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Modeling competencies for supporting work-integrated learning in knowledge work


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Purpose – The purpose of this paper is to suggest a way to support work-integrated learning for knowledge work, which poses a great challenge for current research and practice. Design/methodology/approach – The authors first suggest a workplace learning context model, which has been derived by analyzing knowledge work and the knowledge sources used by knowledge workers. The authors then focus on the part of the context that specifies competencies by applying the competence performance approach, a formal framework developed in cognitive psychology. From the formal framework, a methodology is then derived of how to model competence and performance in the workplace. The methodology is tested in a case study for the learning domain of requirements engineering. Findings – The Workplace Learning Context Model specifies an integrative view on knowledge workers' work environment by connecting learning, work and knowledge spaces. The competence performance approach suggests that human competencies be formalized with a strong connection to workplace performance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosis and competency gap analysis can be embedded into the normal working tasks and learning interventions can be offered accordingly. The results of the case study indicate that experts were generally in moderate to high agreement when assigning competencies to tasks. Research limitations/implications – The model needs to be evaluated with regard to the learning outcomes in order to test whether the learning interventions offered benefit the user. Also, the validity and efficiency of competency diagnosis need to be compared to other standard practices in competency management. Practical implications – Use of competence performance structures within organizational settings has the potential to more closely relate the diagnosis of competency needs to actual work tasks, and to embed it into work processes. Originality/value – The paper connects the latest research in cognitive psychology and in the behavioural sciences with a formal approach that makes it appropriate for integration into technology-enhanced learning environments.
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Modeling competencies for supporting
work-integrated learning in knowledge
Tobias Ley, Armin Ulbrich, Peter Scheir, Stefanie N. Lindstaedt, Barbara Kump and
Dietrich Albert
Purpose The purpose of this paper is to suggest a way to support work-integrated learning for
knowledge work, which poses a great challenge for current research and practice.
Design/methodology/approach – The authors first suggest a workplace learning context model, which
has been derived by analyzing knowledge work and the knowledge sources used by knowledge
workers. The authors then focus on the part of the context that specifies competencies by applying the
competence performance approach, a formal framework developed in cognitive psychology. From the
formal framework, a methodology is then derived of how to model competence and performance in the
workplace. The methodology is tested in a case study for the learning domain of requirements
Findings – The Workplace Learning Context Model specifies an integrative view on knowledge workers’
work environment by connecting learning, work and knowledge spaces. The competence performance
approach suggests that human competencies be formalized with a strong connection to workplace
performance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosis
and competency gap analysis can be embedded into the normal working tasks and learning
interventions can be offered accordingly. The results of the case study indicate that experts were
generally in moderate to high agreement when assigning competencies to tasks.
Research limitations/implications The model needs to be evaluated with regard to the learning
outcomes in order to test whether the learning interventions offered benefit the user. Also, the validity
and efficiency of competency diagnosis need to be compared to other standard practices in
competency management.
Practical implications Use of competence performance structures within organizational settings has
the potential to more closely relate the diagnosis of competency needs to actual work tasks, and to
embed it into work processes.
Originality/value The paper connects the latest research in cognitive psychology and in the
behavioural sciences with a formal approach that makes it appropriate for integration into
technology-enhanced learning environments.
Keywords Competences, Learning, Workplace learning, Knowledge management
Paper type Research paper
Knowledge workers are workers whose critical work resource within their essential
value-creating tasks is knowledge (Drucker, 1994). In order to enhance the productivity of
knowledge workers, their competency enhancement and learning has to take place directly
at their workplaces. According to Haskell (2001), work-integrated learning aims at fostering
the learning transfer (i.e. the application of what has been learnt to current job activities).
Work-integrated learning does not merely rely on pre-defined development plans and on
learning resources, which are specifically designed and produced for dedicated learning
situations. Work-integrated learning instead mainly considers knowledge workers’ actual
tasks, personal competency disposition and work domain as being relevant for deriving
DOI 10.1108/13673270810913603 VOL. 12 NO. 6 2008, pp. 31-47, QEmerald Group Publishing Limited, ISSN 1367-3270
Tobias Ley, Armin Ulbrich
and Stefanie N. Lindstaedt
are based at the
Know-Center, Graz, Austria.
Peter Scheir and
Barbara Kump are based at
the Knowledge
Management Institute, Graz
University of Technology,
Graz, Austria.
Dietrich Albert is based at
the Department of
Psychology, University of
Graz, Graz, Austria.
current learning needs. Work-integrated learning also seeks to reuse the results of the work
task as learning material.
Knowledge work operates in a constant tension between personal goals and organizational
constraints. On the one hand, knowledge workers increasingly learn in an informal and
self-directed manner (Pinchot and Pinchot, 1996). In our approach to workplace learning,
this is addressed by offering knowledge workers easy access to relevant knowledge
artifacts and persons in a workplace learning environment, and thereby giving them
considerable freedom to work and learn in a self-directed manner. On the other hand,
aligning learning to organizational goals and task requirements is an important factor. This
even poses challenges for traditional personnel development instruments and trainings.
How this alignment can be addressed within knowledge work remains an open issue
(Elkjaer, 2000). In order to address these organizational issues, we look at the context in
which the knowledge worker operates. The context is made up of several elements that
address aspects of the task and the organizational setting (such as the process or domain
the person currently works in, and the competencies required for performing the work).
The purpose of the current paper is first to suggest a model of a person’s context with regard
to workplace learning in order to better understand the relevant constituents that make up a
user’s work environment and the relationships between them. We then focus on one element
of the user context, namely the competencies required to perform the work successfully.
Competencies are used as a guide to suggest relevant learning interventions. The paper
then presents a formal model for competencies and suggests a methodology of how
competencies can be modelled in a specific organizational case. A case study illustrates the
methodology and provides a possibility for testing its applicability.
Context in workplace learning
Tight integration of working and learning in a workplace learning environment relies on a
clear computer-interpretable conception of what content the material in question actually
conveys. A clear conception can be provided by annotating material with metadata taken
from a sufficiently expressive model base (Sicilia, 2006). For reuse purposes, metadata is
analyzed during the process of assembling learning material. Assembling learning material
not only consists of selecting mere content, but also of putting it into new usage contexts and
the contexts of other material presented (Heiwy, 2006). To integrate working and learning,
far-reaching strategies need to be considered. This is especially true for the technological
foundations (Lytras and Pouloudi, 2006) and the metadata models to be developed or
chosen. In order to allow for the greatest flexibility in reusing material, often a number of
different metadata models are chosen. In this case measures need to be taken in order to
align different models with each other (Cruz et al., 2004). In order to consider the topics
raised by the aforementioned modeling issues, we propose a formal model that connects
competency development and work execution and is in alignment with metadata used for
annotating learning material.
We refer to the context in which a person operates as the ‘‘workplace learning context’’. It is
comprised of information relevant to a person’s workplace. Given the case of a typical
IT-based workplace of a knowledge worker, the workplace learning context needs to take
care of the at least three conceptual spaces that are considered to make up the workplace
(Lindstaedt and Farmer, 2004):
1. the work space;
2. the learning space; and
3. the knowledge space.
These spaces are unified conceptually using the abstraction of the so-called ‘‘3spaces’’.
Each of the spaces of the 3spaces provides specialized tools and structures supporting one
of three roles a knowledge worker typically fills at the professional workplace (Figure 1):
VOL. 12 NO. 6 2008
APOSDLE is partially funded
under FP 6 of the European
Community within the IST
Workprogramme (Project
Number 027023). The
Know-Center is funded within
the Austrian COMET
(Competence Centers for
Excellent Technologies)
Program under the auspices of
the Austrian Ministry of
Transport, Innovation and
Technology, the Austrian
Ministry of Economics and
Labor and the State of Styria.
COMET is managed by the
Austrian Research Promotion
Agency FFG.
1. Worker This role is typically supported by the work space. The work space contains
work-relevant tools and resources. It is structured according to work processes and
2. Learner This role is typically supported by the learning space. The learning space
contains resources and measures for an individuals’ competencies acquisition. The
learning is structured according to learning topics but does not provide information about
the relationship between work tasks and learning resources that contribute to
competency development.
3. Expert The expert role is typically supported by the knowledge space. The knowledge
space represents the knowledge that is stored within organizational memories and covers
structuring, relationships and semantics of that knowledge.
It should be noted that all three individual spaces of the 3spaces are normally structured
differently (mutual structural disconnection) and are implemented by heterogeneous
systems (mutual technical disconnection) (Lindstaedt and Farmer, 2004).
In order to support work-integrated learning, all three roles that a knowledge worker can fill
need to be supported. The workplace learning context mentioned above serves to wrap up
all three roles conceptually and also serves to integrate them in an unified model, which in
the following is referred to as the ’’Workplace Learning Context Model’’. The Workplace
Learning Context Model represents the three roles of a knowledge worker and provides a
mapping from the 3spaces onto a unified context model. The Workplace Learning Context
Model thus spans the user’s work context, the competency profile and the current
knowledge domain in focus.
The Workplace Learning Context Model
A comprehensive workplace learning context model needs to cover a broad knowledge
spectrum from organizational knowledge to knowledge of individual employees. Because
employees are the main target and beneficiaries of the model, modeling the competencies
of knowledge workers is a key requirement for providing appropriate support. Knowledge
workers can be regarded as individuals that generate, operate on, require and manipulate
organizational knowledge and act within this broader organizational knowledge context.
Therefore, the development of models that deal not only with individual knowledge but also
with the organizational knowledge context is crucial. Organizational knowledge can be
regarded to consist of procedural and declarative aspects (Hartlieb, 2000). This distinction
is of the highest importance, especially in the light of latest research on process-oriented
(procedural; see, for example, Remus, 2002) and ontology-based (declarative; see, for
example, Gronau et al., 2003) knowledge management as well as in the light of the latest
standardization efforts in industry, which include, for example, standards for process
Figure 1 The context of a knowledge worker
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management (International Organization for Standardization, 2000). Each of the spaces
from the 3spaces introduced above can be mapped onto one of these aspects of
Bindividual knowledge finds its representation in the learning space, since both of them
cover the acquisition of competencies;
Bprocedural organizational knowledge is related to the working space; and
Bdeclarative organizational knowledge is related to the knowledge space.
Based on this argument, this contribution introduces the three-dimensional Workplace
Learning Context Model for conceptualizing a knowledge worker’s context, which consists of
individual (competency), procedural (work) and declarative (knowledge) spaces, each of
which is taken care of in its own dedicated package, i.e.:
Bthe competency package;
Bthe process package; and
Bthe domain package.
The competency package
Our attention focuses on the human user who has several attributes, among which her
competencies appear to be rather important. The model in Figure 2 (in UML notation) shows
that competencies are assigned to users. In this simple model, competencies are structured
according to relationships, which are called ‘‘surmise’’. Thus it is assumed that a hierarchical
relationship between competencies exists, where simpler competencies are assumed to be
predecessors of competencies of higher complexity. We will show later that, despite the
simplicity of the approach, this is in line with the formal competency framework we are
introducing with this contribution.
With the competency package we are covering those aspects of a knowledge worker’s
context that are related to her personal knowledge. In Van Elst et al. (2001), personal context
information is created from stereotype role-based information and individual details.
Individual details comprise a person’s task-specific skills, which can be used for identifying
knowledge needs when a person is about to perform a certain task. Schmidt (2004) gives an
introduction to the Learning in Process (LIP) project[1]. In this approach, learning resources
are compiled and presented according to the results of a competency gap analysis. Sicilia
(2005) presents a thorough and detailed introduction into modeling ontologies for
competency management. After an investigation of existing models and schemas for
competencies, the major drawbacks of previous research are identified and formulated as
follows. One drawback of most existing approaches is that often too little consideration is
Figure 2 Competency assigned to a user modeled as a UML diagram
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dedicated towards the semantics of the composition of competencies. Another drawback is
that many approaches do not distinguish between competencies inherent to human beings
and observable activities which have been executed during work tasks (Ley et al., 2007).
With the formal competency framework to be introduced below, we show how to overcome
these drawbacks.
The process package
Contrary to many existing process modeling approaches (e.g. Gronau et al., 2003; Scheer,
2000), the intention of this meta-model is not to model sequences of tasks with the purpose
of giving a normative description of work (e.g. Hollingsworth, 1995). As we are dealing with a
learning environment, the focus is not on supporting work execution in the sense of routing
tasks and/or task-related resources, but to identify and deliver available resources that
support a knowledge worker with respect to the competencies required to execute her task.
In Figure 3, the classes of the process package are presented as well as their relations within
the process package and between the process and competency package. A task is
potentially composed of sub-tasks. To each task (or sub-task respectively) a number of
resources may be assigned, which either are needed for task execution (relationship
‘‘uses’’) or produced during task execution (relationship ‘‘creates’’). A task is executed by a
role, which is impersonated by an actor. An actor from the process package is semantically
equivalent to a user from the competency package.
The domain knowledge package
The purpose of modeling the domain knowledge is to represent the environment the
knowledge worker operates in. Hence we aim at conceptualizing those entities of the
worker’s domain that are relevant for work-integrated learning and modeling the relations
between the domain’s concepts by defining semantic dependencies between them using
ontologies (Gruber, 1993). The semantic metadata created (i.e. the concepts in the
Figure 3 Classes from the process package and their relationships within the package as
well as across the boundaries between the process package and the competency
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ontology) is applied to artifacts present in the organizational memory. This is done to provide
a way for the later retrieval of information items relevant for the current work situation.
Artifacts are resources stored within organizational databases. Artifacts are semantically
equivalent to resources from the process package (see above). To each artifact, a number of
concepts from a domain-specific ontology are assigned in the form of metadata. Concepts
serve to describe the content of the artifacts by semantic annotations. Consequently,
resources are placed within an ontology and can be found and retrieved using ontological
concepts as search terms. Additionally, there is a close relationship between concepts and
competencies from the competency package: concepts are used for labeling
competencies. Hence, the concept(s) used for labeling competencies can be used as
search terms for retrieving resources.
Nevertheless, when analyzing the Workplace Learning Context Model closely it appears that
it is still not possible to conclude clearly what competency need a user actually has in a given
task: the model as it is now only allows searching for resources utilizing competencies
and concepts as search terms but does not allow a competency need to be inferred. In order
to close this gap, a framework and methodology are introduced and presented hereafter
which serve to lay out a conceptual foundation and a comprehensive methodology for
inferring competencies needed based on a user’s past performance with respect to her work
Competencies in workplace learning: a competence performance conception
For purposes of the present paper, we focus on the competency package introduced above,
its relationships to the other packages, and how it can be utilized in a workplace learning
environment. Specifically, our goal is to suggest ways in which a competency gap can be
(semi-)automatically inferred from a comparison of a person’s task performance in the past,
and the tasks she is about to tackle in the future.
For illustration purposes, the first section presents a scenario of how we envision the use of
competencies in a workplace learning environment. In the second section, potential
problems of how competencies are traditionally being used in the context of workplace
learning are identified. Thirdly, we introduce a formal framework for competency
management which specifies the formalization sketched out in the previous sections and
which helps to alleviate some of the problems. The framework is based on a close
connection between competencies and task performance. The framework informs an
implementation method which is then introduced in the subsequent section and illustrated
by means of a case study.
A scenario for performing a competency gap analysis
This section introduces a scenario which illustrates the purpose of our approach. As
mentioned above, the aim of the approach is to calculate a competency gap from a
comparison of tasks performed in the past to those tasks to be tackled in the future. This
analysis needs two steps. As a first step, a workplace learning environment needs
information about the tasks that have been performed in the past and from this derives the
competencies the person has available.
Laura is a requirements engineer. She works for a medium-sized software consultancy. In her
past job activities she was often concerned with the very early stages of requirements projects in
which a rough conceptualization of the system boundaries are usually sketched in a first rough
context model. She has performed tasks such as ‘‘1.1 Build a first cut Context Model to identify
System Boundaries’’ and ‘‘1.2 Carry out an initial stakeholder analysis’’ which are specified in the
company’s process model. Laura’s history of task executions is stored in her user profile. As the
tasks in the process model are related to the competencies needed, the user profile has
automatically determined that Laura has available the following competencies: ‘‘B Knowledge of
different types of system stakeholders’’ and ‘‘C Knowledge of building Context Models’’.
As a second step, the environment needs to know which tasks the person is required to
perform in the future and relate competencies needed in these tasks to those the person has
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Laura has recently been assigned to a large project in which she is required to perform a more
formal approach to modelling the context of the system. This requires her to embark in task ‘‘1.4
Allocate functions between actors according to boundaries’’. The workplace learning
environment detects that according to her user profile she is lacking the competency ‘‘A.
Knowledge about actors, tasks, goals and resources’’. As a result, the environment presents her
some training material which explains these concepts and gives examples of how they are
Traditional approaches to the use of competencies in workplace learning
In past research, competency frameworks have often been advocated as a way to deal with
the challenges in human resource development (Green, 1999; Lucia and Lepsinger, 1999;
Erpenbeck and Rosenstiel, 2003). In these frameworks, competencies are being used to
more closely relate learning to organizational requirements, such as organizational strategy,
goals or task requirements. As reviews such as the one by Lehner (2004) show, most of the
current approaches have been implemented using a centralized database infrastructure.
The suitability of such approaches for more dynamic and knowledge-based workplaces,
such as the one from the scenario in the previous section, has recently been called into
question (Athey and Orth, 1999; Ley et al., 2007). A special problem is the disconnection
between competency descriptions and the actual task performance. This disconnect leads
to unclear meanings of the competency descriptions and therefore to unclear assessment
results. It also means that assessment is not connected to the working situations in which
competencies are being applied.
Ley et al. (2005) have suggested that the competence performance approach (introduced
by Korossy, 1997) serve as a model to formalize competencies and their connection to
workplace performance for work-integrated learning. This approach will be presented in the
next section. As we will see, the competence performance approach provides a framework
that formalizes the relationship between competencies and the tasks specified in the
process package of the Workplace Learning Context Model (see above).
The competence performance approach
With the competence performance approach, Korossy (1997, 1999) has introduced an
extension of knowledge space theory (Falmagne et al., 1990; Doignon and Falmagne,
1999). Knowledge space theory was developed in the 1980s and 1990s as an attempt to
model a person’s knowledge state as closely as possible to observable behavior. It is
predominantly concerned with the diagnosis of knowledge and has been applied in
adaptive testing and tutoring systems (e.g. ALEKS Corporation, 2003; Hockemeyer et al.,
1998). The fundamental idea of knowledge space theory is that a person’s knowledge state
in a certain domain can be understood as the set of problems this person is able to solve.
Since solution dependencies exist among the problems, it is possible to present a person
only a subset of all problems of a domain in order to diagnose his/her knowledge state. The
collection of all possible knowledge states is called a ‘‘knowledge space’’. A knowledge
space is a partial order and is stable under union.
In an attempt to develop knowledge space theory further, Korossy (1997) suggests that in
addition to the set of problems, one should look at the set of competencies – i.e. the
knowledge, skills and abilities needed to solve the problems. This would give information
‘‘ Work-integrated learning mainly considers knowledge
workers’ actual tasks, personal competency disposition and
work domain as being relevant for driving current learning
needs. ’’
VOL. 12 NO. 6 2008
on the reasons for different levels of performance, and thereby help to suggest learning
measures. Similar to the set of questions, competencies are also structured in a competence
space that results from a surmise relation on the set of competencies.
The relationship between the two sets (questions and competencies) is formalized by an
interpretation function which maps each problem to a subset of competence states which
are elements of the competence space. This subset of competence states contains all those
competence states, in each of which the problem is solvable. The interpretation function
induces a representation function, which assigns to each of the competence states all
problems that are solvable in that competence state. Which problems are solvable is
determined by the interpretation function.
The competence performance approach has been applied in technology-enhanced
learning applications. For example, Hockemeyer et al. (2003) have assigned ‘‘competencies
required’’ and ‘‘competencies taught’ ’ as metadata to a collection of learning objects.
Thereby, prerequisite structures are derived for the e-learning content which allow for
adaptive tutoring. New course content could easily be integrated, as metadata was only held
Applying the competence performance approach to workplace learning
An application of the competence performance approach needs to take into consideration
the following three concepts:
1. performance;
2. competencies; and
3. the interpretation function.
The term ‘‘performance’’ is understood to encompass all behaviors relevant for the
accomplishment of a certain task in a specific situation (Schmitt and Chan, 1998). In the
current approach, performance encompasses the set of all tasks that employees perform in
the workplace. Each task that has been performed can be judged according to whether it
was successfully or unsuccessfully performed. As defined here, the concept of performance
has a close relationship to the tasks specified in the process package. For modeling
competence performance structures (see the next section), we rely on the set of tasks
specified in the process package. For assessment purposes (i.e. which employee has which
competency), we rely on the instantiations of the tasks to determine which tasks have been
performed by whom.
In the current approach, we define competencies as the personal characteristics of
job-holders that they bring to bear in different situations. Competencies are hypothetical
constructs that determine performance in a job. The set of competencies encompasses all
knowledge, skills, abilities and other characteristics that are needed to successfully perform
in the tasks. Competencies may be differentiated into knowledge, skills and other
characteristics (KSAOs) (Lucia and Lepsinger, 1999; Schmitt and Chan, 1998). As can be
seen from Figure 4, competency descriptions are linked to the domain ontology.
For formalizing the relationship between competencies and performance, the method of a
competence performance matrix is used. This matrix assigns to each task all competencies
needed to perform that task. This matrix thereby provides the interpretation function in the
sense of Korossy, and a competence performance structure can be derived from it.
The matrix can be included within the Workplace Learning Context Model as shown in
Figure 5. It serves as a connecting element within the competency package.
The newly added class task within the competency package (above) is semantically
equivalent (but not identical) with the task-class from the process package. For practical
reasons, we usually restrict the set of tasks used to construct competence performance
structures to those tasks specified in the process package. In certain cases, it might make
sense to include additional tasks, or to leave out certain tasks that are not relevant for
VOL. 12 NO. 6 2008
The concept of the competence performance matrix as well as the way to derive
competence performance structure is elaborated in the next section.
Modelling competencies: a methodology and a case study
This section introduces the methodology we use to model competencies within the
competence performance framework. According to Ley and Albert (2003a), the
methodology entails the following steps:
Bderive a set of tasks (performance) for the position in question, and for the learning
domain to be supported;
Bdetermine competencies needed to successfully perform the tasks; and
Brelate tasks and competencies (competence performance matrix).
These three steps focus on the process ‘‘defining competencies’’ mentioned in the overall
organizational competency management process presented by Ley et al. (2007).
The methodology has already been applied in different settings (i.e. in the automotive
industry and in a research-based setting). We have recently conducted a further case study
focused more directly on supporting workplace learning. We briefly introduce this case
study here. It will then be used to illustrate the procedure employed for deriving competence
performance structures in the subsequent sections.
A case study of requirements engineering
The case study has been conducted as part of the APOSDLE project[2]. The goal of
APOSDLE is to create a process-oriented learning environment which supports knowledge
workers to work and learn at the workplace. The learning domain for a first prototype is
requirements engineering (RE). The learning environment targets persons with various
levels of expertise in RE. They may be domain experts with little knowledge of RE who have
been made responsible for eliciting requirements for a system to be built, or RE specialists
who need only little guidance to conduct RE projects.
Specifically, we use the RESCUE process (Requirements Engineering with Scenarios in
User-Centered Environments; see Maiden and Jones, 2004). RESCUE is an innovative
process developed for the elicitation and specification of requirements for socio-technical
systems. RESCUE supports a concurrent engineering process in which different modeling
and analysis processes take place in parallel: human activity modeling is done to provide an
Figure 4 Classes from the domain knowledge package and their relationships within the
package as well as across boundaries between the domain knowledge package,
the process package and the competency package
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understanding of how people work in order to establish a baseline for possible changes. The
aim of system goal modeling is to model the future system boundaries and dependencies
between actors for goals to be achieved. The goal modeling is formalized with the i* notation.
Use case modeling is the process of writing use cases for the future system, exploring it with
stakeholders and carrying out impact analyses in order to obtain consistent and valid
requirements. These sub-processes are aligned at designated synchronization points.
Figure 5 The Workplace Learning Context Model
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During the whole elicitation process, RESCUE provides guidance on requirements
management. Furthermore the use of creativity workshops encourages requirements and
design ideas to be discovered and elaborated together.
The purpose of the case study is to construct a competence performance structure for the
domain of RE, and specifically for the RESCUE process. The structures should then be used
to automatically predict a person’s competence state from the kind of tasks successfully
performed in the past, to predict performance on future tasks, and to suggest learning
resources that can be of help when facing new tasks.
Deriving a set of tasks
The tasks can be derived from a detailed analysis of the work to be performed in the chosen
domain. It is important that tasks reflect the learning domain in question well, and that
performance in these tasks can be assessed with regard to some quality criteria which are
agreed within the organization (i.e. whether a task has been performed well or poorly).
We have previously employed hierarchical task analysis to find tasks employees perform in a
certain position (Ley and Albert, 2003b). In Ley and Albert (2003a), we chose documents
produced by the workforce as a way to reflect the more dynamic nature of the tasks.
In the present case study, the set of tasks is rather easily obtained as extensive
documentation exists for the work to be performed in RESCUE. The set of tasks was derived
by means of a detailed content analysis of the RESCUE process document (Maiden and
Jones, 2004). We focused on the two streams of human activity modeling (HAM) and system
goal modeling (SGM). As a result, a first list of tasks was obtained for these two streams and
later reviewed by the authors of the RESCUE process. The final list of tasks was composed of
29 tasks in the HAM stream, and 18 tasks in the SGM stream.
Deriving competencies needed
When eliciting the competencies needed, we rely to a large extent on techniques for eliciting
knowledge from domain experts with structured interviews or questionnaires. For instance,
Ley and Albert (2003a) used the repertory grid technique to elicit competencies from
documents which experts had written in the past. In the present case study, a first
open-ended interview was held with the two RESCUE experts mentioned above. We
considered the tasks obtained in the previous step and asked the experts to name the
competencies (knowledge and skills) needed to perform well in these tasks. The interview
data obtained was then complemented with data derived from analysis of existing
documented sources from related research, such as Van den Berg (1998) and the National
O*NET Consortium (2005). From these sources, an extensive list of competencies was
obtained, cross-checked for consistency and then validated with the RESCUE experts. In
total the list consisted of 33 competencies.
To exemplify the procedure, we selected a subset of tasks to be achieved in the sub-process
of system goal modelling. Table I shows the lists of tasks and competencies selected for our
Constructing competence performance structures
To build the interpretation function, the experts were asked to assign to each task those
competencies they regarded as mandatory for successfully accomplishing the respective
tasks. This was done by means of a task competency matrix (see Ley and Albert, 2003a). In
the present case, the experts were asked to give their assignments independently from each
other. This way, agreement can be measured as one way to evaluate the methodology and
the resulting structures (see below). In continuing the example form above, Table II gives the
results of this assignment. The crosses in the matrix indicate the minimal interpretation for
each task, i.e. the set of competencies that a person has to have at the minimum to be able to
perform the task well.
To obtain the whole competence space, the competence states of the minimal interpretation
were closed under union and the empty set was added. Furthermore, for every competence
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state the representation function was built by assigning to every state the set of tasks a
person would be able to accomplish in the respective state, thereby obtaining the
competence performance structure.
The competence performance structure derived for the previous example can be seen in
Figure 6. In this example, a person who is in the competence state {B, C, D} should perform
well in tasks {1, 2,7} (the respective performance state). A person who is able to accomplish
task 4 (‘‘Allocate functions between actors according to boundaries’’) is assumed to be able
to also perform task 2 (‘‘Carry out an initial stakeholder analysis’’) because any performance
state which contains task 4 also contains task 2. In other words, task 2 is assumed to be a
prerequisite of task 4, since the minimal interpretation of task 2 ({B}) is a subset of the
minimal interpretation of task 4 ({A, B, C}).
The purpose of this procedure is to limit the number of competence states (and performance
states) that can be expected to appear in a population as a consequence of the prerequisite
relationships. As a result, several adaptive procedures can be applied that can be utilized
when the structures are put to use (see below).
Using and validating the structures
Given a valid structure of the domain, one can diagnose the competence state of a person
by evaluating his/her performance in the tasks being performed, and thereby derive a
competency gap. Given certain tasks that were performed well, and others that were not
Table I Tasks and competencies in system goal modeling
Tasks Competencies
1_1 Build a first-cut Context Model to identify system
A Knowledge about actors, tasks, goals, and
1_2 Carry out an initial stakeholder analysis B Knowledge of different types of system
1_3 Develop an extended Context Model C Knowledge of building the Context Model
1_4 Allocate functions between actors according to
D Knowledge about the Strategic Dependency
(SD) Model
1_5 Identify intentional strategic actions E Knowledge about the Strategic Rationale (SR)
1_6 Model dependencies between strategic actions F Ability to produce an i* model
1_7 Write different forms of dependency descriptions G Judgment and decision-making skills
1_8 Produce an integrated SR model using
dependencies in the SD model
H Knowledge of guidelines for validating the SR
1_9 check the i* model for completeness and
1_10 Calidate the i* SR model against the SD model
Table II Task competency matrix and minimal interpretation of tasks in SGM
Task A B C D E F G H Minimal interpretation
1_1 ££ {B, C}
1_2 £{B}
1_3 ££ £ {B, C, G}
1_4 £££ {A, B, C}
1_5 £££ £ {A, B, C, G}
1_6 ££££ ££ {A, B, C, D, F, G}
1_7 £{D}
1_8 £££££££ {A, B, C, D, E, F, G, H}
1_9 ££££{A, D, E, F}
1_10 £££££{A, D, E, F, H}
VOL. 12 NO. 6 2008
performed well, it is relatively easy to find the likely competence state this person is in. If a
person consistently performs well in tasks 1, 2 and 7 in the above example, but fails to
perform well in task 4, this would mean that competency A (‘‘Knowledge about actors, tasks,
goals and resources’’) would be a relevant learning goal. In the case of such discrepancies,
one could provide the person with tailored learning contents.
This competency diagnosis can make use of the adaptive potential mentioned previously.
From knowing that a person can perform well in certain tasks, it can be inferred with some
certainty that this person also performs well in other tasks. This seems to be especially
relevant for structures that encompass a large number of tasks where it is unlikely that
performance information about all tasks is available for each and every employee.
Judgments of whether a certain task has been performed well or not (performance
appraisal) can be obtained in a number of different ways. Standard procedures of self- and
supervisor rating known from competency management and other human resource
instruments (such as assessment centers or performance appraisal schemes) can be
obtained. An important advantage when compared to many of the standard practices is that
appraisal can be based on task performance which is relevant for the job that is being
performed. This avoids several biases known from the appraisal of competencies (Schmitt
and Chan, 1998).
The procedures for diagnosing competence states from past performance, and especially
the adaptive procedures, require that the structures are valid. This is not an exclusive
requirement for our approach, but in fact is essential for any appraisal system that is being
put to use (see, for example, Schmitt and Chan, 1998). A special benefit offered by the
competence performance approach is that it makes validating easier and offers the
opportunity to integrate validation directly into the modeling or assessment process (Ley
and Albert, 2004). Criteria for validating competence performance structures are discussed
in Ley et al. (2007). In the present case study, an initial comparison of the assignments made
by the two experts resulted in an agreement coefficient (inter-rater reliability) of r¼0:26 for
the HAM stream and r¼0:53 for the SGM stream.
Figure 6 Competence space and representation function
VOL. 12 NO. 6 2008
The above structures map the learning domain in terms of learning goals and the
related tasks derived directly from relevant working tasks. This means that learning is
specifically tailored to the requirements of working tasks and processes. The Workplace
Learning Context Model has been developed in order to provide a mapping between
the process, knowledge and competency spaces relevant to a given user. Hence,
several ways of supporting users during their work tasks can be realized. Among them
are the following:
BTask learning The Workplace Learning Context Model allows for deriving resources,
which are considered relevant for executing a given task, and presenting them to the user.
BTask execution support and domain-related support The Workplace Learning Context
Model allows for deriving those concepts of a domain ontology which are directly related
to the work domain, within which the task is going to be executed.
BCompetency-gap based support The Workplace Learning Context Model allows for
computing the competency gap between the competencies that are necessary for
executing the task at hand and the competencies a user possesses, and consequently
for deriving those competencies that are necessary to fill the competency gap.
As a next step, we are intending to integrate the competence performance structures into
the APOSDLE environment. The learning space within this environment can then make use
of the structures when delivering tailored learning material to knowledge workers in the
process of an RE project. We are then intending to evaluate learning outcomes that result
from use of the system by means of a large-scale evaluation study in real-world settings. An
important question in this evaluation will be the added benefit which the different models
provide with regard to the learning outcomes.
The initial results we have presented above suggest that there is plenty of room for future
research aiming at making knowledge management and workplace learning more effective.
In particular, measures applicable for augmenting knowledge workers’ learning transfer and
hence productivity deserve special attention. It is known that approaches are urgently
needed that allow for better alignment of learning material with a learner’s requirements,
hence boostING workforce agility (Brakeley et al., 2004). It should be stated here once again
that monolithic approaches are often not appropriate. They tend to gradually lose their agility
and momentum when deployed in a real-world scenario. This inflexibility is mainly due to the
fact that monolithic approaches are hard to maintain and unsuited to changes and
adaptations. We therefore propose to research offering knowledge services for workplace
learning that strongly consider a user’s context. We envision that the applications mentioned
in the scenario above might be well be suited for such a service-based approach. One such
a service is an analysis service, which infers information based on a user’s past behavior. An
example of this is given in the scenario above, where Laura’s competencies are
automatically inferred from her past task executions. Another service concludes users’
competencies gaps with respect to the task at hand and triggers a competency-sensitive
retrieval for knowledge artifacts. An example of this is given in the scenario above. Still other
services might offer recommendations of learning resources that have been considered
appropriate by users in a similar context.
‘‘ A comprehensive Workplace Learning Context Model needs to
cover a broad knowledge spectrum from organizational
knowledge to knowledge of individual employees. ’’
VOL. 12 NO. 6 2008
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About the authors
Tobias Ley is a Senior Researcher and Deputy Division Manager at the Know-Center in Graz.
Since 2001, he has been leading industry-based research projects in the areas of
knowledge management, technology enhanced learning and competency management.
Tobias holds a PhD in Cognitive Psychology from the University of Graz in Austria, and an
MSc in Psychology from Darmstadt University of Technology in Germany. During his studies
he spent a semester at Melbourne University in Australia and a year at the Krannert
Graduate School of Management at Purdue University in the USA as a Fulbright scholar,
where he studied human resource management and management information systems.
Tobias Ley is the corresponding author and can be contacted at:
Armin Ulbrich is based at the Know-Center, Graz, Austria. After studying at the Graz
Technical University he worked as a software developer and project manager. At the
Know-Center he has been leading industry projects, first in the area of technology-enhanced
learning, and later also in the area of knowledge management. Currently he is researching in
the field of user-context modeling among others for workplace learning.
Peter Scheir received his Master’s degree in Telematics from Graz University of Technology
in 2003. In his studies he focused on information systems and computer graphics. In 2001
he joined the Know-Center’s Competence Center for Knowledge Management, where he
started to work in the area of information and knowledge management systems. Since 2006
he has been working in the field of web science at the Knowledge Management Institute of
Graz University of Technology. His current research interests include semantic web,
information retrieval based on knowledge organization systems, and ontology enabled
information systems.
Stefanie Lindstaedt is Head of the Division Knowledge Services at the Know-Center in Graz,
Austria. In this role she is responsible for the management of many large, multi-firm projects
and the scientific strategy of the division. For more than ten years she has been leading
interdisciplinary, international projects in the fields of knowledge management, (e)learning,
and software engineering. For the last five years she has focused on the issue of
work-integrated learning, developing the concept of AD-HOC Learning and performing
research on competency development and modeling. Since 2006 she has been Scientific
Coordinator of the EU-funded integrated project APOSDLE on the same topic. She holds
both a PhD and an MS in Computer Science from the University of Colorado at Boulder
Barbara Kump is a Research Assistant at the Knowledge Management Institute at Graz
University of Technology. In 2006, she graduated with an MSc in Psychology from the
University of Graz. In her Master’s thesis, she applied principles of cognitive psychology to
organizational competency management and technology-enhanced learning.
Dietrich Albert (see graduated from the University of
¨ttingen in 1966 with a Diploma (MS) in psychology. In 1972 he received his Dr.rer.nat.
(DSc) and in 1975 his postdoctoral degree (Habilitation in Psychology) from the University of
Marburg/Lahn. He was Professor of General Experimental Psychology at the University of
Heidelberg from 1976 to 1993. Since 1993, he has been Professor and Head of the Cognitive
Science Section at the University of Graz. He is member of several scientific societies
(including EARLI, APC of AACE, and the SMP) and advisory boards (including Center for
Psychological Information and Documentation, ZPID; Know-Center; Academy of New Media
and Knowledge Transfer).
VOL. 12 NO. 6 2008
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The purpose of this study was to identify clusters of nursing competency, and investigate the influence of reflective thinking, team learning climate, and learning organization building according to nursing competency clusters.
This chapter develops an extension of Doignon and Falmagne's knowledge struc-tures theory by integrating it into a competence-performance conception. The aim is to show one possible way in which the purely behavioral and descriptive knowledge structures approach could be structurally enriched in order to account for the need of explanatory features for the empirically observed solution behav-ior. Performance is conceived as the observable solution behavior of a person on a set of domain-specific problems. Competence (ability, skills) is understood as a theoretical construct accounting for the performance. The basic concept is a mathematical structure termed a diagnostic, that creates a correspondence be-tween the competence and the performance level. The concept of a union-stable diagnostic is defined as an elaboration of Doignon and Falmagne's concept of a knowledge space. Conditions for the construction and several properties of union-stable diagnostics are presented. Finally, an empirical application of the competence-performance conception in a small knowledge domain is reported that shall illustrate some advantages of the introduced modeling approach.
The notion of competency provides an observable account of concrete human capacities under specific work conditions. The fact that competencies are subject to concrete kinds of measurement entails that they are subject to some extent to comparison and even in some sense, calculus. Then, competency models and databases can be used to compute competency gaps, to aggregate competencies of individuals as part of groups, and to compare capacities. However, as of today there is not a commonly agreed model or ontology for competencies, and scattered reports use different models for computing with competencies. This paper addresses how computing with competencies can be approached from a general perspective, using a flexible and extensible ontological model that can be adapted to the particularities of concrete organizations. Then, the consideration of competencies as an organizational asset is approached from the perspective of particular issues as competency gap analysis, the definition of job positions and how learning technology can be linked with competency models. The framework presented provides a technology-based baseline for organizations dealing with competency models, enabling the management of the knowledge acquisition dynamics of employees as driven by concrete and measurable accounts of organizational needs.
Learning activities can be considered the final outcome of a complex process inside knowledge intensive organizations. This complex process encompasses a dynamic cycle, a loop in which business or organizational needs trigger the necessity of acquiring or enhancing human resource competencies that are essential to the fulfillment of the organizational objectives. This continuous evolution of organizational knowledge requires the management of records of available and required competencies, and the automation of such competency handling thus becomes a key issue for the effective functioning of knowledge management activities. This chapter describes the use of ontologies as the enabling semantic infrastructure of competency management, describing the main aspects and scenarios of the knowledge creation cycle from the perspective of its connection with competency definitions.