Knowledge retrieval tools and learning style
Guide: Professor Madhukar Shukla
Submitted by
Sidarth R.
PMIR 2001
XLRI Jamshedpur
Abstract
Organisational learning is an encompassing field
embracing knowledge management. There has been growing interest in the
effective implementation and efficacy of knowledge management systems. Most
systems implemented have been done by installing the latest or effective
technological tools/package but they are not done taking into consideration whether the employees of the
organisation prefer any particular implement for knowledge retrieval and
dissemination. This study is an
exploratory attempt to investigate linkages between learning style and the kind
of information gathering tool/system an individual prefers. The study has used
a sample of 38 students in XLRI, Jamshedpur doing their second year of the
Business Management and Personnel Management programs. The study opens a number
of useful avenues for analysis while proving that Learning style could be a
factor to consider while designing such systems. It also brings to fore how
individuals generally differ in different cases of knowledge retrieval and
dissemination. Most individuals prefer to self explore and read
reports/projects as a source of gathering information. Nevertheless there is a
great proclivity, shown by individuals, towards personalised information ferreting
as in chat, mail, etc. Findings do not
indicate any great relation between learning styles and inclination towards
actually adapting other works or using the knowledge codified in organisation.
Table of contents
Organisation
Learning and Knowledge Management:
Understanding the
Impact of Transferring Knowledge
Problems With
Current Approaches in the IT Environment
Disemmination in
KM systems (Systems based/ Personalisation Based )
Learning
Theory and Kolb’s Learning Style
Inventory:
Exhibit II: Kolb’s learning cycle
Validity of Kolb’s Learning Style Inventory:
Kolb’s Learning
Cycle and IT tools/ methods
Exhibit III: Respondents Profile
Kolb's Learning Style
Inventory
Literature Review:
By three
methods we may learn. First, by reflection which is noblest; second, by
imitation, which is the easiest; and third, by experience, which is the
bitterest.
-- Confucius (B.C. 551-479 )
Learning can be considered as the way to fill the
“repository of knowledge”. Kolb defines learning as the process where by
knowledge is created through the transformation of experience (Kolb, 1984).
This definition for learning relates to the “know-how” and “know-why” of Kim
(1993). According to this definition
learning takes place in a cycle of four steps: first an experience is made; in
a second stage observations and reflections on that experience are created; thirdly,
abstract concepts and generalizations are formed based on these reflections;
fourthly these ideas on the new situation get tested which in turn gives new
experiences.
Huber
(1991) defines learning as a process
that enables an entity to increase its range of learning is then defined as occurring when any of an organization’s
units acquires knowledge that the unit recognizes as potentially useful to the
organisation - an explicit attempt to avoid ‘narrow conceptions’ that ‘decrease
the chances of encountering useful finding or ideas’. Because the organization
is the potential beneficiary of the knowledge, this learning is organizational.
Organizational
learning is not a new concept. Lee et al (1992) have defined organizational
learning as a process in which “individuals’ action leads to organizational
interaction with the environment; the environment response; and environmental
responses are interpreted by individuals who learn by updating their beliefs
about cause and effect relationships”.
Most of the definitions propose by the researchers consider
organizational learning as a dynamic process of creating, acquiring and
transferring knowledge (Garvin, 1993) and converting a company into a
“knowledge-based” organization (Cash et al. 1988).
Organizational learning literature is fragmented, consisting
of multiple constructs and little cross-fertilization among pockets of
scholars. Some researchers study how Organizations
learn-that is, how these social systems adapt, or change, or process
incoming stimuli; these outcomes are typically functions of individual
cognitive properties or of organization policies or structures. Other researchers primarily examine how individuals learn-that is, how
individuals embedded in organizations develop, adapt, or update cognitive
models. Although they study organizations,
the starting point for their analysis of organizational phenomena is
individuals.
Some organisational learning theorists examine
processes of individual adaptation or development in organizational settings.
For example, Brown and Duguid (1991) describe learning as becoming ‘an insider’
by acquiring tacit or ‘non canonical’ knowledge. Although these researchers
studied how work groups became communities of learning, it is the individuals
who learn, become insiders, and contribute to differences among groups.
Descriptive theory at the individual level of analysis includes models that
specify conditions that elicit employee learning, as well as models that
describe beneficial outcomes of an organizations’ individuals engaging in
learning activities.
The lowest level
of known facts is data. Data has no intrinsic meaning. It must be sorted,
grouped, analysed, and interpreted. When data is processed in this manner, it
becomes information. Information has a substance and a purpose. However,
information does not have meaning. When information is combined with context
and experience, it becomes knowledge.
Baets(1998) in
his book describes how Polanyi’s (1962) distinction between objective and tacit
knowledge challenged the positivist definition of knowledge directly. Objective
knowledge is similar to science-abstract and independent of knower-experience.
Hirschhorn (1984) has suggested that the engineer’s way of knowing an
industrial plant can be distinguished from the skill of the craftsman who made it, for this knowledge is too limited, and from
the operator’s, for his tacit understanding
lacks theoretical depth. Polanyi argued that tacit knowledge is the underlying
fertile intellectual ground for all scientific work (Gelwick, 1977). Yet he illustrated tacit knowledge with
kinetic examples, such as that which bicycle riders can demonstrate but cannot
explain. The incommunicability of the firm’s craft-like tacit knowledge seems
an appropriate way to point to its idiosyncratic experience-base knowledge.
Indeed, it may be that the essential difference between sciences and technology
lies in their tacit components. Polanyi tended to define the tacit in terms of
its incommunicability. Although this seems clarifying at first, it makes it
difficult to see how we can relate incommunicable personal knowledge to a
useful and communicable theory of organizational strategy.
I Nonaka &
H Takeuchi(1996) have dwelled on tacit knowledge in great detail. They suggest
that for cognitive skills to develop, all declarative knowledge, which
corresponds to explicit knowledge in our theory, has to be transformed into
procedural knowledge, which corresponds to tacit knowledge, used in such
activities as riding a bicycle or playing the piano. Knowledge is created
through the interaction between tacit and explicit knowledge allows us to
postulate four different modes of knowledge conversion. They are as follows:
(1) from tacit knowledge to tacit knowledge, which we call socialization; (2)
from tacit knowledge to explicit knowledge, or externalisation; (3) from
explicit knowledge to explicit knowledge, or combination; and (4) from explicit
knowledge to tacit knowledge, or internalisation. Socialization is connected
with the theories of group processes and organisational culture; combination
has its roots in information processing; and internalisation is closely related
to organizational learning. (Ref: Exhibit 1)
Socialization
is a process of sharing experiences and thereby creating tacit knowledge such
as shared mental models and technical skills. An individual can acquire tacit
knowledge directly from others without using language. Apprentices work with
their masters and learn craftsmanship not through observation, imitation and
practice. In the business setting, on-the-job training uses basically the same
principle. The key to acquiring tacit knowledge is experience. Without some
form of shared experience, it is extremely difficult for one person to project
her-or himself into another individual’s thinking process.
Externalisation
is a process of articulating tacit knowledge into explicit concepts. It is a
quite essential knowledge-creation process in that tacit knowledge becomes
explicit, taking the shapes of metaphors, analogies, concepts, hypotheses, or
models. When we attempt to conceptualise an image, we express its essence
mostly in language-writing is an act of converting tacit knowledge into
articulable knowledge (Nonaka,1994). Yet expressions are often inadequate, inconsistent,
and insufficient. Such discrepancies and gaps between images and expressions,
however, help promote “reflection” and interaction between individuals. The
externalisation mode of knowledge conversions typically seen in the process of
concept creation and is triggered by dialogue or collective reflection. A
frequently used method to create a concept is to combine deduction and
induction. Externalisation holds the
key to knowledge creation, because it creates new, explicit concepts from tacit
knowledge.
Combination is
a process of systemizing concepts into a knowledge system. This mode of
knowledge conversion involves combining different bodies of explicit knowledge.
Individuals exchange and combine knowledge through such media as documents,
meetings, telephone conversations, or computerized communication networks. Reconfiguration of existing information
through sorting, adding, combining, and categorizing of explicit knowledge (as
conducted in computer databases) can lead to new knowledge.
In the business
context, the combination mode of knowledge conversion is most often seen when
middle managers break down and operationalize corporate visions, business
concepts, or product concepts. Middle management plays a critical role in
creating new concepts through networking of coified information and knowledge.
Creative uses of computerized communication networks and large-scale databases
facilitate this mode of knowledge conversion.
At the top
management level of an organization, the combination mode is realized when
mid-range concepts (such as product concepts) are combined with and integrated
into grand concepts (such as a corporate vision) to generate a new meaning of
the latter. Internalisation is a process of embodying explicit knowledge into
tacit knowledge. It is closely related to “learning by doing”. When experiences
through socialization, externalisation, and combination are internalised into
individuals’ tacit knowledge bases in the form of shared mental models or
technical know-how , they become valuable assets.
For explicit
knowledge to become tacit, it helps if the knowledge is verbalized or
diagrammed into documents, manuals, or oral stories. Documentation helps
individuals internalise what they have experienced and thereby enriching their knowledge.
In addition, documents or manuals facilitate the transfer of explicit knowledge
to other people, hence helping them experience the experience the experiences
of others indirectly (i.e., “re-experience” them). Internalisation can also
occur even without having actually to “re-experience” other people’s
experiences. Organizational knowledge creation is a continuous and dynamic
interaction between tacit and explicit knowledge. This interaction is shaped by shifts between different modes of
knowledge conversion, which are in turn induced by several triggers.
Exhibit I-A:
Nonaka’s Model
EXTERNALIZATION Interacting
Knowledge SOCIALIZATION Originating
Knowledge
Exhibit I
An organization
cannot create knowledge by itself. Tacit knowledge of individuals is the basis of
organizational knowledge creation. The organization has to mobilize tacit
knowledge created and accumulated at the individual level. The mobilized tacit
knowledge is “organizationally” amplified through four modes of knowledge
conversion. The interaction between tacit knowledge and explicit knowledge will
become larger in scale as it moves up the ontological levels.
Tacit knowledge
gets use in managerial tasks and tacit knowledge is the knowledge that makes
the difference. Key to acquiring tacit
knowledge is experience. A well-identified example of tacit knowledge in
management is the decision-making behaviour of dealers on financial
markets. Based on what they learned
from their past experience, the things they read and hear, probably the
“climate on the market”, they make decisions on buying and selling in a few
seconds. We like to call this
“instinct” or “fingerspitzengefuhl” but the behaviour of different dealers is
different. Each individual dealer seems
to have his own way of dealing based on his experience and his reference
framework. It proves extremely
difficult to extract this kind of “knowledge” from dealers and not because they
do not want to share that. It seems extremely difficult for dealers to express
the knowledge that they use, or in technical term to make tacit knowledge
explicit. (Baets, 1998)
Much of
workplace expertise lies in being able to formulate problems in ways that
successfully reflect the context and its possibilities. Knowing the problem
cannot be separated from knowing the environment in which the problem occurs.
Her research demonstrated the gulf between scientific and workplace knowledge.
It established the bounded ness and complex specificity of workspace knowledge.
Explicit individual knowledge can be tacit when it is bound to the context in
which it is used.
Effortlessness
seems to be one of the characteristics of tacit knowledge. It suggests that the
user is unaware of the tacit knowledge being applied. The metaphor of the iceberg may apply, what individuals know
explicitly is little indication of the vast mass of knowledge that lies beneath
the surface of everyday organizational activity.
Bargh, an
experimental psychologist, has unpacked some of the complexities of the
automatic mode of knowing. He argued that ‘automaticity’ is a notion with
several dimensions: (1) awareness, (2) attention, (3) intention and (4)
control. These terms can be illustrated in the following ways. First, an actor
is often unaware of his or her activity when it is effortless. Thus high-intensity
activity seems effortless when one is in a state of ‘flow’ and one’s entire attention is focused outwards on the
objective of the activity rather than on its process. Second, some actions are
automatic because they do not require one’s conscious attention. Reacting to
sudden noises or avoiding near accidents like tripping or catching a falling
object, involve skilled performance, but without the actor’s attention being
focused. Third, activity is also automatic when it cannot be associated with a goal
and is thus without intention. One
example of this is ‘side-effect encoding’, when people remember patterns which
are unrelated to their task. Another is the ‘action slip’, for instance,
William James’s oft-repeated story of the man who, on going upstairs to rest
for dinner, ‘finds’ himself in bed. Fourth, automaticity also implies loss of
conscious control. Habits are not only effortless and beyond one’s attention,
they are frequently uncontrollable even when one is aware of them. They refuse
to remain in one’s focal awareness, sliding uncontrollably to the subsidiary
level. While the automaticity is clearly psychologically complex, research
underlines that automatic behaviour is both prevalent and researchable. It is
also clear that one way to build up automatic knowledge is via conscious
practice. This ‘sediments’ the incommunicable aspects of practice into the
nonconscious domain while it adds to the inventory of contextualized practice.
Hence workplace knowledge is (1) likely to be practical rather than scientific,
and (2) can be both conscious and the automatic complement each other in an
actor’s practice.
Organisation Learning and Knowledge Management:
Dixon, Huber
etc. view Organizational learning as Information acquisition, Information
distribution and interpretation, Making meaning, Organization memory, and
Retrieval of information. These
components appear similar to those of Knowledge Management. However, Organization learning goes a little
beyond the apparently simple (models of Knowledge Management) and discusses
subjective and perceptive issues involved, such as who decides what is
knowledge, what is the appropriate (right) knowledge, behavioural dynamics of
people that tend to distort the information, etc. That's where some of the behavioural perspectives add to
Organization learning, including 'double loop learning' of Argyris. So, Organization learning in a way
encompasses Knowledge Management.
Smith, Burgoyne
and Aranjo (1999) in their book describe how Lessem (1991) has defined organisational-learning
constructs, viz., knowledge origination, knowledge development, knowledge
refinement, knowledge promotion, knowledge adaptation, knowledge implementation
(dissemination), and knowledge application and has suggested that organizations
build up these constructs to become a learning organization. In most of the literature, a learning
organization is not really defined either. According to Lessem, knowledge
origination is the process that opens up entirely new fields of knowledge.
Knowledge development is the process that uncovers potential for the
application of newly discovered knowledge across a wide diversity of fields in
organizations. In the context of
organizations, both knowledge origination and development take often place in
Research and Development activities.
These two processes are becoming increasingly important if organizations
are to keep apace with technological innovation. Gradually, knowledge origination and development also take place
outside the R&D context; the more a company becomes a “learning
organization”.
Knowledge
refinement is the process that refines the originated and developed knowledge
into systems, policies, routines and procedures. For instance, marketing analysts would refine and specify the
system required for the introduction of a new product.
Knowledge
promotion is the process of promoting the knowledge so that others can use it.
Promotion does not involve any original or developmental activity other than
customisation of knowledge. Knowledge
adaptation is the process of adapting the knowledge which is specific to a
situation / field to solve an analogous problem. Such a process is
conventionally used in management services.
Knowledge
implementation (dissemination) is the process of ensuring that the knowledge
physically reaches the right place at the right time. Information technology plays a major role at this stage. Knowledge application is the process of
putting the acquired knowledge into action.
Learning from a
knowledge-based perspective is the combination of building up these processes.
When the knowledge is generated within the organization or from outside the
organization, learning is said to occur.
Schwardt &
Marquardt (2000) specify that learning in the context of organizations occurs when:
Individual members of the organization form their views/knowledge
(mental models) on the action-response of organization and environment.
Individual members share their knowledge and form pooled knowledge.
Individual members update their knowledge in a changing environment.
Organizational
learning is inherently collective and shared individual learning. Organizations learn from their own past
experiences and also from the experiences of other organizations. Learning may take place within the organization
due to internal information exchange, participative policymaking and a learning
approach to strategy formulation. Internal information exchange and
participative policy making leads to learning as they enable members of the
organizations to share their knowledge and values. In a learning approach to strategy, formation of strategy, its
implementation, evaluation and improvement are consciously treated as a
learning process as it enables the companies to interrelate their performance
an anticipated environmental changes.
In practice, certain organizations do consider planning as a learning
process. For instance, at Shell
“Planning means changing minds, not making plans” (de Geus, 1988). Needless to say, a part of inter-company and
intra-company learning takes place during the planning process.
Understanding the Impact of Transferring Knowledge
Knowledge is
the combination of information, context, and experience. Context is an
individual's framework for viewing life. This includes influences like social
values, religion, heritage, and gender. Experience is previously acquired
knowledge. When knowledge is transferred from one person to another, the
knowledge is drawn into the receiver's context and experience (Bohm, 1994; Gick
& Holyoak, 1987). The new knowledge is interpreted according to the
receiver's context and experience. If the receiver does not have an appropriate
background for interpreting the new knowledge, the new knowledge will not be
interpreted correctly and the knowledge will have little or no value. For
example, if a student does not have experience interpreting text, the student
will not be able to learn from books (Brooks & Dansereau, 1987). At the
same time, if the sender uses a poor symbolic representation of the knowledge,
the receiver will be misled or may even be unable to understand the new
knowledge (Mezirow, 1991).
Knowledge has
several characteristics that affect the use of that knowledge. Knowledge does not
rely on access to the original information. A symbol can be created to
represent the original information (Stehr, 1994). This means the knowledge can
be transferred from one person to another without having to transfer all of the
information.
A knowledge
centric approach in the Information Technology environment would be
characterized by a conscious effort to transfer knowledge rather than
transferring information. To transfer knowledge, the receiver's context and
experience must be taken into account. The intended result is information is
transferred in context instead of with no context. If the sender uses a
knowledge centric approach, the receiver's knowledge base, values, and feelings
will be taken into account. The receiver's knowledge base, values, and feelings
determine how the information being received will be integrated into the
receiver's knowledge base (Mezirow, 1991).
A knowledge centric
approach requires a blurring of the roles of sender and receiver. Each party
must be aware of the other's context and experience. The transfer of knowledge
becomes a participatory event where both parties will add to their knowledge
base (Stehr, 1994). The result will be knowledge that is more than the
knowledge that was to be transferred.
In addition, the scope of
the knowledge being transferred will need to be broadened to include the
context in which the knowledge is placed at the group or organization level. In
order for the knowledge to have meaning, the events that produced the knowledge
must be understood as a whole (Mezirow, 1991).
In order to promote
knowledge transfer within the group or organization, an effort to build a
common context needs to be pursued. This would include ensuring the group or
organization shares common organizational values, shares a common view of the
organization's goals, and shares a common knowledge base. This could be
expanded to include promoting common communication and decision-making
techniques. People act the way they think (Bohm, 1994). The more similar group
members' thinking is, the more similar their contexts will be.
Problems With Current Approaches in the IT Environment
The typical environment
in an Information Technology enabled organization supports the transfer of
information, but does not consciously support the transfer of knowledge.
Problems are addressed and decisions are made with little or no exploration of
the organizational context of the problem, the personal context of each person
addressing the problem, or the experience of each person addressing the
problem. This leaves the problem solving participants relying on knowledge
gained from their personal experience as the main framework for solving the
problem. (Baets, 1998)
Existing systems
encourage sharing work practices and facilitating communication within groups
and among groups. An ‘individual’ capability effectively shared among the
individuals of a group is often a new capability in itself. Also, a way of effectively sharing work
practices and capabilities has to do with the training of individuals and
groups in order to make them effective as users.
The sharing of
capabilities not only contributes to the creation of new ones, but also to the
goal of spreading them in the organization, thus effectively helping to
communicate and share the organizational context to the extent that those
capabilities are relevant to it. Groupware is a technology directly relevant
for all these purposes, in all its forms. Even straight forward applications
based on simple electronic mail infrastructures may be very effective in
facilitating work practice sharing, and in putting different capabilities,
‘owned’ by different individuals or groups even geographically dispersed, to
work effectively together in ways not feasible before (Smith, et al., 1999).
Although, Huber (1991)
explicitly specifies the role of Information Technology in the Learning
Organization as primarily serving Organizational Memory, it can serve the other
three processes - Knowledge Acquisition, Information Distribution, and
Information Interpretation. There has been an increasing application of
Groupware tools, Intranets, E-mail, and Bulletin Boards to facilitate the
processes of Information Distribution and Information Interpretation.
The problem of retrieval and
storage is a real issue facing most organisations. Even when knowledge is
published, and failure to publish is itself an important cause of bias,
knowledge is not always easy to find.
The searcher for knowledge faces the following problems :
Incompleteness, English
language bias, inadequate indexing, Inadequate skill (even the most elegant
electronic database still has to be searched with skill and, understandably,
not everyone has the same levels of skill)
Disemmination in KM systems (Systems based/ Personalisation Based )
|
No. |
Co. |
Sector |
Indicators of Decisions |
|
1 |
RPG Enter. |
Diverse |
Knowledge Integration Process Systems-KIPS Intranet |
|
2 |
Piramal Enter. |
Diverse |
IT based |
|
3 |
Ranbaxy |
Pharma |
Digital Backbone |
|
4 |
E & Y |
Consult |
Global
Database IT Heavy |
|
5 |
AC |
Consult |
Knowledge Exchange |
|
6 |
KPMG |
Consult |
Single Portal |
|
7 |
McKinsey |
Consult |
Discussion- Personalization Strategy |
|
8 |
VT Plex |
|
Engineered Coffee Break |
|
9 |
TCS |
IT |
Intranet Knowledge bases |
Note|: Those underlined indicate Personalization Strategy
The above table
clearly reflects the depth of dependences on technology by companies to help in dissemination of information.
In fact in most cases they are the only tools for knowledge acquisition.
Since the concept of
knowledge management emerged in the early '90s, many ambitious knowledge
management projects have failed. Users and analysts now say an overemphasis on
technology has often been to blame. The secret of success may be to promote
human interaction, not technological interfacing (Deckmyn, 1999).
What has
happened in the past is many people have decided to put in Lotus Notes or a
search engine, but technology alone is not solving the problem. One approach
that has proved effective in many companies is to set up so-called communities
of practice, which allow workers to share knowledge on a particular topic such
as e-commerce. Information technology can provide the infrastructure to help
the groups communicate. But the main goal is to enable person-to-person
communication. The organisation must decide upfront whether strategy is based
on systems or on people interactions. This is a vital aspect that needs to be
addressed.
Learning Theory and Kolb’s Learning Style Inventory:
It is widely
accepted that while it is possible to identify common constituent elements, the
learning process varies at an individual level. Students will develop a way or style
of learning, and refine that style in response to three groups of factors:
unconscious personal interventions by the individual, conscious interventions
by the learner themselves, and interventions by some other external agent. The
term learning style only began to appear in the learning literature in the
1970s. One of the reasons put forward for the emergence of the term is that
learning style has a practical application, particularly in education and
training. Riding & Cheema (1991) suggest that it appeared as a replacement
term for cognitive style, and cognitive style is only part of an individual's
learning style. The term learning style indicates an interest in the totality
of the processes undertaken during learning. A learning style is:
"A complexus
of related characteristics in which the whole is greater than its parts.
Learning style is a gestalt combining internal and external operations derived
from the individual's neurobiology, personality and development, and reflected
in learner behaviour" (Keefe & Ferrell 1990, p. 16).
Learning style therefore relates to the general tendency towards a particular learning approach displayed by an individual.
Kelly (1997)
describes in his paper that in the early 1980's, Mezirow, Freire and others
stressed that the heart of all learning lies in the way we process experience,
in particular, our critical reflection of experience. They spoke of learning as
a cycle that begins with experience, continues with reflection and later leads
to action, which itself becomes a concrete experience for reflection (Rogers,
1996). For example, a teacher might have an encounter with an angry student who
failed a test. This is the experience. Reflection of this experience would
involve trying to explain it to oneself: comparing it to previous experiences
to determine what is the same and what is unique, analysing it according to
personal or institutional standards, and formulating a course of action
connected to the experiences of others, such as talking to other teachers who have
also faced angry students. Talking to other teachers, the action, will then
lead to further reflection.
Kolb further
refined the concept of reflection by dividing it into two separate learning
activities, perceiving and processing. (Algonquin, 1996) He thus added another
stage, called "Abstract Conceptualisation." Whereas in the Critical
Reflection stage we ask questions about the experience in terms of previous
experiences, in the Abstract Conceptualisation stage, we try to find the answers.
We make generalizations, draw conclusions and form hypotheses about the
experience. The Action phase, in light of his interpretation, then becomes a
phase of Active Experimentation, where we try the hypotheses out. (Kelly,1999)
Stages in the
cycles:
1.
Experiencing
or immersing oneself in the "doing" of a task is the first stage in
which the individual, team or organization simply carries out the task
assigned. The engaged person is usually not reflecting on the task as this
time, but carrying it out with intention.
2.
Reflection
involves stepping back from task involvement and reviewing what has been done
and experienced. The skills of attending, noticing differences, and applying
terms help identify subtle events and communicate them clearly to others. One's
paradigm (values, attitudes, values, beliefs) influences whether one can
differentiate certain events. One's vocabulary is also influential, since
without words, it is difficult to verbalize and discuss ones perceptions.
3.
Conceptualisation
involves interpreting the events that have been noticed and understanding the
relationships among them. It is at this stage that theory may be particularly
helpful as a template for framing and explaining events. One's paradigm again
influences the interpretive range a person is willing to entertain.
4.
Planning
enables taking the new understanding and translates it into predictions about
what is likely to happen next or what actions should be taken to refine the way
the task is handled.
The timing of
the LC is particularly important. If one waits until after a task is completed,
there is no opportunity to refine it until a similar task arises. However,
continual reflection leaves the person spending more time on thinking than
getting the task done--these must be balanced. In general, the learning cycle
should be used during initial framing of a problem to see whether past
experience may offer an approach; during natural breaks in tasking such as the
end of meetings or workdays; when progress is noticeably going well or poorly; or
when a crisis occurs that disrupts the process. The logic of the learning cycle is to make many small and
incremental improvements, which when done by many people, constitute major
improvements over time.
Type 1 (concrete, reflective). A
characteristic question of this learning type is "Why?" Type 1 learners respond well to explanations of
how course material relates to their experience, their interests, and their
future careers. To be effective with Type 1 students, the instructor should
function as a motivator.
Type 2 (abstract, reflective). A
characteristic question of this learning type is "What?" Type 2 learners respond to information presented
in an organized, logical fashion and benefit if they have time for reflection.
To be effective, the instructor should function as an expert.
Exhibit II: Kolb’s learning cycle
Type 3 (abstract, active). A
characteristic question of this learning type is "How?" Type 3 learners respond to having opportunities to
work actively on well-defined tasks and to learn by trial-and-error in an
environment that allows them to fail safely. To be effective, the instructor
should function as a coach, providing
guided practice and feedback
Type 4 (concrete, active). A characteristic
question of this learning type is "What
if?" Type 4 learners like applying course material in new situations
to solve real problems. To be effective, the instructor should stay out of the
way, maximizing opportunities for the students to discover things for
themselves
Henke(1996)
explains in his article how Kolb’s Learning Style Inventory, as defined by
(Kolb et al. 1979), includes four learning styles:
1.
Converger
who can be classified as someone who wants to solve a problem and who relies
heavily upon hypothetical-deductive reasoning...to focus on specific problems.
(Kolb et al., 1979)
2.
Diverger who
can be classified as someone who solves problems by viewing situations from
many perspectives and who relies heavily upon brainstorming and generation of
ideas. (Kolb et al., 1979)
3.
Assimilator
who can be classified as someone who solves problems by inductive reasoning and
ability to create theoretical models. (Kolb et al., 1979)
4.
Accommodator
who can be classified as someone who solves problems by carrying out plans and
experiments...and adapting to specific immediate circumstances. (Kolb et al.,
1979)
These
four style are based upon established learning theories as described by Kolb:
The ideas behind assimilation and accommodation originate in Jean Piaget’s
definition of intelligence as the balance between the process of adapting
concepts to fit the external world (accommodation) and the process of fitting
observations into the world of existing concepts (assimilation). Convergence
and divergence are the two essential creative processes identified by J.P.
Guilford’s structure-of-intellect model. (Kolb, 1985).
To determine a
person’s learning style, the person completes an instrument called
Learning-Style Inventory (Henke, 1999).
Validity of Kolb’s Learning Style
Inventory:
Since it was
first published in 1976, Kolb's Learning Style Inventory (LSI1) has been used
extensively in both academic and professional settings to identify the learning
style preferences of different groupings. Based on Kolb's theory of
experiential learning which posits varying degrees of preference for learning
involving concrete experience, reflection, abstraction and experimentation, the
LSI1 provided a relatively simple means of investigating learning style
preferences but was open to question on the basis of its validity and
reliability. Kolb (1985) responded to these concerns by publishing a revised
version of the Learning Style Inventory (LSI2) for which he claimed improved
reliability but to date, despite the fact that the LSI2 is widely used by
teachers and trainers, there have been few studies investigating its
reliability or validity. Since it was first published in 1976, Kolb's Learning
Style Inventory (LSI1) has been used extensively in both academic and
professional settings to identify the learning style preferences of different
groupings.
Willcoxson and Prosser(1995) studied the validity and reliability of the revised version of Kolb's (1985) Learning Style Inventory using the responses of students in an Australian university. Their study proves that this instrument has high reliability in terms of its internal consistency. There is also some evidence of validity but variation has been found on the basis of discipline.
Hypothesis
This whole
study is based on the problems that crop up when knowledge has been collected
but remains unused. The dissemination of information is one of the most
important aspects in Knowledge management. Most of the companies spend heavy
resources (financial and time) in mainly the activity of trapping information
and create databases that are the repository of all collected and documented
information. Organisations do not consider or study as to what kind of system
would be appropriate for their employees. There is no linkage drawn as to
whether an IT enabled database and search engines will be the right method of
not only helping knowledge transfer but also storing/acquiring it. My hypothesis builds on the theory of Kolb’s
learning cycle. Every person has his unique style of learning and falls between
the four categories. My hypothesis is that every learning style would dictate
certain modes of knowledge transfer and acquisition. People would prefer a
typical mode of communication and hence knowledge bases or retrieval system
should be calibrated in that fashion.
Kolb’s Learning Cycle and IT tools/ methods
The Converger
The Converger's
biggest strength is the practical application of ideas. The dominant learning abilities
are abstract conceptualisation and active experimentation. Engineers are a
typical example.
Hypothesis:
They would prefer to learn on the job and may even read help files,
finally develop enough understanding to operate. They would be good users of
only a few IT tools and would utilize their time with a computer only when the
need arises or for routine purposes. They would not mind using other people’s
idea or work as they are focussed on the practical side of things. Their
preferred style of knowledge would be to talk to the person rather than to put
it down in words (document it), mail, chat or any other mode.
The Diverger
The Diverger
has strengths which includes good imaginative ability that enables them to view
concrete situations from many different angles – good for brainstorming and
other activities intended to generate diverse ideas. Imaginative and emotional,
they like people. Research suggests that this style is characteristic of people
with a liberal arts or humanities background. Personnel managers often fall
into this category.
Hypothesis: These would tend to have a personal
interaction hence chatting would be more common than mailing. Anything that is
colourful and gives entertainment or related value would attract them to the
computer. They would be respond to and
even be adept in non-technical and graphical tools like PowerPoint, browsing
for fun, etc. In an organisation these would be the least interested in
searching huge databases and would rather prefer to call up or chat with the
knowledge carrier.
The Assimilator
The Assimilator
is good at inductive reasoning, assimilating many different observations into a
rational explanation. More interested in abstract concepts than people, the
validity of the theory is usually more important than its application. More
typical of the basic sciences than the applied sciences; such individuals are
found in research and planning departments.
Hypothesis: This person would be interested in
researching previous works and reviewing articles and papers on the web. The
time spent would be highly academic or research related. In their area of
interest they would go into different applications. They would go in depth and
finally prefer to develop their own. Once they take up a package or application
they will study it totally and hence will generally develop into topical
experts. They would be the right kind of people who would use search engines
and other technological tools effectively. Their least preferred mode would be
where human contact is enabled such as chat and email hence they would resort
to documenting knowledge and expecting the recipient read and assimilate it.
The Accommodator
The
Accommodator's strength lies in doing things – carrying out plans, taking
risks, experimenting and gaining new experiences. Accommodating to particular
circumstances in order to get things done, such people may well drop a theory
or plan when it does not fit the facts. With a background in technical or
practical fields (eg business), the accommodator is comfortable with people but
may show impatience. This action-oriented person can often be found in sales or
marketing.
Hypothesis: These people would be the first movers in utilising
information and would experiment with different tools and applications. They would
have a large number of bookmarks and indexes, as their experience would be
wide. They would normally be users of chat, as they do not have the time but
better users at impersonal and direct tool such as mails. In terms of knowledge and information, breadth
would be better than depth. They would have least botheration in copying or
transferring learning from other projects.
Research Methodology
The whole
research for this hypothesis was undertaken with a view that the students in
XLRI would be future managers and corporate citizens. The knowledge base used
by them is the archives and Internet apart from personal projects that can be
shared among batch mates. They have all the means for other modes of knowledge
transfer such as chat, mail and personal contact. Hence they comprise an
appropriate sample. There were two questionnaires used for the study. One was
the Kolb’s Learning style inventory. The other questionnaire framed by the
author was qualitative and not quantitative as the intention was to tap and
gather as much as possible about the aptitude and proclivity of students for a
particular tool and related aspects. The total number of students surveyed was
38 across the senior batch of both streams of Business Management and Personnel
Management. Students were randomly
selected to try and avoid biases. Nevertheless the sex and academic background
of respondents was taken into consideration before drawing the sample.
Education was
taken as either Engineering or Arts wherein all fields apart from engineering
were clubbed into Arts to mainly distinguish between the professional status
and different academic pattern of engineering and the rest.
Exhibit III: Respondents Profile
The
questionnaires supplied are provided at the end as appendix A and B. A few
questions that were not included in the questionnaire and clarifications was
asked in person or later by mail to all concerned. The first questionnaire that
was on Kolb learning style was used to differentiate respondents and the rest
of the analysis was done based on that.
Findings
The responses
based on Kolb learning style were quite satisfactory. Surprisingly, the numbers
were almost evenly spaced out. In fact it was not in total consonance with Kolb’s
prediction as many engineers were not convergers and so were a few other cases
but overall it mapped one-on-one with the established studies(literature) of
learning style and education or profession. The following is the details of the
learning styles of the sample :
The results
were slightly unexpected or not so in line with the hypothesis. Most of the
questions pertinent to the survey were taken and surveyed based on a scale
representing two ends or extremes as shown in the later part of the report. The
respondents were not told or reveled as to which of
these is the extreme.
The description
above was arrived at after detailing a few questions for each factor. The graph
below depicts the different styles mapped on the three main factors. Each person has been mapped and the above
graph is a reflection of the spectrum which arose and it tries to show how
members of individual style more or less clustered together. The graph is not a
qualitative support but a representation of how each style mapped.
The different
classifications have been described below:
A. Mode of information gathering (Qn # 08, 09
& 18): This describes
the respondents proclivity to a particular method when in need of information
or facing a descriptive problem related to knowledge. The graph signifies as to how the learning style
differentiates each groups preferred mode.
Convergers and Assimilators were in line with the
hypothesis as most of them preferred to explore on their won and search
information. If not, they would prefer to read literature on the topic before
they would want to communicate with someone else. Divergers on the meanwhile
were not in consonance with the hypothesis, as they too preferred to explore on
their own. Accommodators were the only group who preferred to talk to people
B. Mode of communication (Qn # 4 and
verbally): This is the
generally preferred method used by respondents to communicate. This was
gathered specifically in relation to accessing knowledge base and also in
general as to how they normally communicate. This was not related only to
academic matters but also to the other needs for communication. This was
analysed to see as to how much variance there would be between normal form of
communication and accessing information for a problem or information one
requires.
This factor was quite unlike to the hypothesis and
apart from assimilators most of the rest were specific that their preferred
mode of communication is ‘talk’. This
was not only in relation to any particular problem but even in general
discussions, some accommodators did prefer to mail. Somehow the majority of
assimilators veered towards mail. This depicts only the preferred mode and most
of them did prescribe chat or other tool as secondary mode hence the middle
point may not be much represented in the diagram.
C. Propensity to learn (Qn # 5, 6 & 07): This indicated the ability to learn IT
tools and packages. It was not only a reflection of pace of learning and
learning cycle but also an interest in the subject itself hence if a person was
spending more hours in the computer centre it was noticed that the learning
cycle was shorter.
Convergers and assimilators were quite the perfect
learners as they had a very small learning curve. There was a wide difference in
both the time and interest displayed between them and others. It was noticed
that convergers were the fastest learners even if the required application or
IT tools was a part of a course and not out of interest. This was also the
perception among other students (not from the sample set) when asked to name a
few from the sample who they think were experts and learn fastest on IT tools,
most of them were either assimilators or convergers. Accommodators were
slightly scattered and as an average they would lie somewhere in between both
the ends. Divergers were the least versatile or slowest on the tools and most
of them professed a distinct dislike for highly technical tools like SPSS.
Another factor
that was important and analysed was in relation to how the individuals would
impart or disseminate information (Qn. 19).
This revealed a skewed trend. Most of the divergers and assimilators
preferred to ask the person to read the project thoroughly and then entertain
questions. Accommodators were almost
clustered on this issue by expecting to talk to the person before expecting him
to read the report. Convergers were widely dispersed on this issue.
There were
other factors studied which were hypothesised to be decided by an individual’s
particular learning style. These were not proved or the results were not
revealing a clubbed series based on the learning style. The frequency to use
information technology related tools or the computer lab(Qn 3 &20) was an
aspect that did not follow any trend or group as per learning style. This was
surprising despite a great difference in learning curves. Similarly another aspect related to usage of
material from existing or related projects that was asked in the questionnaire
also showed diverse trends and did not follow the hypothesis. The rationale in
this case could be that under organisational circumstances where data mining
and knowledge transfer is expected from employees, the individual would
probably tend to use the resources whereas in management institution students could
have ethical dilemma or the yearning to learn on their own which restricts them
to use others work.
The responses
were also scattered on the question of different parameters affecting their
method of learning a particular IT tool (Qn 11). The answers were not as
hypothesised and the answers across respondents favoured a graphical interface
system. They did not feel any distinction based on help utilities or the
availability of better search facilities. It was also noticed that those who
had trouble (Qn 2) initially or a mental block were poor users or experts even
if they professed that these had been overcome. This did not show any
correlation with the learning style.
Some of the
questions posed were not related to the essence of the study nevertheless
revealed interesting answers. Almost 80% of the respondents felt that web or
computer based database system are much better in efficiency and convenience
than books as in physical libraries. This could be of importance as there are
organisations building volumes of literature as well besides online systems.
There was a broad consensus that the main folly of Internet based information
was credibility and format. This should not be a major problem in organisations
as the information would be mostly internal or validated. A few interesting
ideas were also raised in response to ways to improve retrieval systems which
falls out of the scope of this study.
Conclusion
There is
definitely a linkage between the learning style of an Individual and the kind
of information gathering tool used. Knowledge systems should definitely be
crafted by considering the learning style of their employees and factor in this
aspect. The study has revealed that this is true even at the stage of
information sharing and not only gathering. The research could not prove
linkage in a few other areas but as the discussion above has shown it is
especially in crucial area of mode of communication and desire for a particular
information-gathering tool.. Learning styles were clearly differentiating not
only how they would prefer to search and retrieve information but also as to
how they would impart. The research into the linkages and connection between
using learning style and usage of a particular IT tool or method has helped
identify newer issues
In the areas
where there was no great linkages between the two it was noticed that the
aspects raised by respondents was of crucial importance in spite of their
learning style. Also the trend towards preferring graphical interfaces and
effective search algorithms across styles could help in the design of knowledge
management systems. These aspects are definitely of useful importance as
discussed by other authors in the failure of current knowledge management
systems, a structural change based on learning style and preferences of people
in the organisation could yield better results and prove high on efficacy.
Limitation
The study was
conducted among students under the assumption that the conditions prevailing
were similar to the one that exists in an organisation and the students who
would be future corporate citizens would represent a right sample for the
exercise. This may not be true and the same assumptions may not hold good in an
organisation. In the course of the survey it was revealed that the same set of
respondents might act differently as the need and reasons for using the
dissemination and retrieval tools are not the same here as they would be in an
organisation. The questionnaire was
also a qualitative one that was administered to a small batch of students. The
selection of students was done on certain premises to avoid biases. This aspect
of removal cannot be totally ensured unless the batch size is increased. Due to
the exploratory and qualitative nature of the questionnaire useful statistical
tools couldn’t be administered. There are different instruments to measure
learning style and this study I s based on only one, a review of these other instruments might help
determine if one instrument is superior over another instrument in determining
learning styles and also implementing aspects of learning styles into knowledge
management systems.
References
1. Baets R J Walter(1998), Organisational Learning and Knowledge technologies in a Dynamic Environment., Kluwer Publishers, 1998.
2. Cash J, Applegate L & Mills Q(1998), Information Technology and Tomorrow’s Manager, Harvard Business Review, Nov-Dec.
3. Kelly Curtis (1997), David Kolb- The Theory of Experiential Learning and ESL, http://www.aitech.ac.jp/~iteslj/Articles/Kelly-Experiential/
4. Deckmyn Dominique (1999), Human Interaction Key to Knowledge, www.computerworld.com/cwi/story/0,1199,NAV47_STO37149,00.html
5. De Geus A., Planning as learning, Harvard Business Review, Mar’88
6. Hayes, J. & Allinson, C.W. The implications of learning styles for training and development: a discussion of the matching hypothesis. British Journal of Management, 7 pp. 63-73, 1996.
7. Henke Harold (1996), Learning Theory: Using Kolb's Learning Style Inventory with Computer Based Training, http://www.chartula.com/ learnthy.htm.
8. I Nonaka & H Takeuchi (1996), Theory of Organizational Knowledge Creation, International Journal of Technology Management, Vol 11.
9. I Nonaka (1994), Dynamic theory of organisational knowledge creation, Organisational science, Vol 5.
10.
Kolb, D.A. (1985), LSI
Learning-Style Inventory, McBer & Company, Training Resources Group
11.
Riding, R. & Cheema, I. (1991), Cognitive
styles - an overview and integration, Educational Psychology, 11 (3 &
4) pp. 193-215
12.
Schwardt & Marquardt (2000), Organisational
Learning, St. Lucie.
13.
Senge P., Art and Practice of
Learning Organisation, Double day.
14.
Smith, Burgoyne and Aranjo (1999), Organisational
Learning and the Learning organisation, Sage.
15. Willcoxson Lesley and Prosser Micheal (1995), Kolb's learning style inventory (1985): Review and further study of validity and reliability,. http://cleo.murdoch.edu.au/asu/staffdevt/pubs/lesleyw/bjedpsych96.html,.
16. Phuong N. Pham, Learning Style, http://payson.tulane.edu/ppham/Learning /lstlyes.html
Appendix
–A
Kolb's Learning Style Inventory
There are nine set of words and phrases listed below. In each set, please rank the word or phrases according to following scale:
4 = best describes your style of functioning
3 = next best characterises your style of functioning
2 = next most characteristic word or phrase, and
1 = least describes your style of functioning
There are no right or wrong answers. Be sure to assign a different rank to each of the four words or phrases in each set. Do not make a tie.
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1.
I would describe myself as: |
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a_____ |
discerning |
b_____ |
tentative |
c_____ |
involved |
d_____ |
practical |
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2.
In my work, I aim to be: |
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a_____ |
Receptive |
b_____ |
relevant |
c_____ |
analytical |
d_____ |
Impartial |
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3.
I spend my time on: |
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a_____ |
feeling and experiencing |
b_____ |
watching and observing |
c_____ |
thinking and analysing |
d_____ |
doing and performing |
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4.
I can be best described as: |
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a_____ |
accepting |
b_____ |
Risk-taker |
c_____ |
evaluative |
d_____ |
aware |
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5.
While taking decisions, I am: |
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a_____ |
intuitive |
b_____ |
Productive |
c_____ |
logical |
d_____ |
questioning |
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6.
In my problem-solving style, I am: |
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a_____ |
abstract |
b_____ |
observing |
c_____ |
concrete |
d_____ |
active |
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7.
In my behaviour and activities, I am: |
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a_____ |
present-oriented |
b_____ |
reflecting |
c_____ |
future- oriented |
d_____ |
pragmatic |
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8.
I appreciate and value: |
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a_____ |
openness to experience |
b_____ |
keenness of observation |
c_____ |
analysis and concepts |
d_____ |
action and experimentation |
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9.
My relationships can be best described as: |
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a_____ |
intense |
b_____ |
reserved |
c_____ |
rational |
d_____ |
responsible |
Your Style of Functioning
(scoring key)
1. Please write down your ranks in the following matrix.
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ITEMS |
a (CE) |
b (RO) |
c (AC) |
d (AE) |
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1. |
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* |
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* |
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2. |
* |
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* |
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3. |
* |
* |
* |
* |
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4. |
* |
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* |
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5. |
* |
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* |
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6. |
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* |
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* |
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7. |
* |
* |
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* |
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8. |
* |
* |
* |
* |
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9. |
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* |
* |
* |
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TOTAL |
CE= |
RO= |
AC= |
AE= |
2. Add up the totals (column-wise) of your rank in those cells which have a “*” sign
3. Compute the values of “x” and “y” using the following formulae:
x = AC - CE =
y= AE - RO =
Your Style of Functioning
(Plot your Scores)
4. Use your “x” and “y” scores to plot your style in the following graph:
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y-axis AE |
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Accommodators |
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+9 |
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Convergers
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+8 |
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+7 |
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+6 |
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+5 |
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+4 |
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x-axis CE |
-3 |
-2 |
-1 |
0 |
1 |
2 |
+3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
AC |
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+2 |
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+1 |
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0 |
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-1 |
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-2 |
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-3 |
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Divergers |
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-4 |
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Assimilators |
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RO |
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Appendix
–B
IT Tool Questionnaire:
1) When did you first get trained or acquainted with a) computers b) internet?
2) Did you have any block or problem anytime regarding the usage or skills involved in computers and where you able to overcome this later?
3) How often do you use the computer centre - hrs per day on an average?
4) For what purposes do you use the CC ? Kindly give the break up of time you spend ( % would be sufficient)
a) Browsing for fun incl. news, games, etc.
b) Browsing Internet /Archives for information incl. Articles, projects, etc
c) Using MS Office/ SPSS package for presentations, projects etc.
d) Checking mail
e) Chatting
5) How familiar are you with (kindly describe your familiarity):
a) Internet:
b) MS-Office excl SPSS:
c) SPSS and Excel:
6) In which of the above or other tools would you consider yourself as an expert
7) How fast were you able to learn the basics in each of the se and move on to advanced features
a) MS-Office incl. PowerPoint
b) SPSS
c) Internet
8) Do you take the help of your classmates when you are are starting off on a new IT tool or in doubt?
9) Do you feel comfortable using previous works/ knowledge/projects or do you feel it is unethical?
10) To what extent would you copy the content and other aspects on a scale of
1 - 7 (7 being the highest):
11) Is there a great difference in the way you learnt and mastered different IT packages/tools based on the below parameters or any others
a) Graphical content e.g. Internet Vs Archives
b) Availability of better search tools e.g. Find (Windows) Vs Google (Internet)
c) Help utilities available e.g. SPSS Vs Excel
12) Could you compare the efficacy/time of searching the library and the net for similar resources and identify the factors which make the difference?
13) Do you estimate some particular individuals / classmates as better at internet and other knowledge bases in comparison to others? If so, kindly identify what you feel as contributes to that difference?
14) In what aspects do you feel the search facility could be improved to enable better retrieval and identification of information?
15) When you come across a new software package or tool, what do you do –
(a) expect someone else to teach you (b) start working to find out what it does or (c) go through the help manual in detail? Differentiate
16) What are the aspects that you feel will make Internet and other knowledge bases more comfortable or convenient like catalogues in libraries?
17) Would you attend or listen if you were provided inputs or given coaching by tutors or peers who are experts on IT tools used by you or related to them?
18) If you found a classmate or colleague who had done a similar project you are on, how would you go about it- Read the report, talk to the person, send him mail?
19) How would you prefer to impart if it were you who had done the project that someone else wants now? Mail, chat, Ask him/her to read it or talk? Describe.
20) How do you communicate in general and for what purposes do you use the following media?
Chat
Internet