USING A COMPUTER-AIDED PBL APPROACH IN THE DESIGN

OF A COURSE IN ENTREPRENEURSHIP AND MANAGEMENT

João Luís de Miranda

Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre

Lugar da Abadessa, apt 148, 7301-901, Portalegre, Portugal

Keywords: Computer Aided, Problem-Based Learning, Decision Support, Optimization, Multivariate Analysis.

Abstract: In a Management Science MSc. course, we use a computer-aided approach to conjugate multivariate

analysis with decision support through optimization tools. The targeted decision support is based on a

methodology that relies on specific problems built to promote learning. The optimality of discrete decisions

on uncertain environment is aimed, they are applied computational tools onto multivariate analysis and

optimization frameworks and these tools support the development of decision rules. Case problems

specifically built are focused, as investments programming, financial risk treatment, or supply chain

planning and distribution activities, on a Problem Based Learning (PBL) approach. This PBL methodology

is embedded on a context where we simulate enterprise computer-aided activities and this immersive

approach is encapsulated by a blended learning framework. Considering the intensive schedule that is

defined to cope with the student-workers availability, this approach avoids some dislike related to long

lectures of quantitative kind, which can reach eight hours in continuum.

1 INTRODUCTION

In this paper, it is presented a computer-aided

approach that we had applied to conjugate

multivariate analysis and decision support, in a

course of a Management Science MSc. program: the

Entrepreneurship and Management of Small and

Medium Enterprise (SME).

The lectures schedule assumes an intensive and

aggregative form, defined this way to accomplish

with student-workers availability. Ours lectures of

mathematics kind are assigned in blocks of four

hours, and this can originate some dislike among

students. Then, we had built specific and realistic

cases, where decision support principles are focused,

based on optimization of probabilistic measures.

Consequently, the computer-aided approach that

underlies the learning process is compulsory.

The case problems are focusing investments

programming and financial risk treatment, or supply

chain planning and distribution activities. We

present a Problem Based Learning (PBL) approach:

this methodology is based on problems, and it is

embedded on enterprise and businesses contexts.

Also, enterprise computer-aided activities are

simulated, and a blended learning framework

encapsulates all the procedures.

The structure of the paper is as follows: in

Section 2, the purposes of the Management Science

MSc. program and the course addressed in this paper

are synoptically described; in Section 3, it is

described how the learning methodologies, based on

problem specifically built are conjugated with the

computer-aided environment and assessment; then,

two illustrative examples are described in Section 4,

focusing supply chain planning and investment and

financial issues; finally, main conclusions are

presented in Section 5.

2 BRIEF DESCRIPTION OF THE

MSC PROGRAM

In this Section, they are synoptically described the

purposes of the Management Science MSc. program

that aims entrepreneurship and small and medium

enterprises (SME), and the course that focuses

optimal decision rules.

171

Miranda J. (2010).

USING A COMPUTER-AIDED PBL APPROACH IN THE DESIGN OF A COURSE IN ENTREPRENEURSHIP AND MANAGEMENT.

In Proceedings of the 2nd International Conference on Computer Supported Education, pages 171-176

DOI: 10.5220/0002783401710176

 SciTePress

2.1 The Entrepreneurship and

Management of SME MSc.

Program

The MSc. program in Entrepreneurship and

Management of SME is developed (Mourato, 2007)

with the purpose to train business professionals

which are able to:

• Develop business plans;

• Implement businesses;

• Develop, implement and monitor business

strategic plans;

• Develop, implement and monitor systems of

performance assessment;

• Develop marketing plans;

• Engineer and re-engineer financially enterprises;

• Develop, implement and monitor innovation

systems.

In this program, it is aimed that the student has

competences to develop activities in each level of

the enterprise. That means the student must be able

to design a business plan, to assume risk as

entrepreneur, to design a project and to develop the

related technical activities, and to supervise the

evolution of the planned activities. Also, the student

must understand the environment where he is

placed, together within the main line forces. The

polyvalence targeted is developed through the

conception of a final project.

Accordingly with the described characteristics,

the following professional profiles are targeted:

• The entrepreneur, who must be able to transform

and idea in a business, to develop strategic

understanding, to materialize its organization

capacities, to master bargain techniques;

• The project coordinator, who must present fine

tuned leadership capacities and to master

planning techniques and tools;

• The technical director, who must to master

techniques of several fields, such the financial,

the marketing and the organizational behaviour

fields.

• The controller, who must be capable to monitor

the enterprise performance in several specialties,

and to promote relationships development.

It must be referred the wide interest revealed in

this MSc. program dedicated to Entrepreneurship

and Management of SME, with a large number of

candidates. The selected candidates are student-

workers, a status that is also addressed by the

intensive schedule of lectures. Some of the students

are in-between jobs, but there is also a group with

responsible posts (Figure 1).

dministrative &

Accounting

Services - 6

Commercial &

Customer Services -

Direction &

Entrepreneurship - 4

In-between jobs - 9

Teaching & Training

- 2

Figure 1: Function types of the Msc students.

Nevertheless the schedule or the time availability of

the student-workers, a course that treats multivariate

analysis and decision support is crucial on the real

world of entrepreneurship and enterprises.

2.2 The Multivariate Analysis and

Decision Support Course

The course dedicated to multivariate analysis and to

decision support is focusing the treatment of

uncertainty related to decision making, on a context

of enterprise management. The treatment of

uncertainty, together with the assessment of risk, is

based on statistic analysis conjugated with

computational implementation.

The target is the efficient use of multivariate

statistics, from data gathering to data treatment, and

also focusing the insight on results. Then, the

decision support is based on probabilistic methods of

optimization (Mathematical Programming), by

applying parametric and stochastic approaches, to

define and adjust decision rules. The course is thus

formally partitioned in two modules, but there is

built the underlying connection in between, through

the learning methodology that is based in case

problems and supported in computational tasks, as

described in Section 3.

The specific purposes of the first module,

dedicated to multivariate analysis, are related to the

critical use of the methodologies that aims data

gathering, data treatment, analysis of results, and it

includes:

• The multivariate analysis basics;

• The organization and treatment of multivariate

data;

• Forecasting;

• Hypothesis testing with multivariate analysis;

• Multivariate regression analysis;

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• Several applications, of interest, to be described

later in Section 4.

They are aimed competences on statistical methods

related to multivariate data, and to develop

perspective about computational results due to

several difficulties foreseen from the graduation

background of the students: mainly, the students are

graduated on Management Science specialties, but

they are also students from Engineering and Social

Sciences fields (Figure 2).

Assessoring - 5

ccounting &

Auditing - 3

Communication &

Marketing - 4

Engineering - 1

Management - 11

Social Services - 1

Teaching & Training

- 1

Figure 2: Type of graduates on the MSc course.

The second module is dedicated to decision support,

and it is based on optimization (Mathematical

Programming) tools from probabilistic nature. Then,

recurring to parametric and stochastic methods, the

procedure to build decision rules is addressed. They

are focused the following subjects:

• Decision Theory;

• Game Theory;

• Stochastic Linear Programming;

• Network Optimization;

• Several applications, complementary to the first

module, to be described in Section 4.

It is aimed to understand, to develop and to use

quantitative methods in a way to optimize decision

rules. As in the first module that addresses

multivariate analysis, the oral presentation, the

treatment of problems embedded on real cases, the

resolution of basic exercises and the computational

application of the notions are the main pedagogic

subjects. The methodologies are presented in next

Section.

3 LEARNING METHODOLOGIES

In this section, the learning methodologies are

focused, and it is also described how they are

conjugated with the computer-aided approaches,

namely, onto the specific problems that are built and

the learning assessment.

The computer support is necessary, due to the

management environment that is intended to

simulate, where statistics and optimization methods

are day-to-day tools. Also, it promotes the learning

success on the referred fields, which are commonly

recognized as very difficult. We must remember the

weak or delicate conditioning in quantitative basics

of the incoming students, originated even from

Social Sciences specialties.

Consequently, we had to search new approaches

in the sense to promote the learning success of

students with quite different profiles. We thus

conjugate various approaches and methodologies,

and we assume a blended learning and encapsulating

framework. It includes a simulation of enterprise

context, assuming characteristics of an immersive

learning or virtual learning environment (VLE)

approaches, and a selection of cases, based in real

situations arising in business context, including a

PBL approach.

Heinze and Procter (2004) developed a definition

for blended learning that states the facilitation of

learning through:

• The combination of different modes of delivery –

we distributed ours efforts from oral

presentation, theoretic support, reading of

selected texts and cases and B-On search,

computational applications and assessment;

• The combination of different models of teaching

– assuming a virtual environment, like the

student is working on a enterprise where he faced

some problems (specifically built to combine

multivariate analysis and decision support

subjects);

• The combination of different styles of learning –

the personnel way of learn of each one of the

students is allowed, as is usual on the PBL

approach;

• The effective communication between all the

elements involved – the team work is stimulated,

and faculty simultaneously assumes instructive,

tutoring or mentoring roles.

We intend to conjugate the human intervention with

the electronic learning component, from the themes

introduction and theoretic support (by the faculty),

to the computer-aided jobs (from the student). It

must be noticed that we target difficult fields like

multivariate analysis and optimization, which aim at

decision rules on an enterprise context.

We thus develop an immersive learning

approach, which allows us to apply the same kind of

USING A COMPUTER-AIDED PBL APPROACH IN THE DESIGN OF A COURSE IN ENTREPRENEURSHIP AND

MANAGEMENT

173

quantitative tools that are used in enterprise or

business contexts. This contexts that we simulated

promotes the student experiencing, and it draws near

the challenge and the competition occurring in real

businesses situations.

Usually, immersive learning is used to promote

experiencing of critical incidents to security and

rescue staff (riots, plane crashes, terrorist attacks,

etc…), through virtual simulation of the complexity

of such real situations, and the staff personnel must

be able to face the stressful context. The main

purposes of our approach are:

• To provide a context, simultaneously innovative

and defiant, in which students can achieve the

professional competencies, as entrepreneurs or

for the careers focused (project coordinator,

technical director, and controller) in enterprise or

business environment.

• To facilitate learning, by spreading the

application field of action of lectures to other

information deliveries, we can improve student

self-learning through problem solving

approaches.

We intend to recreate enterprise or business

challenging situations, through simulations of the

trial-and-error learning cycle: decision, followed by

feedback, new and improved decision. When the

student follows this iterative procedure, he gains

experience on make decisions, he has knowledge of

his difficulties, and he learns with mistakes of his

own.

This immersive approach resorts to critical

understanding of the problematic situations, instead

of the usual passive role of the student: he reads or

hears about some subject, he has to mentally

assimilate it, and to repeat the pre-defined answer in

final. The immersive approach avoids some dislike

or nuisance of the long lectures scheduled, foreseen

in blocks of four hours, which can reach eight hours

in continuum if the blocks are allocated side by side.

In a similar way, we use various information

means (introductory presentation, web-based or

electronic tutorials, texts reading, computer business

applications, computer assessment and self-

assessment), and we tried to recreate realistic

problems. Thus, we simulate problematic situations

that arise frequently in businesses world, namely, we

present in this paper the contexts of logistics and

supply chains operations, and in investments and

financial markets: we thus assume a PBL approach.

4 THE PBL APPROACH

In this section, an approach based on realistic

problems is described and two illustrative examples

are focused: logistics and supply chain operations;

and investment planning considering financial

markets. The approach relies on realistic cases

specifically built to address the subjects of decision

support, optimization tools and multivariate analysis.

In the PBL approach, we use the referred

problem situations, originated from real and

professional life, and this also allows the simulation

of the real procedures used to solve them. To solve

these specific problems it is required competences

on basics knowledge, in learning strategies, and

team work skills. The overall purposes of our

approach intend to develop and deepen the

graduation knowledge, to promote innovation and

research activities, to integrate multivariate analysis

within decision support issues, and to promote

autonomous learning on the entrepreneurship and

enterprise contexts.

In a way to hold the interest of the students and

avoid nuisance of long and intensive lectures in

mathematics fields, we included some challenging

elements from real life, from professional cases, to

improve the efficiency of communication.

The student had active role, and his participation

relies on a trial-and-error learning cycle to integrate

knowledge on multivariate analysis and decision

support themes. To achieve this, a combination of

information deliveries is used and we also built

specific problems to create defying situations, even

to stress it up to induce bad decisions. The feedback

on decisions is promptly known, through pre-

established rewards or consequences, despite the fact

the student can gather information from external

sources. We used a combination of technology-

based materials, computer-aided sessions, and oral

communication to present each one of the realistic

case problems.

4.1 Logistics and Supply Chain

The logistics and supply chain case was objectively

written, it included an overview of the situation on

the petrochemical (Julka et al., 2002; Lasschuit and

Thijssen, 2004; Neiro and Pinto, 2004) and

pharmaceutics (Shah, 2004; Kallrath, 2002) multisite

networks, their specific context, and the major

decisions to be made.

There is and introductory presentation of the

open-ended problem, of the possible scenarios, the

critical data of the situation, and the related

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knowledge is refreshed. The context considers a

petrochemical cluster (Julka et al., 2002) and its

overall strategic management, to make decisions

• To select projects from a group of proposals;

• To increase the net value generation of the

cluster;

• And to appreciate the support organizations and

shared services of the cluster.

The focus is on the basic concepts behind Stochastic

Linear Programming (SLP): SLP limitations,

probabilistic data, and the trade-offs achieved from

the optimal activity analysis. The case problem aims

to the comprehension and insight of a SLP

multiperiod problem where the formulation of the

objective function and restrictions are detailed.

It is intended to develop students reasoning, to

frame the problem situation, and the features of

uncertainty and complexity that characterize the

problem-solving approach are incorporated. The

main steps for the student are the development of a

specific problem statement, to list the quantitative or

qualitative information needed, to formulate and test

alternative hypotheses, to present and support the

decisions(s) proposed. Thus, competences to solve

problems are conjugated with the knowledge of data

treatment, forecasting, time series (Tavares et al.,

1996), and possible extensions are balanced.

4.2 Investment and Financial Problems

Following the former supply chain case, instances of

a case of high level of complexity are presented, and

reformulations considering financial and investment

issues are treated.

It is introduced a robustness framework for

planning, with a probabilistic measure of

performance (Suh and Lee, 2001), where the worst

scenario addresses the reference solution and a

Pareto analysis is proposed. Also, it was focused the

decision-making procedure to select the best robust

scenario, which uses the Pareto curve and considers

the trade-off between the expected performance and

the robustness measure.

The main objectives are to develop and deepen the

graduation knowledge, in an innovation and research

environment, where computational applications are

compulsory. The usual paradigm of problem solving

supposes that we know the resolution route, but this

route implies a complex relationship between

reasoning and feeling, involves bad partial decisions,

and some negative results. Consequently, our PBL

approach considers the trial-and-error learning cycle.

The case problem is open-ended, and various

extensions are suggested, like the following ones.

• The treatment of risk on investment planning

(Rodera et al., 2002), through a multiobjective

programming that requires an iterative procedure

(Augmented Tchebycheff Algorithm). This

procedure allows the visualization of the efficient

solutions in the multidimensional space and

facilitates the assessment of the economical risk

of the project.

• The financial risk assessment and management,

through various probabilistic estimators (Barbaro

and Bagajewicz 2004a). Among others, they are

specifically considered the expected profit, the

financial risk and the downside risk. The

maximization of the first one considers the at the

same time the minimization of the others at every

profit level.

• The use of inventory and financial tools such

derivatives (Barbaro and Bagajewicz 2004b),

options and futures, which are balanced in a way

to manipulate risk curves. These notions are

intuitive, but to effectively reduce risk it is

necessary to consider appropriate models for risk

management. Otherwise, the use of inventory or

financial tools can lead to higher risk exposures.

Alternatively, a simplified approach (Guerreiro et

al., 1986) is suitable for students originated from

Social Sciences field.

5 CONCLUSIONS

A computer-aided approach is applied to conjugate

multivariate analysis and decision support, in a

course of a Management Science MSc. program that

aims entrepreneurship and SME management.

The schedule of the program is designed to meet the

needs of student workers. Thus, mathematical

lectures are provided in intensive four-hour blocks.

However, students would prefer shorter blocks of

time, to avoid some nuisance or dislike.

Thus, realistic cases are built, specifically aimed

at Management Science students, and it is featured

some decision support principles, based on

optimization of probabilistic measures. The

multivariate analysis tools are compulsory, such as it

is the computer-aided approach underlying the

learning process. The case problems are focusing,

among others, investments programming, financial

risk treatment, or supply chain planning and

distribution activities.

USING A COMPUTER-AIDED PBL APPROACH IN THE DESIGN OF A COURSE IN ENTREPRENEURSHIP AND

MANAGEMENT

175

A Problem Based Learning (PBL) approach is

assumed. This methodology based on problems is

embedded on enterprise and businesses contexts, and

enterprise computer-aided activities are simulated.

Consequently, this immersive approach is

encapsulated in a blended learning framework.

Finally, the faculty’s role is critical as he acts as

learning facilitator, he introduces cases and partial

questions, he supports computational tasks, and he

supervises team work.

ACKNOWLEDGEMENTS

The author thanks the support from Technology and

Management College at the Portalegre Polytechnics

Institute (ESTG/IPP) and from the Centre for

Chemical Processes (CPQ/IST).

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