Post on 21-Mar-2023
IT Knowledge Requirements Identification In
Organizational Networks: Cooperation Between
Industrial Organizations And Universities
Peteris Rudzajs1 & Marite Kirikova
2
1, 2 Department of Systems Theory and Design, Riga Technical University, Latvia.
1peteris.rudzajs@rtu.lv;
2marite.kirikova@cs.rtu.lv
Abstract: ICT professionals face rapid technology development, changes in de-
sign paradigms, methodologies, approaches and cooperation patterns. These
changes impact relationships between universities that teach ICT disciplines and
industrial organizations that develop and use ICT-based products. The required
knowledge and skills of university graduates depend mainly on the current indus-
trial situation therefore university graduates have to meet industry requirements
which are stated at the time point of their graduation, not at the start of their stu-
dies. Continuous cooperation between universities and industrial organizations is
needed to identify a time and situation dependent set of knowledge requirements,
which lead to situation aware, industry acknowledged, balanced and productive
ICT study programs. The paper proposes information systems solutions supporting
cooperation between university and industrial organizations with respect to curri-
culum development in ICT area.
Keywords: educational institution, knowledge requirements, study program, in-
dustrial standards
1. Introduction
Educational institution is a member of an educational “ecosystem” [12] that con-
sists of scientific and industrial organizations as well as public / governmental in-
stitutions and schools [19]. For the educational institution to be a productive
member of the ecosystem it is necessary to satisfy the needs of scientific, industri-
al and other organizations. In this paper we focus on the cooperation between in-
dustrial organizations and the university in the context of knowledge provision in
the ICT field. The paper addresses the problem that arises because of rapid devel-
opments and changes in ICT area, namely, the problem that industrial require-
ments stated at a particular time point, when a study program is started, have al-
ready changed at the time point when the first students graduate the program. In
order to identify, monitor, reflect and anticipate changes in knowledge require-
2 Peteris Rudzajs & Marite Kirikova
ments for both educational and industrial partners we propose to develop a sup-
porting education-industrial information system (EIIS). The paper presents the
educational knowledge requirements identification part of EIIS architecture. This
part of EIIS architecture is designed for handling heterogeneous sources of infor-
mation that are relevant for continuous knowledge requirements identification and
monitoring. The main research question addressed here is how to facilitate re-
quirements amalgamation and representation in a unified form that is relatively
easy to maintain and change.
The paper is structured as follows. In Section 2 the main problems of know-
ledge requirements identification are characterized, the proposed solution outlined
and related works briefly overviewed. In Section 3 the issue of knowledge source
and representation heterogeneity is addressed. The proposed modes of information
handling are described and exemplified in Section 4. Section 5 consists of conclu-
sions and directions of future work.
2. Problems, Proposed Solution, and Related Works
ICT field is the educational area that faces continuous changes in industrial re-
quirements of the knowledge possessed by university graduates. Those changes
are so rapid that frequently the industrial requirements that were taken into con-
sideration by universities at the time-point of students starting a particular study
program are not valid anymore at the time-point of graduation. In addition, indus-
trial representatives due to the necessity to focus on the utilization of advanced
technologies do not always estimate correctly the value of basic knowledge (e.g.,
physics and mathematics, systems theory, etc.) that contribute to abstract and sys-
tems thinking abilities of students [8, 1]. Small and medium companies are not
always able to follow advances in ICT field development and therefore cannot
state realistic requirements for their future employees’ knowledge; and, due their
not fully advanced knowledge in the field, do not trust university educators who
are more focused on future trends than on current situation. One of the reasons
underlying this problem is the lack of transparent knowledge development trends
representations that could be utilized by both university and industrial partners for
gaining mutual agreements and providing maximum of support to one another in
knowledge provision for ICT students. The purpose of research described in this
paper is to develop gradually required knowledge representation and monitoring
system that could provide educational institutions and industrial partners with a
transparent view on knowledge (skills, competencies) requirements in the field of
ICT development and use. Such system could be a core of EIIS continuously sup-
porting university and industry collaboration.
There are two essential challenges that affect the possibility to develop the
above mentioned system, namely (1) diversity, conceptual heterogeneity and wide
distribution of knowledge sources for requirements identification and (2) frequent
IT knowledge requirements identification in organizational networks: cooperation between in-
dustrial organizations and universities 3
changes in contents of identified sources. The architecture of an intended EIIS
therefore has to address these problems by providing multiple ways of information
gathering, fusion and representation. The part of EIIS architecture that addresses
these problems is presented in Figure 1. The figure shows a three layer architec-
ture where the central element is a knowledge external representation and moni-
toring service (10) that is intended to provide a transparent representation on
knowledge trends in ICT area. This service is supported by an internal unified
knowledge representation model/repository (8) that maintains not only current
knowledge representation, but also the history of representations (9). This model
is structured in three main knowledge subsystems where each has a different fre-
quency of changes. Knowledge representation should be made on the basis of
some existing skills frameworks that define the structure of skills (categories, sub-
categories, or the level of skills). Some examples include Skills Framework for the
Information Age (SFIA) [18], European e-Competence Framework [3] and others.
Vacancy
description model
Description model
of university
courses
Description model
of student
knowledge
Description model
of Occupational
standard of Latvia
Description model
of technology
courses
Body
of knowledge
(BOK)
Kn
ow
led
ge
req
uir
emen
ts i
den
tifi
cati
on
ser
vic
e Combination of model
instancesWEB
WEB----
---
----
<..>--
</..>
<..>--
</..>
WEB
WEB
DB
Unified knowledge
representation
model
(Knowledge
Classifier)
Core
knowledge
Advanced
knowledge
Situation specific
knowledge
Appilcations &
Technologies
1
2
3
4
5
6
7
8
9
Knowledge and change identification service
SKCI SKRq
Internal and external knowledge
representation and monitoring services
SKRM
10Knowledge monitoring and
external representation
services
Figure 1. Part of EIIS architecture
Changes into the unified internal knowledge representation model are requested
by the knowledge requirements identification service SKRq (7). This service, in
turn, is supported by several knowledge identification and change services (SKCI)
Information about required / obtainable / obtained knowledge could be retrieved
from: (1) employers’ published vacancies, (2) register of national Occupational
standards (3) descriptions of industrial certification (technology-oriented) courses,
(4) descriptions of university courses, (5) descriptions of student knowledge, (6)
4 Peteris Rudzajs & Marite Kirikova
descriptions of the so called Body of Knowledge (BOK) standards for education
(both academic and professional) such as Business Analyst BOK, Software Engi-
neer BOK, Project Management BOK, etc. [5, 10, 11, 15]. SKCI level supports
SKRq level, which, in turn, supports internal and external knowledge representa-
tion and monitoring services (SKRM). Thus the system is “fed” by SKCI. The
number of these services is not limited to the ones presented in Figure 1. Two of
the presented services (3 and 4) utilize internal systems feedbacks additionally to
the investigation of external environment. Multiple knowledge identification ser-
vices are used due to the heterogeneity of information sources. Each service pro-
vides several modes of operation (starting from manual to fully automated ones),
that allows flexible customization of information acquisition depending on the ac-
quisition purpose and availability of source knowledge.
The development of EIIS, in general, and services described in this paper, in
particular, are based on related work in organizational ecosystems [12, 7], intelli-
gent agents [24], information fusion [22], ontology matching and maturing [4, 2],
as well as knowledge mapping in the area of ICT [21, 14], and business intelli-
gence [23].
3. Diversity of Knowledge Requirements Sources
Every previously identified source has its own form of knowledge representation –
usually free formed description of information published in Web or sent to educa-
tional institution by e-mail. Other information forms include databases, annotated
texts [20] and conversations with the employers. Each SKCI level service can
provide information source searching facilities, information change searching fa-
cilities (for sources already found) and information retrieval facilities in fully au-
tomated, semi-automated and manual modes. Information changes searching web
crawler is developed [17] and used as a part of any SKCI level service. Methods
of information handling that utilize the crawler together with manual operations
are described in Section 4. Information sources of knowledge requirements and
forms of information relevant for invoking particular SKCI corresponding to the
type of information source are summarized in Table 1.
As mentioned before, the forms of information in knowledge requirement
sources are different. The first step to start using information about knowledge re-
quirements is to retrieve and transform it. To structure the retrieval and transfor-
mation efforts, the initial general source sensitive description models were devel-
oped. They are represented in Figures 2 to 6. We can see that these models differ
widely one from another. Therefore SKRq is needed to handle these differences
and prepare information for inclusion in the unified knowledge representation
model (SKRM layer in Figure 1).
As proposed in the information system architecture of knowledge monitoring,
one of the information sources is vacancy descriptions (Element 1 in Figure 1).
IT knowledge requirements identification in organizational networks: cooperation between in-
dustrial organizations and universities 5
Table 1. Information sources of knowledge and their forms
Information source /
form
Vacancy
description
Occupational
standard
Technology
course de-
scription
University
course de-
scription
Student
knowledge
description
BOK
1. Annotated text + +
2. Free form text + + + + +
3. E-mail + + + +
4. Conversation + + +
5. Database + +
The largest variety of forms is found in employers’ published vacancies. These
forms include: (1) annotated text, (2) free form text, (3) e-mail and (4) conversa-
tions with the employers (see Column 1 in Table 1). To make clear the conceptual
structure of vacancy contents published by employers, simplified description
model (see Figure 2) is constructed. In the published vacancies we usually can
find such conceptual parts as: (1) title of vacancy (occupation), (2) brief descrip-
tion of vacancy followed by knowledge requirements - (3) requirements of educa-
tion, (4) requirements of knowledge in languages, (5) experience in occupation re-
quirements, (6) specific knowledge requirements (this describes required
knowledge of various programming languages, environments, tools, operating sys-
tems, web technologies, system analysis and design, project management skills
etc.), (7) general knowledge requirements. In Figure 2, specific knowledge is de-
scribed in more detail than usually it is available in vacancy descriptions.
National occupational standards (relevant for Element 2 in Figure 1) are usually
published in a structured manner, i.e., particular information units can be empha-
sized in the description of occupation: number of registration, occupation title, qu-
alification level, employment description, tasks, skills (classified as common skills
in industry, specific skills in occupation and general skills / abilities) and know-
ledge levels – idea, understanding and usage - are specified [16]. Conceptual mod-
el of occupational standard description is given in Figure 3. E.g., in the registry of
Occupational standard of Latvia [16] there are described (standardized) various
occupations in the field of computer science and information technology. They
are: programmer, software engineer, computer system and computer networks
administrator, system analyst, computer system technician and information tech-
nology project manager. These standards are freely accessible online [16].
6 Peteris Rudzajs & Marite Kirikova
Figure 2. Description model of a vacancy
Figure 3. Description model of occupation in
Occupational standard
Figure 4. Description model of technology
course / exam
Figure 5. Description model of university
course
The purpose of technology courses (relevant for Element 3 in Figure 1) is to
certify a person in the area of some technology, e.g., Microsoft offers certification
exam “Designing, Assessing, and Optimizing Software Asset Management
(SAM)” [13]. After the completion of this exam, Microsoft issues a certificate,
what acknowledges the acquired knowledge and skills. The descriptions of exams
are publicly available [13, 9]. The description defines required preliminary know-
ledge and expected knowledge (in the form of the name of knowledge elements
and their description) after the exam has been taken. After analyzing certification
courses and exams offered by IBM and Microsoft, a conceptual model of their de-
scription was constructed (see Figure 4).
In an educational institution every course (relevant for Element 4 in Figure 1) is
planned by defining various attributes, such as course name, field of specializa-
tion, purpose and tasks of the course in the form of skills and competencies, and
the description of topics covered by the course. Conceptual model of course de-
scription is given in Figure 5.
By taking university courses, the student of a higher education institution ac-
cumulates knowledge. Therefore university courses could serve as the basis for
student knowledge identification. By combining courses we could get the “ideal”
set of student’s accumulated knowledge. In the ideal case – the student has fully
IT knowledge requirements identification in organizational networks: cooperation between in-
dustrial organizations and universities 7
obtained knowledge from his courses. The prerequisite to map student knowledge
to unified knowledge representation model is to have university courses mapped
to unified knowledge representation model. In the next step, courses taken by stu-
dent should be identified by describing the level of obtained knowledge units
(measuring as – partially, average, fully). This ensures the evaluation of real
knowledge obtained by the student. The conceptual model of student knowledge is
given in Figure 6.
Figure 6. Description model of student knowledge
To complete student knowledge we should use the results of knowledge
represented by university and technology courses. In this case, the knowledge ob-
tained from the courses is automatically assigned to the student. For assignment of
courses taken by student, the functionality of SKRq can be used.
BOK standards are defined by the relevant professional association. This leads
to differences in the structure of these standards. We propose to identify the name,
developer association, version and the year of publication of every BOK standard.
These standards are significant both for educational institutions and for employers
in their industry, because they define best practices in various occupations. There-
fore a periodical review and analysis of these standards are vital for the develop-
ment of educational knowledge requirements.
4. Customized Handling of Knowledge Requirements Sources
In order to retrieve and analyze knowledge contents of identified knowledge
sources it is necessary to take into consideration the form of information represen-
tation (rows in Table 1 and columns in Table 2). Information handling methods
depend also on the type of knowledge sources represented in columns of Table 1.
In this section we discuss information handling methods for three sources. The
method of vacancy description handling is the most complicated and therefore is
described in detail as an example for better understanding of other methods. Sev-
eral other information handling methods are still under the development and are
not considered in this section in detail.
8 Peteris Rudzajs & Marite Kirikova
4.1. Method for vacancy description handling
To get information about vacancies, appropriate methods and technologies should
be used for every form of information source (Tables 1 and 2):
1. Retrieval of annotated vacancy description is possible with the use of Web
agent. In this case Web agent (crawler) searches the Web, identifying links
to vacancy descriptions. Identified annotated descriptions of vacancies can
be presented by different document models. If the annotation [20] pub-
lished by the employer would be built by using unified knowledge repre-
sentation model, transformation could be done semi-automatically. If the
annotation is structured differently, then the published model initially
should be mapped manually, and next time when a similar model is re-
trieved, the mapping process may be done automatically on the basis of the
initial mapping between models.
2. Web agents are useful when free text form vacancy description has to be
retrieved. After retrieving vacancy description, the contents needs to be
structured, i.e., description should be mapped to unified knowledge re-
quirement model manually.
3. If a vacancy description is sent via e-mail, the content should be mapped to
unified knowledge requirement model.
4. In conversations with employers useful information could be captured,
e.g., the employer describes future needs for specialists and gives their de-
scription defining knowledge and skills expected after some period of
time. This type of conversations is important for educational institutions as
the trends in the labor market could be noticed to deliver particular option-
al courses in order to prepare students for the chosen vacancies and satisfy
market demand.
In cases 2, 3 and 4 definition of SKRq is required (Element 7 in Figure 1). The
main role of this service is to help to structure and to map identified knowledge to
unified knowledge representation model. This service provides automatic analysis
of the terms in the vacancy description. On the basis of the analysis of knowledge
requirements, recommendations are prepared. For example, Web agent has re-
trieved a vacancy description; the next step is to process it manually (map to uni-
fied knowledge requirement model). To automate this process we can use know-
ledge requirements identification service – vacancy description is analyzed for
known terms describing knowledge requirements. These terms are suggested to
user to ease the process of information rereading and the structuring of knowledge
requirements. To implement these suggestions, initial dictionary (ontology) of
terms used in vacancy descriptions should be engineered.
Vacancy retrieval process depending on information representation form is ex-
emplified in Table 2.
IT knowledge requirements identification in organizational networks: cooperation between in-
dustrial organizations and universities 9
Table 2. Vacancy retrieval process depending on information representation form
1. Annotated text 2. Free form text 3. E-mail 4. Conversation
The need for specialist Future need for spe-
cialist
Employer defines knowledge requirements for specialist
Employer produces vacancy description including required knowledge and skills
Employer publishes
vacancy description
in free form text
with annotation
Employer publishes
vacancy description
in free text form
Employer sends va-
cancy using e-mail
In the form of conver-
sation employer de-
scribes future needs
for vacancies and
gives their description
Web agent finds published vacancy by
searching the web
Web agent informs users who are responsi-
ble for employers’ database about the va-
cancy found
There was made
mapping for anno-
tated text to unified
knowledge represen-
tation model.
Vacancy description is reviewed
Vacancy description
is automatically
transformed to uni-
fied knowledge re-
presentation model
Acquired knowledge about vacancies ma-
nually is transferred to unified knowledge
representation model by extracting required
knowledge, skills, and abilities. This could
be done by using Knowledge identification
service
Acquired knowledge
about future vacancies
is transferred manually
to unified knowledge
representation model
by specifying the time
when vacancy could
open. Knowledge
identification service
can be used.
Knowledge identification tool looks for
“known” terms identifying knowledge re-
quirements in vacancy description. These
suggestions support manual description
analysis
Submit a knowledge requirements to unified knowledge representation model
It is necessary to note that multilingual [6] information identification and retrieval
services are needed because of global nature of IT labor market.
10 Peteris Rudzajs & Marite Kirikova
4.2. Method for occupational standards handling
For handling occupational standards we propose periodical browsing of registry of
occupational standards by crawler and identifying changes in desired (e.g., system
analyst) occupation descriptions. This is the way for ensuring up-to-date informa-
tion about knowledge and skills required in a certain occupation. Standard change
frequency is low, e.g., most of the standards [16] were last time changed in 2002
and 2003. Web agent searches the registry, identifying new standards or identify-
ing changes in existing ones. If changes are identified, standard review is required
to determine what exactly has changed. In this step knowledge identification ser-
vice could be used. Knowledge units in standard description should be identified
and mapped to unified knowledge representation model.
4.3. Method for technology course description handling
After analyzing defined knowledge units in the descriptions of technology courses
and exams, two types of information retrieval were identified – (1) search the Web
and (2) receive course lists and their descriptions from some collaborative institu-
tion (industry partner).
If course descriptions are published in Web, they can be retrieved using Web
agent technology – search the Web, identify courses and represent search results
to a user. In the search results we could identify three cases: (1) new courses are
discovered, (2) changes in existing courses are identified comparing with previous
search results and (3) some courses could not be found anymore. In the first case,
by using knowledge identification service, mapping to unified knowledge repre-
sentation model should be done. In the second case, selection of results from pre-
vious mapping results and remapping the changed ones (by identifying new know-
ledge in existing courses) should be done. If the conditions for the third case are
true, then the course should be deleted from the database keeping existing map-
ping of terms. These mappings could be used for other course descriptions to map
new knowledge.
When the educational institution receives e-mails from some collaborative
partners, the flow of events is similar to the above mentioned only with one excep-
tion – Web agents are not used.
5. Conclusions
The paper presents a part of architecture of EIIS that is developed with the pur-
pose to establish continuous collaboration between the industry and university
IT knowledge requirements identification in organizational networks: cooperation between in-
dustrial organizations and universities 11
with respect to transparent and well motivated student knowledge development
strategies in the area of ITC education. The sub architecture of EIIS consisting of
three layers (knowledge and change identification, knowledge requirements iden-
tification, and internal and external knowledge representation and monitoring) are
proposed and described. The paper focuses mainly on the first layer of the archi-
tecture by describing a variety of knowledge sources and corresponding informa-
tion handling methods. The web agent/crawler that is used in several steps of the
methods has been developed and tested. First attempts for organizing unified re-
presentation model have been made, however further research is needed to achieve
minimization of manual comparison of knowledge structures. Mapping of all de-
scription models into unified knowledge representation model would give an op-
portunity to analyze different aspects of student knowledge and automatically or
semi-automatically [19] obtain information regarding the following indicators:
correspondence of student knowledge to occupational standards
correspondence of student knowledge to different occupations
correspondence between knowledge required by vacancies and knowledge pro-
vided by study courses (mandatory and elective)
correspondence of university courses and industrial certification courses
etc.
Research work presented in this paper revealed that ITC organizations differ con-
siderably with respect to detail of their internal job descriptions and in many cases
their wishes change when explicit knowledge about university knowledge devel-
opment is presented. They see the EIIS as a tool for workspace acquisition and
analysis of labor market knowledge requirements. Most probably each component
of the system will have to be used in all three modes (manual, semi automated,
fully automated modes), depending on the organizations that use EIIS and availa-
ble information sources of knowledge requirements. Nevertheless one of the main
directions of future work is introduction of more sophisticated ontology matching
and information fusion methods. Another direction of future research is a more
formal utilization of feedback mechanisms in the university-industry ecosystem in
general, and in EIIS in particular.
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