of pedagogies and curricular frameworks and models as toolkit for engineering education academics and schools for facilitation of the implementation of industry-integrated engineering education. In particular, we provide further details on competency-based education (Frank et al. 2010; Kenkel and Peterson 2010; Morcke et al. 2013; Johnstone and Soares 2014) and a number of learner-centric, constructivist, and experiential pedagogies (Abdulwahed et al. 2008) such as service-based learning (Bringle and Hatcher 1996; Waterman 2014), high-impact practices (Brownell and Swaner 2009; Kuh 2008), problem-/project-based learning (Mills and Treagust 2003; Abdulwahed et al. 2009), engineering design (Dym et al. 2005; Abdulwahed and Hasna 2017), etc. The learner-centric approaches are in particular useful for executing on competency-based education models in practice in the classroom and curricular delivery. Competency-based education is in particular a suitable framework for implementing industry-integrated engineering education because CBE emphasizes the combination of the following triangulation in situation, context, or professional activity:
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