DESIGNING A DSS FOR HIGHER EDUCATION MANAGEMENT

Vasile Paul Bresfelean, Nicolae Ghisoiu, Ramona Lacurezeanu

Mirela Pop, Miranda Vlad and Ovidiu Veres

Babes-Bolyai University, Faculty of Economics and Business Administration, Cluj-Napoca, Romania

Keywords: Decisions, Information and Communication Technologies (ICT), Decision Support Systems (DSS), Data

Mining.

Abstract: In order to achieve quality in education there is a need to optimally combine a complex of factors for the

education system to be developed to highest standards. Competitiveness of higher education institutions is

closely related to the development of information technologies, as key factor for future European citizens, to

beneficiate from The European Higher Education Area and the collaboration in education. Data mining thus

appears as one of the opportunities that have been less exploited, but whose application increases steadily,

to solve various problems by analyzing data already present in the databases of various institutions. One of

the viable solutions to support decision makers may possibly be the decisions support systems (DSS) which

integrate specific tools to assist decision together with those of general use to form a constituent part of the

institutions’ information system. In this paper the authors present the design of a DSS which integrates data

mining technologies, with the purpose to assist the managers of higher education institutions in quality

decision-making processes.

1 INTRODUCTION

Seen in the light of rapid changes of our society, the

access to information is a requirement of prime

importance in any organization that wishes to have a

competitive presence in the market field. Managers

want accurate and current information provided in

real time, in an appropriate format and at low costs.

In recent years, rapid developments in ICT have

contributed to a significant increase in the global

computer network. The Internet has evolved from a

network for researchers and academics, to a platform

which gave the opportunity to find new ways to

offer products and services.

We are witnessing profound transformations in

the manner of transfer and knowledge management.

Internet and ICT have had a major effect in the way

educational institutions operate - in the sense that

they made it possible for many inventive teachers to

study new methods for the development of education

management and training opportunities.

Due to the increased data volume, and especially

because of the complexity of data and relations

between them, the possibility for a user to find links

between various events encapsulated in the

information systems stored data, is becoming ever

smaller. In this context, appeared the need to create

automated tools which can transform data contained

in various databases by proper processing it into

information and knowledge useful especially to

decision-making. Here come the solutions like

decision support systems and data mining, which

find and automatically or semi-automatically verify

links between related events. The basis of these

systems is to offer the information support needed to

mitigate the effects of limits and restrictions faced

by human decision maker, with the intention to

resolve various decision problems.

In this paper we proposed an instrument to

support the managers of higher education

institutions in quality decision-making processes,

based on data mining technologies integrated in an

academic DSS prototype. The main objective was to

provide them with ample information and

knowledge to prepare new assumptions, in a short

period of time, which is presently hard to achieve,

and also to suggest viable decisional alternatives.

335

Paul Bresfelean V., Ghisoiu N., Lacurezeanu R., Pop M., Vlad M. and Veres O. (2009).

DESIGNING A DSS FOR HIGHER EDUCATION MANAGEMENT.

In Proceedings of the First International Conference on Computer Supported Education, pages 335-340

DOI: 10.5220/0002012603350340

 SciTePress

2 DECISIONS AND DECISION

SUPPORTING

TECHNOLOGIES

The decision is the outcome of a conscious activity

to choose a course of action and to engage in it, as a

result of processing information and knowledge

(Filip, 2005). It belongs to a person or group of

persons who have authority and responsibility for

the efficient use of resources. The decision is meant

to be a fundamental element of managerial activity,

as an expression active, dynamic management,

through which it fulfils its functions. Decisions

taken in the field of education have a much more

complex than in other social fields, because the

educational activities, scientific research, highlight

the individual in training under multiple aspects:

professional training but also mental, physical and

moral development etc. (Atanasiu, 2001).

The mission of the university is to prepare

personnel to the highest level of knowledge at that

time in history (Marga, 2007). The functions that

comprise its mission are diverse and claim distinct

actions, each being met by thematic areas and

guidelines. The multicultural profile of Babes-

Bolyai University of Cluj-Napoca is accentuated

with the development of international dialogues and

intercultural approach cultivated by the European

Union. Each of the leading persons involved in the

decision-making positions have coherent and well

set tasks for taking the best decisions, therefore

Babes-Bolyai University has increasingly become an

innovative and prolific actor in interaction with the

economic, administrative, and cultural environment.

2.1 Decision Support Systems

Over the years, support for decision making has

taken a diversity of forms, and as the forms have

evolved, decision making support has become more

comprehensive and integrated. Today, there are

several system alternatives available, and matching

the suitable system to the particular problem or

opportunity has created new tasks for management

(Forgionne, 2003).

Decision Support Systems (DSS) represent a

specific class of information systems designed to

help users which rely on knowledge, in various

decision-making positions to solve the encountered

problems that matter for the organization’s

prosperity (Filip, 2005). The support received by the

decision takers, whether they are top executives,

managers placed on different levels (rector, dean

etc.) advisers and other assistants lies primarily in

helping them to overcome the limits of knowledge

regarding the problem, possible alternatives for

action and methods of analysis used in the decision.

Decision support systems are designed to assist

and support the decision making processes, and

centre on the efficiency of this processes and the

precision of the resultant information. The

development of this type of system to assist in

problems of analysis, solutions building and decision

making is presently a major challenge in the

academic management. A tendency of the actual

decision support systems is to facilitate

communication and cooperation between

participants in the collective decision or between

those who make decisions on the one hand, and

those who are meant to provide the necessary

information or to execute alternative adopted, on the

other hand.

D.J. Power offered an extended classification of

DSS based on the dominant technology that

determines the features of the decision-making

(Power, 2003): communications-driven DSS, data-

driven DSS, document-driven DSS, knowledge-

driven DSS, and model-driven DSS. Some decision

support systems are hybrid systems driven by more

than one major component.

DSS include applications in numerous areas,

such as (Kersten et al., 2002): environmental

decision making and assessment, water resource

management, agriculture, forestry, manufacturing,

medicine, business and organizational support,

infrastructure etc. Software products like Expert

Choice (EC), Exsys, Braincel, Evolver, Excel and

other DSS packages, are examples of DSS

capabilities and development. Over the years, further

categories of systems have been developed to offer

decision support, such as group decision support

systems, expert systems, executive information

systems, knowledge management systems, genetic

algorithms, intelligent agents, fuzzy logic etc.

2.2 Data Mining and Decision Support

Data mining is the process of extracting implicit and

viable information and knowledge which presents

interest in processing of large data sets. There are

many examples of data mining successful

application in different areas: marketing and CRM

(Customer relationship management), fraud

detection, financial and banking processes,

astronomy, genetics, text mining, Web mining etc.

Data mining represents a novel research

technology which is being implemented in education

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with several promising areas for data mining

suggested and partially put into practice in the

academic world. Particular attention was given

recently to broader implications of data mining

technologies in the field of education, and

particularly in higher education, applications related

to the students’ and alumni future career and

educational paths, to the students’ "baggage of

knowledge", predictions of academic failure, school

drop-out, as well as those based on data extracted

from on-line educational systems (portals, courses,

tutorials, on-line examination etc.), and virtual

communities (Bresfelean, 2008). The educational

data mining was defined as “the process of

converting raw data from educational systems to

useful information that can be used to inform design

decisions and answer research questions” (Heiner et

al., 2006)

According to recent trends, the number of

students has increased in several faculties and

specializations in the higher education institutions;

the city of Cluj-Napoca, one of the regions with the

fastest economic development, has one of the largest

numbers of students in the country. A major concern

in the academic institutions is the prediction of

students’ and graduates’ behavior in order to

maintain and increase the number of students, to

attract them to continue education through master

and doctorate studies in the same institution.

Given the large number of faculties of various

specializations, each with a variety of departments

and fields of postgraduate study, continuing

education may become a question mark. This

depends on students’ personal reasons (family

support, current employment, ambitions for the

future, etc.), but also on the educational environment

in which they operates (technical equipment to

faculty, quality of course materials, teaching quality,

practical and research activities along with the

teachers, relaxed curricula, etc.).

Understanding, prediction and prevention of

academic failure are complex and continuing

processes, anchored in the past and at present

through the information collected on scholastic

situations, various surveys and tests applied to

students, as well as information resulting from

research activities, based on data mining

technologies. Constructing a typical profile for

students, and also grouping them on the basis of

exam failure and continuing education can help both

higher education institution and its students.

Universities can take such views to meet students’

opinions on educational processes, curricula,

courses, equipment, specific learning gaps and also

students’ requests for further assistance needed for

graduation.

Data mining and decision support are two

disciplines aimed at solving difficult practical

problems, and in many ways they are

complementary (Bohanec & Zupan, 2001). To solve

a particular problem, decision support tends to rely

on knowledge acquired from experts, while data

mining attempts to extract it from data. Their

combination would result in important benefits in

solving real-life decision and data-analysis

problems:

- Data mining has the prospective of solving

decision support problems, when earlier decision

support answers was recorded as analysis data to be

used with mining tools.

- Decision support methods typically product a

decision model, proving the expert knowledge of

decision makers.

3 DESIGNING A DSS IN HIGHER

EDUCATION MANAGEMENT

Designing ICT based systems for the higher

education institutions presents some common

aspects with the design of economic systems, but

with a series of special features specific to

academics. These issues are related to the

functioning and organization of educational

institutions that have developed in a rapid pace.

There should be taken into consideration the

university autonomy, even in financial issues or

other forms according to public and academic

responsibilities. These are some of the main reasons

why an in-house developed system can be better

suited to the specific needs of a user in an higher

education environment. Universities are at the heart

of the community and an integrating part of them,

having central tasks in education, training, research

and other activities, integrated into a whole, called

education system.

The higher education’s result/product is the

knowledge provided under various forms to the

society (Popescu, 2008):

- Graduates’ competences in specific areas;

- Results of scientific research - broadening the

horizon of human knowledge;

- Consultancy, expertise, knowledge transfer to

organizations belonging in socio-economic

environments, in order to generate novelty and

increased their competitiveness.

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337

- The involvement of academic community

members in the society - the transfer of knowledge

achieved through direct involvement in

organizations of the local, regional, national and

international community.

Among the activities to be carried out by the

designed decision support system, we mention:

- Providing a suitable framework for representing

data;

- Modeling and processing data, performing

operations on the representations previously

established;

- Administrating more complex representations

of the data: graphs, tables, images and other

synthetic representation of data;

- Providing new means of data processing and

making models (extraction and aggregation of

certain data, calculate various indicators, graphics,

comparisons, trends, etc.).

- Integrating modules to generate knowledge

(data mining) and suggesting alternatives in making

decisions.

3.1 DSS Existing and Newly Generated

Data

The in-home DSS for higher education management

system was designed to work best with the existing

data of the Babes-Bolyai University (UBB) and the

Faculty of Economics and Business Administration

(FSEGA), using the separated databases of the

following systems:

• The research activity management system - for

teaching staff and research management of the

departments, faculties and administration. Its

aim is to highlight the research activity of

teachers and synthesize it to different levels.

• ManageAsist system - developed to model

administrative activities at UBB and for the

integration of administrative facilities.

• The EvidScol system - related to the

management of school records (plans for

education, faculties, students and their grades),

• The AcademicInfo - centralizes databases of

Secretariats providing facilities to access the

information registered in the Secretariat,

teachers and school management, resulting a

Web based gradebook available online for

students (access founded on code numbers and

passwords).

• The application Taxes for fee management in

the FSEGA.

• The web education portal available for online

and distance education (ODL).

Data mining extracted data is centered on two

main methods: classification learning and data

clustering, and with the objective to build a typical

profile for students and to predict their options,

based on exams failure and continuing education.

The higher education institution can learn about the

students’ content/discontent regarding the

educational processes, the curricula, courses,

equipment, and can also discover specific learning

gaps and students who might require extra attention

and training in order to graduate, and also can

improve teaching methods and educational

management processes.

3.2 DSS Architecture

The design process followed a thoughtful analysis

and interpretation of the actual systems’ models in

order to use them later in the decisions, concerning

the creation of a resourceful new system (Figure 1).

The DSS is designed using a modular structure, in

accordance with the basic activities of the higher

education institution: teaching activities (Teaching

module), scientific research (Research module) and

scholastic situation management (Students module).

It is developed from the model presented in first

author’s book (Bresfelean, 2008).

The DSS has 3 main modules (Figure 2):

Students, Research and Teaching. The Students

module represents the results and activities of

FSEGA students, with data extracted from

scholastic, fees, databases, questionnaires, and the

eLearning platform. It serves as the basis for

building and providing alternatives in decision-

making on students’ issues, based on the internal

procedures of FSEGA. In this respect, based on the

data, tables, graphs, the results of data mining

processes, the system is able to suggest decisional

alternatives in the following situations:

- Students’ registration

- Students’ transfer

- Suspension / extension of studies,

- Other specializations,

- Students’ reclassification

- Granting dormitories,

- Granting scholarships,

- Tutors,

- Career Guidance, etc.

The Research module includes the performance

achieved in the scientific research by the teaching

staff, departments, PhDs, etc. and it is based on the

databases extracted from the scientific research

management system, the Department reports,

questionnaires.

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Figure 1: Main architecture of the designed DSS for higher education management.

Figure 2: Module map of the designed DSS.

The Teaching module represents the work of the

teaching staff of the faculty, through data extracted

from staff assessment (by colleagues, students, and

managers), databases of the faculty and university.

Research and Teaching modules serve as the basis

for building and proposing alternatives in decision-

making on the staff’s management engaged in

teaching activities and scientific research, based on

the internal procedures of FSEGA, in the following

situations:

- Evaluating teaching performance,

- Scientific research evaluation,

- Establishing salary coefficients (depending on

performance)

- Establishing human resources strategy,

- Job opening and interviewing for teaching /

research positions,

- Insurance jobs associated with teachers and

overlapping,

- PhDs activity and evaluation,

- Laying Optional courses packages,

- Drafting of new teaching materials, etc.

The system is designed to provide the higher

education managers, in this context, with important

DESIGNING A DSS FOR HIGHER EDUCATION MANAGEMENT

339

tools to facilitate their actions in decision-making

activities. These features are available through the

user interface, screen formats, menus, graphs,

information and knowledge generated by data

mining processes integrated in the DSS.

4 CONCLUSIONS

In an attempt to support the managers of academic

institutions in decision-making processes, we

proposed in this paper the design of a DSS which

also incorporates data mining technologies. This

study is part of a range of activities that has evolved

continuously, from the revision of several aspects of

general education in Europe, higher education

management, single issues in the implementation of

information technology, to the personal

achievements to assist the higher education

managers in decision-making processes.

Prospects for further research directions rely on

the grants of the Business Information Systems

department and on the general objectives of the

Strategic Program of Babeş-Bolyai University of

Cluj-Napoca for 2007-2011. These will include:

- Further research to develop and implement the

academic decision support system, and fully

integrate other modules;

- The application of other modern technologies,

such as data warehouse, to the new system

architecture;

- The continued application of data mining

technologies on scientific research databases and

teaching activities, to disseminate the results of

research activities and teaching;

- Comparative analysis on the new direction of

higher education and the results obtained in terms of

integration into EU structures.

ACKNOWLEDGEMENTS

The research from the present article is a part of the

Romanian CNCSIS IDEI 1598 grant “Invatamantul

superior si piata muncii. Cercetari bazate pe

tehnologii informatice privind corelatia dintre

calificarile cerute de piata muncii si cunostintele

reale ale studentilor”, manager Nicolae Ghisoiu,

Professor Ph.D.

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