A MODEL FOR COMPETENCE GAP ANALYSIS

∗

Juri L. De Coi, Eelco Herder, Arne Koesling, Christoph Loﬁ,

Daniel Olmedilla, Odysseas Papapetrou and Wolf Siberski

L3S Research Center and University of Hannover, Hannover, Germany

Keywords:

Competence, competency, model, gap analisys, lifelong learning.

Abstract:

Modeling competences is an integral part of many Human Resource (HR) and e-Learning related activities.

HR departments use competence descriptions to deﬁne requirements needed for performing speciﬁc tasks or

jobs. The same competences are acquired by employees and applicants by e.g. experience or certiﬁcations.

Typically, HR departments need to match such required and acquired competences in order to ﬁnd suitable

candidates. In e-Learning a similar situation arises. Curricula or training programmes need to describe pre-

requisites that must be fulﬁlled before joining and the competences that will be acquired after successful

completion. This paper analyses the limitations and extends existing approaches for modeling competences in

order to allow (semi-)automatic competence matching.

1 INTRODUCTION

Nowadays, people mobility has increased. Learn-

ers may study abroad with the beneﬁts of improving

their language skills, receiving a better certiﬁcation,

or specializing in a topic not available in their re-

gions. The same applies to the labour market. Peo-

ple do not need to restrict themselves to their city or

region while seeking for a job but may consider of-

fers in other countries, too. This situation compli-

cates the already difﬁcult job of managers in learn-

ing organizations and Human Resource (HR) depart-

ments to decide who may have the right qualiﬁca-

tions to join a project or the company itself. For

learning organizations, requirements to join the pro-

gramme must be taken into account. For example, an

applicant needs to possess a Bachelor degree to apply

for Master studies; in order to attend an expert course

on a topic, a certiﬁcation on a basic level may be re-

quired. Furthermore, assuming that an applicant ful-

ﬁlls such requirements, exemptions could be granted

for parts of the programme that are similar to ear-

∗

This research was partly funded by the

EU TENCompetence project (FP6-IST-02787)

(http://www.tencompetence.org) and EU PROLIX project

(FP6-IST-027905) (http://www.prolixproject.org/).

lier followed courses. Imagine a Mathematician start-

ing a Computer Science degree. Most likely, courses

like Algebra and Statistics could be exempted. In

the case of Human Resource departments, the task

is equally complex. HR experts need to match ap-

plicant or employee experience and knowledge with

the requirements of a job offer or a project, including

both mandatory requirements and desired ones (e.g.,

Business English is required and French would be a

plus). Currently, all these competence matches have

to be performed manually, with hardly any guidelines

or support. One important reason for this is that there

are currently no sufﬁciently expressive common for-

mats for the representation of competences, which is

needed for complex competence proﬁles and require-

ments. Some initiatives, such as the IEEE Reusable

Competency Deﬁnition (IEEE RCD, 2005) and HR-

XML (HR-XML, 2004), have done initial steps to de-

ﬁne common models and schemas for interoperabil-

ity, but their current work lacks some important infor-

mation that is required for competence matching, like

proﬁciency levels, context (cf. Section 3) or mech-

anisms for increasing reusability. In this paper, we

enhance and extend the work developed under vari-

ous initiatives and introduce a model for represent-

ing competences with their relationships as well as

304

L. De Coi J., Herder E., Koesling A., Loﬁ C., Olmedilla D., Papapetrou O. and Siberski W. (2007).

A MODEL FOR COMPETENCE GAP ANALYSIS.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Society, e-Business and e-Government /

e-Learning, pages 304-312

DOI: 10.5220/0001272603040312

 SciTePress

usage proﬁles (such as proﬁles for job requirements

description or for learner achievements description).

This model provides the basis for allowing advanced

(semi-)automatic competence matching and gap anal-

ysis.

This paper is organized as follows: section 2 clariﬁes

the terms used throughout the paper and brieﬂy in-

troduces our requirements for modeling competences.

Section 3 provides an overview of existing modeling

speciﬁcations, and section 4 describes our modeling

approach for competences in a more detailed manner.

Section 5 introduces competence proﬁles (collections

of competences) which represent the most visible as-

pect of competence modeling in real-life applications.

Section 6 gives an example on how a simple proﬁle

and related competences can be modeled. Finally,

section 7 concludes this paper with a summary and

an outlook on future work.

2 WHAT IS A COMPETENCE?

In this work we adopt the deﬁnition of competence

as “effective performance within a domain/context at

different levels of proﬁciency”, as given in (Chee-

tam and Chivers, 2005). Note that there exists some

confusion on the term competency

in the literature.

(IEEE RCD, 2005; IMS RDCEO, 2002) deﬁne the

stricter term of competency as “any form of knowl-

edge, skill, attitude, ability, or learning objective that

can be described in a context of learning, education

or training”. This deﬁnition is insufﬁciently expres-

sive for competence gap analysis. For example, it is

not clear if “piloting” covers both the ability to pilot a

small plane and to pilot a big passenger airplane. Or

if the competency “English writing skills” represents

a speciﬁc level such as intermediate, ﬂuent, native

or simply the existence of the competency. In fact,

if that information becomes part of the competency

deﬁnition, its reusability is drastically reduced (with

the consequence of, e.g., having different competency

deﬁnitions for each context in which a competency is

applied, and for any proﬁciency level and proﬁciency

level scale). The deﬁnition given in (HR-XML, 2004)

tries to extend the previous one: “A speciﬁc, iden-

tiﬁable, deﬁnable, and measurable knowledge, skill,

ability and/or other deployment-related characteristic

(e.g., attitude, behavior, physical ability) which a hu-

man resource may possess and which is necessary for,

or material to, the performance of an activity within

a speciﬁc business context”. In this case, “measur-

able” indicates a relationship with a speciﬁc proﬁ-

The reader is alerted for the distinction between the two

terms, competence and competency

Competence

Proficiency

Level

Context

Competency

Figure 1: Competence as a combination of competency,

proﬁciency level and context.

ciency level

and competency now applies only to the

business context. In any case, since context is im-

plicit, the models proposed from these speciﬁcations

do not include context information.

As stated above, current approaches to model-

ing competencies do not explicitly address proﬁ-

ciency level and context. On the contrary, we be-

lieve that competency, proﬁciency level and context

are three different dimensions that should be mod-

eled separately in order to maximize their reuse.

For example, the same competencies may be used

in different contexts, or the same proﬁciency level

scales may be reused among different certiﬁcations.

The same applies to contexts (or “domain models”),

which in many situations already exist and there-

fore may be reused by competences. Therefore, ac-

cording to what stated above, we model competence

(plural:competences) as a three-dimensional variable,

made up of a competency (plural:competencies), a

proﬁciency level and a context (see ﬁgure 1). For

example, “Fluent Business English” would be com-

posed of the competency “English”, the proﬁciency

level “Fluent” and the context “Business”.

For sake of clarity, and in order to avoid confusion

between the terms competence and competency, we

may use competency and skill interchangeably here-

after. However the reader should be aware that skill is

not a synonym for competency, as it only covers part

of its scope.

3 RELATED WORK

There exist some standardization efforts on modelling

competencies. These efforts focus on different as-

pects related to competency: competencies as such,

competency proﬁles and relationships among compe-

tencies.

Although they later refer to it as “grade”, which is dif-

ferent from proﬁciency level - see section 5.1

A MODEL FOR COMPETENCE GAP ANALYSIS

305

The IMS Reusable Deﬁnition of Competencies or

Educational Objective (IMS RDCEO, 2002) and the

later IEEE Reusable Competency Deﬁnition (IEEE

RCD, 2005) (based on IMS RDCEO) focus on

reusable competency deﬁnitions. The primary idea is

to build central repositories which deﬁne competen-

cies for certain communities. These deﬁnitions can

be referenced by external data structures, encouraging

interoperability and reusability. However, IEEE RCD

lacks information on context and proﬁciencylevel and

does not allow relationships or recursive dependen-

cies among competencies.

HR-XML focuses on the modeling of a wide range

of information related to human resource tasks (like

contact data or aspects of the curriculum vitae). The

work performed in HR-XML Measurable Competen-

cies (HR-XML, 2004) tries to deﬁne proﬁles in order

to use such competency deﬁnitions. It speciﬁes data

sets like job requirement proﬁles (which describe the

competencies that a person is required to have) or per-

sonal competency proﬁles (which describe the com-

petencies a person has). Such proﬁles are composed

of evidences (either required or acquired) referring to

competency deﬁnitions (e.g., IEEE RCD). Unfortu-

nately, the proposed model does not clearly separate

required and acquired proﬁles. The consequence is

that an acquired competency could have mandatory

and optional elements according to the model. Fur-

thermore, it is unclear why a competencyis composed

of several evidences: since a competency is a reusable

object, evidences should rather represent a require-

ment or demonstrate the acquirance of a competency.

Hence, the evidences should refer to or contain com-

petency deﬁnitions and not vice versa.

The Simple Reusable Competency Map (SRCM,

2006) tries to model relationships between competen-

cies. A map can contain information about depen-

dencies/equivalences among competencies, including

the composition of complex competencies from sim-

pler ones. In SRCM, relationships are modeled us-

ing a directed acyclic graph. However, the semantics

of the model proposed in SRCM is confusing. Re-

lationships among different nodes may have different

meanings: composition, equivalence or order depen-

dency. This leads to confusion when modeling tasks

as well as when creating algorithms to use such in-

formation. Furthermore, combination and weighting

of competencies is not clearly deﬁned, and external

references to the maps (e.g., from proﬁles) must point

to the root (and not to any node), therefore requiring

the traversal of the graph until the appropriate node

is found. Moreover, in this paper we argue that it is

not possible to model relationships among competen-

cies, because proﬁciency level and context have to be

considered. For example, statistics knowledge may

be a requisite for becoming a computer scientist or a

sociologist. However, the proﬁciency level required

and the context in which the competency is applied

are completely different, hence making impossible to

create relationships directly among competencies.

In OntoProPer (Sure et al., 2000), proﬁles are

described by ﬂat vectors containing weighted skills

(where weights grow from 0 to 3), which are ex-

pressed as labels. Weights represent importance if ap-

plied to requirements or skill level if applied to ac-

quired skills. The system itself mainly focuses on

proﬁle matching and introduces an automated way

of building and maintaining proﬁles based on ontolo-

gies. (Colucci et al., 2003) describes an ontology-

based semantic matchmaking(using Description Log-

ics) between skills demand and supply. In (Lefebvre

et al., 2005), which also deﬁnes a competence on-

tology for domain knowledge dissemination and re-

trieval, a competence is related to capabilities, skills

and expertise (measured by levels growing from 1 to

5). Although this approach is closer to our deﬁnition

of competence, still the context is not tackled, the re-

lationships are deﬁned at the skill level and the proﬁ-

ciency levels are not ﬂexible enough.

4 MODELLING A COMPETENCE

In this section we introduce a model for representing a

competence with a broader and clearly deﬁned view.

We base this model on the three dimensions that a

competence is composed of: competency, proﬁciency

level and context. We ﬁrst describe each dimension

separately and ﬁnally present how they are combined

in order to build a competence and how competences

may be composed of sub-competences. Several issues

encountered during the modeling process, and possi-

ble solutions (eventually with a trade-off between ex-

pressiveness and complexity) are described. We also

discuss the decisions we have taken as well as their

features and the limitations derived from them.

4.1 Competency

The IEEE Reusable Competency Deﬁnitions (IEEE

RCD, 2005) provide a model for the representation of

competencies (ﬁgure 2). The model does not include

proﬁciency level or context information. In addition,

as stated in the speciﬁcation, IEEE RCD is “intended

to meet the simple need of referencing and cataloging

a competency, not classifying it”, that is, it does not

provide any means to specify relationships between

competencies. We agree upon this view and believe

WEBIST 2007 - International Conference on Web Information Systems and Technologies

306

that relationships should not be modeled at this level

because they also depend on the other two dimen-

sions: proﬁciency level and context. For example,

piloting cannot be related to other competencies with-

out knowing if it refers to helicopters, small planes or

passenger planes.

The ideas described in (IEEE RCD, 2005) meet

our requirements, with the advantage that this work is

already acknowledged from the community as a draft

standard. Therefore, we decided to reuse IEEE RCD’s

model to represent competencies (see model depicted

in ﬁgure 2).

The RCD identiﬁer provides the basis for refer-

encing and reusing such RCDs, while title and de-

scription provide free text to represent them. We as-

sume the existence of repositories of RCD elements

which may be referenced from different competences

by their global identiﬁer. In addition, (IEEE RCD,

2005) includes information which is not thought to be

machine-processable

but for human interpretation.

Such information includes structured descriptions for

more complete deﬁnitions.

4.2 Proﬁciency Level

Different scales (qualitative and quantitative) may be

used in order to represent proﬁciency levels. For in-

stance, a computer science curriculum may simply

want to specify whether a student has acquired a com-

petence or not, whereas an English certiﬁcation insti-

tution may want to classify students into intermediate,

advanced or proﬁcient. Many different scales may be

used but it should be possible to reuse them within

and across the borders of the institution. For example,

scales are typically the same for most certiﬁcations

given by one institution and even among them (e.g.,

all curricula in Spanish universities). Hence, they can

be modeled once and referenced many times.

Proﬁciency levels are not simply a ﬂat set of el-

ements. There are implicit relationships among ele-

ments within one scale. For example, a proﬁciency

level may be subsumed by another (“proﬁcient” sub-

sumes “advanced” which subsumes “intermediate”

and so on). We need to model such relationships be-

cause they will be needed for competence matching.

For instance, a job requiring someone with interme-

diate English skill typically has implicit the quantiﬁer

“with at least”, meaning that anyone with advanced

English would also be accepted (and maybe even pre-

ferred ). In order to represent this relationships, an or-

dered list provides a reasonable means to represent a

proﬁciency level scale (see ﬁgure 2). In such a list, the

minimum value (subsumed by any other in the list) is

Do not confuse with machine-exchangeable

given by the ﬁrst element and the maximum is given

by the last one. Therefore, the order in the list repre-

sents subsumption relationships, that is, the ﬁrst ele-

ment is subsumed by the second one which is as well

subsumed by the third one and so on.

In order to improve interoperability and matching

among scales, an optional ﬁeld is included for map-

ping to a universal scale (e.g., [0,1]). The reason

why this mapping ﬁeld is optional is that even though

it would be useful to include it, in some contexts it

may not be possible to ﬁnd a suitable mapping or it

may not even be necessary (e.g., if a scale is used

only within an institution and no interoperability is

intended).

Competence descriptions can refer to speciﬁc

items of these scales in order to represent the proﬁ-

ciency level acquired/required. Algorithms could take

relationships among proﬁciency levels into account in

order to ﬁnd out how much training/learning is re-

quired to reach a determined employee/learner proﬁ-

ciency level. For example, if advanced English skills

are required, training an employee who already ac-

quired intermediate English skills will cost less time

and money than training another employee who has

only beginner English skills.

4.3 Context

(Webster, ) deﬁnes context as “the interrelated condi-

tions in which something exists or occurs”, which in-

cludes “the circumstances and conditions which sur-

round it” (Wikipedia, ). Regarding to competences,

context may refer to different concepts. It might be

the speciﬁc occupation in which a competence is ac-

quired (e.g., driving as an ambulance driver or a pizza

delivery employee), a set of topics within a domain

(e.g., telecommunications or tourism, or theoretical

vs. applied physics) or even the personal settings re-

lated to the learner (e.g., competences are different if

acquired in a group-based learning setting than indi-

vidually). All these (and possibly more) are contexts

which may be part of a competence. What actually

makes up sufﬁcient context descriptions can not be

deﬁned in general, but depends on the scope and pur-

pose of the competence descriptions to which they are

attached. As with the skill deﬁnitions and proﬁciency

levels, context deﬁnitions may be reused.

Modeling contexts may be a complex task, as

it may coincide with modeling the whole domain

knowledge of an institution. Ontologies can capture

such knowledge (Lau and Sure, 2002) and use ar-

bitrary complex structures, from simple sets or tree

structures to directed acyclic graphs. Up to date, our

investigations of existing relationships between con-

A MODEL FOR COMPETENCE GAP ANALYSIS

307

text elements (regarding its use within competences)

do not show the need for providing a graph represen-

tation or multiple inheritance. For this reason, we de-

cided to ﬁrst restrict the modeling of context to trees

(see model

depicted in ﬁgure 2). This has multiple

beneﬁts:

• it reduces the computation complexity of compe-

tences

• it is easier to understand by users

• it avoids the need for cycle-detection mechanisms

while modeling is done

• it simpliﬁes the algorithms for competence match-

ing.

We are still investigating the advantages and draw-

backs of this decision and we do not discard an ex-

tension of the model in case we ﬁnd some scenar-

ios for which such a structure would be beneﬁciary.

Allowing for more advanced algorithms could also

be a reason for choosing a more expressive context

model. Furthermore, the relationship among context

concepts may also be used by algorithms analyzing

competence gaps. For example, assume that a context

models all occupations of an airline company within

an airport. In case it is needed to train a new pilot for

passenger ﬂights, it would be preferred to train some

of the pilots of cargo planes instead of a person from

the check-in counter. This information could be ex-

tracted from e.g. distances between the occupation

“pilot” and the rest of occupations in the tree/graph.

4.4 Competence

Competences are described as reusable domain

knowledge. Any model representing competences de-

scribes what a competence is and how it is composed

of sub-competences. These competences are general

descriptions, independent of speciﬁc learners or job

descriptions. For example, being a good taxi driver

or an expert Oracle database administrator are con-

cepts with ﬁxed meaning (domain knowledge), inde-

pendentof which person possesses such competences.

This is important to be noticed, because competences

are to be referenced from certiﬁcations or job descrip-

tions, in order to stimulate their reuse. For instance,

a company may deﬁne required, relevant or desirable

competences for their business, which are included in

job offers or projects descriptions. The exact meaning

of these competences is provided by a company-wide

The set of attributes in the context structure is the min-

imum one allowing reference and reuse. This model may of

course be extended with more data speciﬁc for the areas in

which it is used

competence model. Using this approach, the expla-

nation of a competence needs not to be explicitly in-

cluded every time it is used

. These explanations may

cover a broad range of aspects, such as:

• how a competence may be achieved, for example

by acquiring some sub-competences;

• to which level each competence should be ac-

quired. As an example, scientiﬁc research in a

University may require only basic knowledge of

mathematics while at NASA, expert knowledge is

needed;

• whether sub-competences must be all achieved or

simply a subset of them. For instance, it is typi-

cal in curricula that in order to get a degree, some

topics are mandatory and some other are optional,

from which a subset has to be chosen (e.g., pass k

optional courses out of n offers);

• if the sub-competences must be acquired in a spe-

ciﬁc order. Some companies may require that an

applicant acquired a competence on personal task

organization before becoming a good team leader.

Otherwise, they may assume that the performance

related to the competence of being a good team

leader is reduced.

In order to model all these elements we created

an object model derived from the Composite design

pattern (Riehle, 1997) (see ﬁgure 2). In our model,

a competence can be either simple, an aggregation

of children, or a selection from children alternatives.

Competence models a competence, with references

to a skill (RCD id), a proﬁciency level and a context.

It can be a SimpleCompentence (an atomic

description) or a CompositeCompetence. The

latter can be either be an AggregateCompetence

or AlternativeCompetence. An

AggregateCompetence can be used to de-

ﬁne a competence which consists of several

sub-competences, all of them required. The sub-

competences can be either an ordered set (meaning

that the sub-competences must have been acquired

in such an order) or unordered (default). An

AlternativeCompetence can be used to

construct a set of alternative sub-competences. It

is possible to specify a minimum and a maximum

number of alternatives that must be acquired (e.g.,

minimum k out of n). “Exactly” k sub-competences

might be speciﬁed by setting both minimum and

maximum to the same number. By default minimum

is set to 1 so at least one subcompetence of the set is

required.

As with the use of ontologies, whose classes can be

simply referenced without the need of copying the whole

ontology every time they are used

WEBIST 2007 - International Conference on Web Information Systems and Technologies

308

Proficiency Level

-Universal Scale Mapping [0..1]

-Label

Aggregate Competence

-Sequenced : boolean = false

Alternative Competence

-minNumber : Integer = 1

-maxNumber : Integer

Composite Competence SimpleCompetence

Metadata

-RCD Schema Version

-Additional Metadata

-RCD Schema

Proficiency Scale

Global Identifier

-Catalogue

-Entry

Statement

-Token

-Name

-Text

-Id

Competence

-Name

Definition

-Model Source

RCD

-Description

-Title

Context

-Label

Ordered

list

-RCD Ref

-alternatives

2..*

-parts

2..*

-Context Ref

-levels

1..*

-Statements

1..*

-identifier

-subClassOf

0..1

-Prof Level Ref

0..1

Figure 2: Competence Model.

Such a model allows to represent atomic compe-

tences, (un)ordered aggregation (all sub-competences

must be acquired), alternative composition (a subset

of sub-competences must be acquired) and any com-

bination of all of them.

It is important to notice that if a competence is

composed from several sub-competences, the proﬁ-

ciency level referenced in each subcompetence repre-

sents the minimum level required. For example, if it

is required to have intermediate English skills in the

context of science in order to be a good researcher,

then anyone with advanced skills fulﬁlls such a re-

quirement. The subsumption relationship modeled

within the proﬁciency levels is used for this purpose,

and the proﬁciency level on the competence itself

needs not to include all possible subsumers.

Our model is open to the addition of new relation-

ships, among them an equivalence relationship. This

is especially interesting if competence repositories of

two communities are joined and mappings between

overlapping competences have to be modeled.

5 COMPETENCE PROFILES

Previous sections described how competences and re-

lationships among them can be modeled. In real

world applications, competence deﬁnitions are to sup-

port different tasks like creating job proﬁles for hiring

or selecting people for a particular project, creating

personal competence proﬁles showing the abilities of

a person, and modeling the prerequisites and expected

results of joining a learning or training programme.

These tasks require modeling collections of required

or acquired competences. Furthermore, the require-

ments speciﬁed by a job offer must be matched by the

acquired competences an applicant provides. It there-

fore indicates that the model should be similar for all

the cases enumerated in order to ease its matching.

We refer to this model “competence proﬁle” hereafter.

We can distinguish between two types of compe-

tence proﬁles, depending on their purpose:

Required Competence Proﬁle: Speciﬁes the re-

quirements (in terms of competences) to be

fulﬁlled by an applicant. These are typically used

for job descriptions or programme prerequisites.

Acquired Competence Proﬁle: Speciﬁes the ac-

complishments (in terms of competences) of

employees and learners. These are typically used

in order to show (and possibly prove) which

competences have been acquired or to represent

the expected accomplishment after successful

completion of a programme.

Each kind of proﬁle is composed of a set of Pro-

ﬁleElements

. These proﬁle elements may be re-

quired or acquired, depending on the type of the pro-

ﬁle container (see ﬁgure 3). A proﬁle element con-

tains data which

• may be part of the criteria a companyor a learning

programme uses to decide whether an applicant is

appropriate

• an institution providing degrees or certiﬁcations

issues to learners as a prove of the acquired com-

petence

• a learner uses to describe acquired competences

For clarity, we did not keep the term evidence intro-

duced in (HR-XML, 2004). A ProﬁleElement represents a

requirement or a statement of an acquired competence but

not necessarily a proof. Therefore, evidence could be mis-

leading since it may be confused with proof or certiﬁcation

A MODEL FOR COMPETENCE GAP ANALYSIS

309

ProfileElement

-Supporting Information

-Issuer Organisation

-Competence Ref

-Expiration Date

-Description

-Date Issued

-Name

-Type

AggregateProfileElement

-Sequenced : boolean = false

AlternativeProfileElement

-minNumber : Integer = 1

-maxNumber : Integer

CompositeProfileElementSimpleProfileElement

Global Identifier

-Catalogue

-Entry

Value

-Scale Reference

StringValue

-maxValue

-minValue

-Value

NumericValue

-maxValue

-minValue

-Value

Acquired

Profile

Required

Profile

Composite profile

elements are allowed only

for Required Profiles

This association may

hold a tag/weight

-alternatives

2..*

-parts

2..*

-Global Identifier

0..1

-contains *

-grade

0..1

Figure 3: Competence Proﬁle.

in her CV (not necessarily with a proof or certiﬁ-

cation, e.g.,based on her experience)

Such information includes a type (e.g., driving li-

cense or university degree), the competence required

or acquired and (possibly) a grade

, the issuer organi-

zation, issue date and expiration date (i.e., from when

the driving license is not valid anymore). All these

ﬁelds are optional since not all are always needed.

Typically, requirement proﬁles do not need to spec-

ify all ﬁelds of expected proﬁle elements but only

part of them. In these cases, some ﬁelds may be

left empty, ensuring comparison only on those ﬁelds

which specify constraints. For example, expert com-

puter scientist may be a requirement but it may not

be relevant where the competence was acquired (only

competence ﬁeld is ﬁlled in) or any applicant with a

master degree may be sought but it does not matter in

which ﬁeld (only “type” is ﬁlled in and competence

is left empty). In contrary, acquired proﬁle elements

should typically be ﬁlled in to a larger extent, spe-

cially if provided by certiﬁcations.

Note that the structure of a “ProﬁleElement” is

different for required and acquired proﬁles. On the

one hand, required proﬁles need to represent manda-

tory (English and French) and alternative require-

ments (either English or French) or even desired re-

quirements (English mandatory and French is a plus).

For that, we use the same composite model (meta-

model) as the one speciﬁed for competences in sec-

tion 4 (with the addition of tagging relationships with

e.g. ’desired’), thus easing understanding and simpli-

Note that grade and proﬁciency level represent different

concepts (cf. Section 5.1)

fying the tools needed to process these models. On the

other hand, acquired proﬁles do not need such com-

plex relationships and will therefore be represented

as sets, that is, a ﬂat collection of “SimpleProﬁleEle-

ment” elements.

5.1 Competence Proﬁciency Level vs.

Grade in Competence Proﬁle

Element

Proﬁciency levels are part of competences, for exam-

ple “Fluent English”. This is different from grades

provided by institutions (e.g., 250 in TOEFL test).

While the former represents that “any person who

has such a competence is supposed to perform ef-

fectively”, the latter provides a “way to rate persons

having such competence at a speciﬁc level of proﬁ-

ciency, by means of some sort of assessment”. For

example, two people having successfully completed

an “Advanced Oracle Database Administrator” pro-

gramme are able to perform effectively. However,

they may have different grades in their ﬁnal certiﬁ-

cation, which may be considered by HR departments

before accepting any of them. In other words, pro-

ﬁciency levels (which are not bound to speciﬁc pro-

ﬁles) represent the scope of the competence acquired

(advanced database administration vs. basic database

administration) independently of whether a speciﬁc

learner or employee (bound to a proﬁle) learned the

content perfectly or sufﬁciently to acquire the com-

petence (higher or lower grade). For instance, be-

ing a proﬁcient computer scientist requires to have

advanced knowledge on databases, be intermediate

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310

Intermediate IT Spanish Vocabulary

: SimpleCompetence

Context Ref = IT

Prof Level Ref = Intermediate

RCD Ref = Spanish Vocabulary

Intermediate IT English Vocabulary : SimpleCompetence

Context Ref = IT

Prof Level Ref = Intermediate

RCD Ref = English Vocabulary

: Composite Competence

Intermediate IT Spanish

: SimpleProfileElement

Type = Master’s Degree

Competence Ref = Expert Computer Scientist

Issuer Organisation = Hannover University

Date Issued = 08/04/2005

: SimpleCompetence

Advanced Spanish

Context Ref = Root

Prof Level Ref = Advanced

RCD Ref = Spanish

: SimpleProfileElement

Competence Ref = Intermediate IT Spanish

Issuer Organisation = Cervantes

: Composite Competence

Intermediate IT

English

: SimpleProfileElement

Competence Ref = Intermediate IT English

Issuer Organisation = TOEFL

: SimpleProfileElement

Type = Certification

Competence Ref = Java Expert

Issuer Organisation = Sun Microsystems

Date Issued = 02/03/2006

: SimpleCompetence

Advanced English

Context Ref = Root

Prof Level Ref = Advanced

RCD Ref = English

: Composite Competence

Java Expert

Context Ref = Prog. Languages

Prof Level Ref = Expert

: SimpleCompetence

Expert Servlet

Context Ref = Prog. Languages

Prof Level Ref = Expert

RCD Ref = Servlets

: SimpleCompetence

Expert J2EE

Context Ref = Prog. Languages

Prof Level Ref = Expert

RCD Ref = J2EE

: SimpleCompetence

Expert JSP

Context Ref = Prog. Languages

Prof Level Ref = Expert

RCD Ref = JSP

: Proficiency Level

Intermediate

: SimpleProfileElement

Competence Ref = Java Expert

: Proficiency Level

Beginner

: Proficiency Level

Advanced

: SimpleProfileElement

Type = Certification

Competence Ref = English

Issuer Organisation = TOEFL

Date Issued = 10/10/2004

: AlternativeProfileElement

English Vocabulary

: RCD

: AggregateProfileElement

Spanish Vocabulary

: RCD

: Proficiency Level

Expert

Prog. Languages

: Context

Label = Prog. Languages

: Alternative Competence

: Proficiency Level

Fluent

: Aggregate Competence

: SimpleProfileElement

Type = Master’s Degree

: Context

...

Label = ...

: Context

IT-...

Label = ...

Servlets

: RCD

: Context

Root

Label = Root

Spanish

: RCD

English

: RCD

: Context

Label = IT

: RCD

J2EE

: Required

Profile

: Acquired

Profile

: RCDJSP

PROFICIENCY LEVELS

CONTEXT

RCDs

Figure 4: Competence Proﬁle and Personal Proﬁle Example.

software engineer and have basic knowledge on eco-

nomics. Those represent the content (scope) required

to acquire the competence, independentlyof the grade

received by learners.

6 EXAMPLE

We assume the existence of repositories with infor-

mation about skills, proﬁciency levels, context and

competences as depicted in ﬁgure 4. In this work

we do not deal with the problem of ontology hetero-

geneity and we therefore assume that there either ex-

ist appropriate standards for this information or there

are available mappings between different ontologies

(see e.g. (Rahm and Bernstein, 2001; de Bruijn and

Polleres, 2004)). In addition, how these models are

instantiated is also out of the scope of this paper. We

assume the existence of appropriate tools to hide the

model from end users (e.g., competence management

proﬁle or CV creation).

Typically, a recruiter in a HR department would

write a job offer

Wanted: J2EE consultant

• Completed Master’s Degree (any faculty)

• Expert Knowledge in Java J2EE, Servlets,

JSP

• Very good English and/or French

Excerpt extracted from a newspaper

Among other drawbacks, such an advertisement does

not indicate what is mandatory or optional and, more

importantly, it is not machine-understandable. Per-

forming a manual matching (as widely performed

now from the recruiters), the recruiter will have a hard

time matching applications against this offer.

An alternative would be to use the model proposed

here, to encode the job advertisement (see ﬁgure 4).

The model not only enforces a well-structured pro-

ﬁling, it also saves the information in a machine-

readable and machine-understandable way. The re-

cruiter can as well reuse information created from

previous job advertisements (e.g., reuse the deﬁni-

tion of Java Expert for his company, as well as use

the well-accepted deﬁnition of Master). This ’index-

able’ representation also has signiﬁcant advantages

compared to the manual approach for the applicants:

the applicants can now quickly seek on the advertise-

ments, ﬁlter out advertisements for which their proﬁle

does not satisfy the requirements. In an even more ad-

vanced scenario, the proﬁle representation can enable

some ranking of the advertisements for which the ap-

plicant satisﬁes the requirements and some of the op-

tional competences. Finally, the cycle is concluded

when the applications come back to the recruiter. The

recruiter can use a (semi)automatic matching engine

to ﬁlter the non-satisfactory applicants according to

their proﬁles, and rate the suitable applicants. For

example, an applicant proﬁle as depicted in ﬁgure 4

would be a perfect match for such an offer. More

complex techniques could be used for partial matches

and rankings/ratings, as they have been hinted along

A MODEL FOR COMPETENCE GAP ANALYSIS

311

this paper or in (Colucci et al., 2003). However, elab-

orating on the matching techniques themselves is out

of the scope of this work.

7 CONCLUSIONS AND FURTHER

WORK

This paper addresses the problem of competence

representation and exchange. Current speciﬁcations

focus on the modeling of competencies (not com-

petences) and they miss important information that

should be included, such as proﬁciency level and con-

text. We provide a machine-processable representa-

tion of competences, relationships among them and

competence proﬁles. Such a model has been spe-

cially designed for reusability and allows advanced

algorithms for competence and proﬁle matching.

We are currently working on the development of

applications in order to help end users to provide such

competences and proﬁles. We will use our model

within two different areas. On the one hand, we

plan to develop advanced algorithms for competence

matching and gap analysis in the business context as

part of the EU PROLIX project. On the other hand,

we plan to apply to the creation of competence de-

velopment programmes and advanced assessment and

positioning services within the EU TENCompetence

project. Furthermore, we are in contact with repre-

sentatives of the IEEE LTSC WG20 on Competency

Deﬁnitions and the HR-XML Consortium in order to

contribute to the improvements of their speciﬁcations

according to the ideas presented in this paper.

REFERENCES

Cheetam, G. and Chivers, G. (2005). Professions, Compe-

tence and Informal Learning. Edgard Elgar Publish-

ing Limited.

Colucci, S., Noia, T. D., Sciascio, E. D., Donini, F. M.,

Mongiello, M., and Mottola, M. (2003). A formal

approach to ontology-based semantic match of skills

descriptions. J. UCS, 9(12):1437–1454.

de Bruijn, J. and Polleres, A. (2004). Towards an ontology

mapping speciﬁcation language for the semantic web.

DERI Technical Report.

HR-XML (2004). HR-XML Measurable Competencies.

http://www.hr-xml.org.

IEEE RCD (2005). IEEE 1484.20.1/draft - draft stan-

dard for Reusable Competency Deﬁnitions (RCD).

http://ieeeltsc.org/wg20Comp/Public/

IEEE 1484.20.1.D3.pdf.

IMS RDCEO (2002). IMS Reusable Deﬁnition of

Competency or Educational Objective (RDCEO).

http://www.imsglobal.org/competencies/.

Lau, T. and Sure, Y. (2002). Introducing ontology-based

skills management at a large insurance company. In

Modellierung 2002, Modellierung in der Praxis -

Modellierung f¨ur die Praxis, pages 123–134, Tutzing,

Deutschland.

Lefebvre, B., Gauthier, G., Tadi´e, S., Duc, T. H., and

Achaba, H. (2005). Competence ontology for domain

knowledge dissemination and retrieval. Applied Arti-

ﬁcial Intelligence, 19(9-10):845–859.

Rahm, E. and Bernstein, P. A. (2001). A survey of ap-

proaches to automatic schema matching. VLDB J.,

10(4):334–350.

Riehle, D. (1997). Composite design patterns. In ACM SIG-

PLAN Conference on Object-Oriented Programming

Systems, Languages & Applications, pages 218–228.

SRCM (2006). Simple Reusable Competency Map proposal

(SRCM). http://www.ostyn.com/resources.htm.

Sure, Y., Maedche, A., and Staab, S. (2000). Leveraging

corporate skill knowledge - from proper to ontoproper.

In Mahling, D. and Reimer, U., editors, 3rd Interna-

tional Conference on Practical Aspects of Knowledge

Management, Basel, Switzerland.

Webster. Webster online dictionary.

http://www.webster.com/.

Wikipedia. Wikipedia encyclopedia.

http://en.wikipedia.org/.

WEBIST 2007 - International Conference on Web Information Systems and Technologies

312