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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