A Project Based Learning approach for Production Planning and

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 1/16

1. Introduction

Engineers traditionally require solid knowledge in science and technology. However, with the advent of the

21st century comes the necessity of promoting skills that are not easily developed within traditional methods

of engineering education. Challenges for new engineers involve the development of a systemic viewpoint, and

the capacity to interface between the different branches of engineering, as well as the ability to propose and

solve complex problems, always considering the constant need to accompany the current trends in the field

(Bezerra et al., 2010).

Studies related to teaching methodologies have demonstrated many universities’ concerns with regards to

traditional methods of education, especially in the engineering field. Traditional teaching methods, also known

as deductive approaches, in which teachers motivate their students towards focusing their attention and efforts

on exams and tests, have resulted in students’ increasing lack of motivation and interest (Tobin et al., 1990;

Prince & Felder, 2006). The focus on specific knowledge, which is relevant primarily for tests and exams have

resulted in students focusing their efforts exclusively in obtaining passing grades in these evaluations. It results

in individuals that neither fully understand what is being studied, nor the manner in which the knowledge must

be actually applied, leading to disappointing levels of learning retention.

The development of novel education practices in the field of Engineering has resulted in universities creating

new teaching methods. These institutions are increasingly considering the merits of the practical application

A Project Based Learning approach for Production

Planning and Control: analysis of 45 projects

developed by students

Sanderson César Macêdo Barbalho

*, Ana Carla Bittencourt Reis

, Julia Alexssandra Bitencourt

Maria Clara Leopoldino de Arêa Leão

, Gladston Luiz da Silva

Universidade de Brasília, Brasília, DF, Brazil

*sandersoncesar@unb.br

Abstract

The project-based learning (PBL) approach has been a part of the University of Brasilia’s programme from the inception

of its Industrial Engineering syllabus. Production Systems Project 4 course is one among eight courses that drive the

utilization of PBL approach at the curriculum, the subject of this study, in which we present a set of analyses of the

projects developed between 2013 and 2016. The projects involved real-world problems, related to public and private sector

enterprises in Brazil’s Federal District. The conducted analyses aimed to identify the organizations’ profiles and the PPC

(Production Planning and Control) techniques which were used to achieve project objectives. Therefore, statistical analyses

were performed, such as Correlation Analysis, Cluster Analysis, as well as qualitative documental analysis. The results of

this study indicate the profile of external partners that have the highest probability of achieving satisfactory results, as

well as the main planning elements which impact the final grades of the projects.

Keywords

Active learning. Case study. Production engineering. Production, planning and control.

How to cite this article: Barbalho, S. C. M., Reis, A. C. B., Bitencourt, J. A., Leão, M. C. L. A., & Silva, G. L. (2017).

A Project Based Learning approach for Production Planning and Control: analysis of 45 projects developed by students.

Production, 27

(spe), e20162259. http://dx.doi.org/10.1590/0103-6513.225916

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 2/16

of knowledge, internalized along the course, and before the program’s conclusion. It is seen as essential that

engineers develop not only purely technical proficiencies, but also interdisciplinary skills such as cooperation and

project management (Taajamaa et al., 2013). In this sense, problem-based learning and project-based learning,

both commonly called as PBL, have been widely applied as a teaching and learning strategy (Bassily et al.,

2007; Kadlowec et al., 2007; Ras et al., 2007; Gillette et al., 2014; Lin et al., 2014; Jeon et al., 2014; De los

Ríos-Carmenado et al., 2015).

In order to improve students’ knowledge retention, and better relate theory to practice by adopting a

methodology that is focused on the students themselves, beginning in 2009 the University of Brasilia (UnB) has

implemented an innovative undergraduate program in Industrial Engineering (Prince & Felder, 2006; Lima et al.,

2012; Zindel et al., 2012). Based on the PBL approach, the course aims to foster graduate students’ abilities

to handle real world problems through the development of specific projects that encourage a systemic and

professional interaction with several different environments.

According to Zindel et al. (2012), the proposed approach promotes students’ learning by means of assisting

an external third party with their internal improvement projects. External partners, as pubic or private companies,

will typically propose problems that are handled by course students in a group setting. These real-world case

studies are developed through a project-based approach, where planning and execution are graded separately,

in order to verify the undergraduates’ capacity to apply technical knowledge on these authentic situations.

In this context, this paper’s goal is to present the results of the PBL approach in one specific course, termed

Production Systems Project 4 (PSP4). This course is taught in the same semester as the program’s Production

Planning and Control course.

In the next section, theoretical concerns are presented. Secondly, the research methodology is described.

Section four presents the case study, concluding with a discussion of the results and, finally, several remarks

related to the main elements of the study are highlighted.

2. Problem-based learning

Teaching approaches based on PBL have been widely employed as a way to develop students’ skills, through

the practical application of academic concepts learned from real-world situations, throughout a University’s

program (Soares et al., 2013).

The use of active learning approaches has been the subject of many studies that seek to demonstrate it value

in improving students’ outcomes. Aalborg University has presented an important example of the implementation

of educational models, and has been the object of relevant attention. In 1974, Aalborg University was founded

using a combination of a problem-based and a project-organized approach (Kolmos et al., 2004). There is evidence

that this university is ranked as one the top institutions in developing engineering education in Northern Europe,

in accordance to both the needs of the labour market and its extensive PBL environment (Zhoua et al., 2014).

According to Balve & Albert (2015), the PBL approach increases individual motivation, and introduces

students to actual demands that need to be managed. In this approach, the student transitions from a passive

role, in which he or she receives knowledge, to a more active stance. This serves to anticipate future employment

experience, by associating classroom theory to authentic professional practices (Ríos et al., 2010).

In the PBL approach, students develop their knowledge through active learning, interactions with the external

environment, and independent or collaborative teamwork, with the accompanying guidance of the course’s faculty

(Taajamaa et al., 2013). According to these authors, in this approach students are not merely passive recipients

of knowledge. They are immersed in an experience that is similar to what they will face in their professional life.

When the project-based education includes the whole curriculum, it is used to be called of Project-led

education (PLE) (Lima et al., 2007; Crosthwaite et al., 2006). An important use case of this approach is the program

of Industrial Design Engineering at the University of Twente. In this program, the students are experienced in

working as a group, with four projects per year, in the first and second year of course (Damgrave & Lutters, 2016).

The PBL approach begins with the accomplishment of one or more tasks that lead to the development of

a project scope, which results in a report that summarizes the procedure that was applied (Prince & Felder,

2006). According to Thomas (2000), PBL projects can be based on one or more thematic units. Through this

manner of knowledge construction, it is observed that learning could foster students’ motivation and provide

them with a greater sense of satisfaction (Frank et al., 2003).

Literature suggests that the qualities related to the competencies learned from PBL are (Bassily et al., 2007;

Kadlowec et al., 2007; Ras et al., 2007; Gillette et al., 2014; Lin et al., 2014; De los Ríos-Carmenado et al.,

2015; Miranda, 2004):

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 3/16

• The ability to obtain knowledge that can be used in a professional environment;

• The ability to develop skills used in extending and improving such knowledge;

• The ability to expand problem solving skills.

The use of multiple subjects in an engineering education provides different perspectives regarding several

issues, with the study of topics aimed at developing communication skills and teamwork, as well as the ability

to solve real-world problems (Aquere et al., 2012). Therefore, PBL is considered as being an approach that can

assist in the development of skills and competencies that are now understood as being essential to an engineer’s

background (Taajamaa et al., 2013).

There are various studies on the use of the PBL approach. The results, with regards to improvements in

learning, have been seen as positive for the universities themselves, and have resulted in increasing retention

rates for students in various programs (Ragusa & Lee, 2012). Likewise, the experience acquired by students

in the application of technical knowledge in real-world problems have resulted in other benefits, such as

improvements in their project planning skills and abilities related to leadership, communication and teamwork

(Dym et al., 2005; Coyle et al., 2005).

In a problem or project-based learning methodology, the development of competencies requires the application

of technical knowledge in specific contexts linked to the professional practice. This learning methodology,

linked to the development of technical and transversal competencies, requires the application of skills that can

be characterized as “How to do”, that is, it is necessary to apply knowledge in practical contexts (Soares et al.,

2013). According to Project Management Institute (2013), the main “soft competencies” for success in carrying

out projects are: leadership and influencing, team building, motivation, communication, decision making,

political and cultural awareness, negotiation, trust building, conflict management, and coaching. All of them

are subtly interrelated, and even difficult to manage separately.

It is important to note that much of current research explores students’ own perspectives regarding

the improvements seen by learning through solving real-world problems using the PBL approach (De los

Ríos-Carmenado et al., 2015; Ras et al., 2007). In this particular research, in spite of considering the development

of transversal competencies by students, greater emphasis is being given on studying the impact of the projects’

technical characteristics on their outcomes, as well as analyzing the aspects of the project planning process

over the results achieved by students.

3. Methodology

This research utilizes a case study approach (Yin, 2010) in which a deeper analysis is performed with regards

to an object of investigation, which in this case is a PSP4 course structured according to the PBL approach.

It was based on qualitative and quantitative data, the latter obtained by means of analyzing grading sheets, and

the former by registering the reflections of the course’s faculty, who are also the authors of this study. These

reflections were used for understanding the results of analysis in form to triangulate data.

Documental analysis (Dane, 1990) was also performed, by first evaluating all final reports and presentations,

in order to identify the specific technical knowledge which was applied, and also to characterize both the assisted

company and the particular problem which was addressed.

The grading sheets stored data from the first semester of 2013 until the first semester of 2016, totaling 45

projects, with each semester generating a specific sheet. These were analyzed and formatted, being consolidated

into a single document containing all projects and their respective grade summaries. As more accurately described

later, the students’ overall project grades were composed from a weighed average of the grades from their

Preliminary, Intermediate and Final reports.

The qualitative data from this documental analysis was inserted into a different dataset, in which a binary

classification was considered for each technique used by the project teams. Namely, “1” when the technique

was used, or “0” when it was not. This classification was performed by means of a deeper analysis of students’

reports. If the report did not mention the technicque, or if it lacked demonstrable results, it was not considered

as compliant, and was given the classification “0”. A statistical analysis was then conducted regarding the

techniques of the anchor course that served as a basis for the developed projects.

Eventually, a new dataset was created based on data from the two previous sets. This dataset was used for

the application of deeper statistical analysis using the SAS® software, such as Correlation Analysis and Cluster

Analysis. The objective of this analysis was to characterize the companies used to develop the projects, by

providing information about their context. Other objectives included the discovery of the correlations between

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 4/16

the planning steps and grades, as well as the indentifcation of the techniques that were useful in solving the

problems presented, also in order to relate them to the final grades.

The last step was to register the data according to the principles related to operations planning and control,

project management and active learning. Qualitative data, gained from the experience of the course’s faculty

were used in order to reflect discovered significances and correlations.

4. Case study

In the University of Brasilia’s Industrial Engineering program, the PBL approach is drove by a set of

disciplines called production system project (PSP). Each PSP course is related to one or more anchor course

in a specific semester, and projects are carried out in accordance with the theme of their respective anchor

courses, involving the discussion of practical problems from external agents, with a support of specific project

management methodologies used in order to solve them (Figure 1). This approach stimulates students’ learning

by offering them the opportunity to search for solutions and project proposals aimed at addressing the issues

of external agents. No related content is presented to students before each PSP course. Their anchor disciplines

are ministered alongside a co-requisite course, and as deeper concepts and techniques are scheduled for the

last classes, students commonly utilize self-learning as a method for achieving project solutions.

Figure 1. General structure of the University of Brasilia’s Industrial Engineering program.

The PSP4 course is related to Production Planning and Control (PPC) discipline, as a co-requisite. The PPC

course in the University of Brasilia’s Industrial Engineering program is a unique discipline, where students

review production system design methods according to Slack et al. (2009) as well as theories related to demand

forecasting, capacity planning, inventory management, scheduling and shop floor control (Sipper & Bulfin, 1997;

Vollmann et al., 2004). Although students in the PPC course are tasked with reviewing real-world companies,

the course’s main focus is theoretical. The professor assigned to PSP4, who commonly also ministers the PPC

course, advises students in the implementation of effective actions aimed at solving a real-world PPC problem,

as presented by external partners.

The PSP4 course is structured around the assessment, by faculty, of the work accomplished by students, as

presented in Figure 2.

The PSP4 course grading structure consists of three grades, in which students are assessed based on the quality

of the work presented. The first grade is related to the Preliminary Project (PP) and consists of the evaluation of

the Project Management Plan (PMP). This plan is evaluated according to the PMBOK Guide’s five areas: Scope,

Time, Communications, Stakeholders and Risks. Students are instructed to develop a project plan using the

sequence established by the PMBOK Guide©, in which the Work Breakdown Structure (WBS) consolidates the

scope, serving as a basic input for the preparation of the schedule and other technical elements of the project

plan (Project Management Institute, 2013).

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 5/16

As the Production Systems Project course is presented as a co-requisite to PPC, meaning that students do

not yet comprehend all the contents that can be applied to a specified problem, it is emphasized that the main

output of this step is a well-defined scope. In each presentation, professors are able to correct specific mistakes

which students can commit when planning their tasks without having had any previous classes in pre-requisite

subjects.

Other PMBOK areas are not reviewed, in order to avoid an over emphasis on planning and a lack of focus

in solving the external partner’s problem.

The second grade is assigned to the students based on the Intermediate Project (PI). While the Preliminary

Project focuses on the project planning stage, involving the definition of its scope, schedule, and risk analysis,

the objective of the PI is primarily to observe the technical development of the tasks, in relation to the initially

proposed scope. Therefore, if the project has a purpose of, for instance, analyzing the alignment of the

company’s share price with its’ external demands, the Intermediate Project must present the use of inventory

analysis techniques, such as a Pareto analysis, the identification of inventory costs per unit, demand analysis,

and identifying trends and randomness, depending on the previously defined scope. The students’ presentations

form the basis of the Intermediate Project’s grade, with the subsequent evaluation made according to students’

answers to technical questions posed by the professors over the course of the presentation. As already mentioned

in relation to the Preliminary Project phase, in this case professors also give their support in solving problems

arising from misunderstandings by students who have not yet attended the previous PPC classes in PPC course.

The normal interval between the Preliminary and Intermediate Projects is of approximately five weeks.

During this period, the professors oversee each group’s work, advising them over data collection and analysis

techniques. In some cases, teachers work jointly with the teams, in order to solve issues related to data gathering

or communication pitfalls. If necessary, professors will advise students with regards to mandatory changes in

scope, according to projects’ deployments.

The Final Project (PF) consolidates the assignment based on the elements developed in the Preliminary and

Intermediate phases. As for the PP, the project plan is also analyzed. In this case, students must indicate the

differences between the initial plan and the outcomes. As such, the last version of the project’s scope is compared

to the initial version. The same follows for other elements, such as the project’s schedule, communications and

risk planning, and stakeholder definitions. It is required that students present a comparison of the changes

made between the preliminary delivery’s baseline and the project’s final configuration. However, the main result

of the final project is the content of the technical assignment executed by the project teams. In this sense, the

concepts of Production Planning and Control are thoroughly analyzed and compared with the results achieved

by the project teams. Concurrently with the final Project, the PPC class is, at this point, concluding its activities.

All of the students’ coursework has already been reviewed in class, (and the final PCP test might already have

been administered), which facilitates the technical discussion of the results. In addition to the revised project

plan, students compose an academic article, detailing the technical tasks performed for the external partner.

Figure 2. PSP4’s basic grade structure.

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To conclude, the Final Project presentation is given in the presence of the external partner, which reflects

on the overall results, in the presence of both students and the course’s professors, with the resulting feedback

being incorporated into the final evaluation.

The schedule of the PSP4 course’s final project is sometimes postponed, in order to ensure the quality of

the work submitted. There is a usual interval of four to five weeks of activity between the Intermediary and the

Final Project, during which the faculties monitor the students’ tasks, and eventually work directly with the teams

and external agents towards facilitating data collection, analysis, technical applications, and in formatting the

final delivery for the external partners.

4.1. A review of concluded PSP4 projects

the psp4 course has been ongoing from the first graduate class of the university of brasilia’s (unb) industrial

engineering program, starting in the second semester of 2012. data from the first semester’s class was not

considered, as the course’s structure was at that stage not consolidated in the manner shown in the previous

section. considering the period from 2013’s first semester to 2016’s first semester, seven psp4 courses were

undertaken, totaling upwards of 204 students who have accomplished the activities previously detailed. overall,

45 projects were analyzed. Figure 3 presents the profile of the organizations that participated as external

partners during this period.

Figure 3. Classification of external partners of the PSP4 course: (a) size and profile; (b) economic sector.

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 7/16

Figure 3 presents the characteristics of the organizations that participated in PSP4 course projects. The entities

are classified as public or private, and according to their size. It is observed that most assisted organizations

are private, corresponding to 30 projects, while public organizations are linked to 15 projects. The data also

demonstrates that most participating public organizations are classified as “large”, containing more than

500 employees, while private organizations were mostly composed of very small or small enterprises, with up

to 10 and 100 employees, respectively.

The analysis of the organizations’ economic profiles was conducted according to a classification of goods

and services provided. However, as some subcategories were most common, it was considered more appropriate

to present the data separately. Thus, Figure 3b demonstrates the preponderance of projects related to the service

sector, rather than the industrial field. The most representative service sectors are those related to healthcare - in

this case, consisting of public hospitals in the Federal District -, and transportation. The Figure also highlights

the storage logistics and restaurant subsectors, which are both classified as services, although they do offer

aggregate value in which the delivered product is considered as a key component of customer service.

Other service sectors contemplated in the finished projects are those related to tutoring, document

management, recycling, car dealerships, medium-sized supermarkets, gas stations, and food transportation.

The industry classification is also comprised of various subsectors, such as metal-mechanical, pharmaceutical,

food preparation, factories, footwear, medical and dental products, and beverages.

As described in the methodology, the projects were analyzed in order to characterize them in accordance

to the utilized production planning and control techniques, as well as the main contributions of the project

to the organizations, as required by their owners. The graph in Figure 4 presents the techniques used in the

evaluated projects.

Figure 4. Use of PPC techniques in assignments.

The main techniques used in the assignments are demand forecasting and ABC curves. The projects usually

focus on the analysis of the companies’ production data, which justifies the application of these techniques.

The basic data is related to production demand, and the main analysis has, as its objective, the identification

of the company’s main products, in terms of volume, revenue or costs. Process modelling techniques are also

heavily emphasized, with a predominant use of qualitative methods for analyzing production processes (such

as mapping techniques), although several projects have been done based on data analysis. Quantitative data –

such as measuring the time elapsed in each manufacturing process - were collected throughout the production

process, especially in cases in which capacity evaluation and planning was the teams’ focus.

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4.2. Grades attributed to projects

The consensus among those who experience active learning holds that the grade is not as important as the

process of working in teams, and the opportunity to interface directly with real clients (De los Ríos-Carmenado et al.,

2015; Ras et al., 2007). In essence, a grade is a summary of the evaluation that professors attribute to projects,

according to their results. As such, initial analyses were conducted regarding the projects’ grades. The first of

these serves to relate the given grades to some of the characteristics of the external partners themselves.

Despite of the fact that PSP4 projects are performed in teams, the grades are individual for each student.

Two faculty members share the process of assigning grades in order to reduce subjectivity. The overall results

have been positive for student development. From a group of 204 incoming students, only 11 failed the courses,

representing 5.4% of the total.

However, individual grades have mostly been used as stimuli for students’ efforts. The actual effective result

is tied to the outcomes from each team. Consequently, in order to analyze data from PSP4 courses, only the

overall team grades were considered, not withstanding the merits of peer evaluations and individual grades.

Figure 5 presents the general grades (GG) assigned to projects developed during the courses.

Figure 5. General project grades.

Of note in Figure 5 is the fact that the majority of assigned grades lie between 8.0 and 10.0, falling short

of the excellence level, represented by grades 9.0 to 10.0. Additionally, there were no grades below 6.0, which

denotes the high standard of PPC applications performed by student groups. Table 1 presents the distribution

of the highest scoring projects, according to the characteristics of the organizations presented in this topic.

In accordance with their size, better results were observed in very small or small organizations. Only two out

of ten cases with the highest grades were related to large ou medium-sized businesses. This result may be related

to the hindrances that large companies possess in transferring the considerable amount of data that must be

available for students participating in projects similar to those in PSP. Smaller companies, lacking such levels of

bureaucracy, can be more agile in providing data for academic projects. This same factor may be used to explain

the fact that eight out of the ten most successful cases occurred in private organizations. Public organizations

must usually offer their services for a large number of users, which results in their larger structures. Specifically,

bureaucratization is a common characteristic of public organizations in Brazil.

As can be seen, Table 1 also presents the related economic sectors of the external partners in the most

successful projects. Four were performed in the industrial sector, only one in the general services sector; three were

performed with restaurants, and two in the healthcare field. A deeper analysis of projects related to restaurants

and services reveals aspects involving materials planning and control. In this distribution, all cases of projects

involving restaurants focused on inventory management, and at the single service company, a capacity planning

study was performed, related to service operations at an automotive center.

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In fact, only two of the most successful cases were truly related to service activities, both in hospital settings.

This could mean that industrial activities and service operations involving material flows are, effectively, easier

to approach with regards to the application of production planning and control techniques. There could also

be a relation to the relative ease, in these cases, of visualizing the production process as a value-added chain,

and even in identifying process standards and gathering data in a more objective manner. On the other hand,

in service operations, it is often seen that the organization’s decision makers themselves do not realize they are

producing a service, or in what manner they are able to control the information resulting from their services.

In a hospital setting, however, human lives are often at stake, and technicians and professionals that are more

skilled oversee all tasks. They are commonly specialists in their respective fields, thus probably exhibiting less

difficulty in visualizing their process workflow. These explanations can serve to somewhat clarify the resulting

data, but would be better served by an evaluation in future works.

Looking qualitatively at the projects graded 9.0-10.0, as stated on Table 1, a profile of highly focused

students becomes evident. These cases show groups in which frequent student engagement was observed. These

students typically put forth greater effort in anticipating content not yet presented in the classes of anchor

disciplines. This observation will be discussed further on, although a more in-depth analysis of the profile of

students is beyond the scope of this article.

The next section presents a statistical analysis of the projects’ results, in terms of both their grades and in

relation to PPC techniques and project management practices.

4.3. Statistical analysis

In order to evaluate the relationship between the variables mentioned in the previous topics, an initial

bivariate analysis was performed, followed by a multivariate analysis aimed at identifying homogeneous groups

and understanding their characteristics.

In essence, the data was collected from the grades resulting from the PSP4 projects’ final scores, as already

presented. The grades were initially analyzed in relation to each other, in order to verify which elements were

most influential in determining the final grade.

The relation between the grades of the Preliminary, Intermediate and Final phases is purely mathematical,

because of the weighted relationship between the grades earned during the semester and the final evaluation

grade.

However, the grades that make up both, the preliminary project (which is comprised of the scope, schedule

and other planning aspects) and the final scores, are not initially clear. As their influence is the result of several

calculation steps, which do not facilitate the perception of correlation, especially when in relation to projects

with a variety of themes, organizations and techniques. In fact, there was also a need to analyze the correlation

strength between the Primary, Intermediate and Final grades.

Ultimately, the data in Table 2 represents the correlation between the grades. The Table’s data corresponds

to the following: N1 represents the Project Planning Grade, composed of the N2 to N6 grades, which correspond

respectively to the scope, time, communication, stakeholders and risk planning components. N7 represents the

Intermediary Project grade, N8 the Final grade, with NG representing the General Grade of the project.

Analyzing the impact of the project grades at the end of the semester and the final score, it is evident that

all the correlations are strong, with the highest correlation between all factors (r = 0.85121) occurring between

the Intermediary Project (N7) and the General Grade (NG).

Table 1. Grades From 9.0 to 10.0, according to external partners’ characteristics.

Size Public or private Sector Grades

Large Public Healthcare 9.0-10.0

Small Private Industry 9.0-10.0

Small Private Services 9.0-10.0

Small Private Industry 9.0-10.0

V.Small Private Industry 9.0-10.0

Median Public Healthcare 9.0-10.0

V.Small Private Restaurant 9.0-10.0

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It could be hypothesized that, considering the grading chronology, the nearest grade (N8) would have the

highest correlation no NG, due to students’ attempts to achieve a positive result, as the “student syndrome” is

reported in project management literatures (Goldratt & Cox, 2014). Although there is in fact a strong correlation

between N8 and NG (r = 0.82793), this correlation is lower than the one between N7 and NG, which illustrates

that the Intermediary Project phase is the most representative in terms of the projects’ success rate. Additionally,

this could be related to the fact that the project team does not possess, at the Intermediate stage, full knowledge

of the techniques related to the anchor course. Therefore, the performance at this stage often depends on the

student’s initiative in seeking to study the content of the lessons before they are presented in classes, as the

project’s timing is different from the anchor course’s schedule. This occurs due to the fact that the strategy

adopted by the Industrial Engineering major creates a co-requisite relationship between the PSPs and anchor

courses, as opposed to a pre-requisite, system.

In fact, professors have observed that, every semester, the highest-rated projects are those in which the

students actively anticipate the classes’ content. The techniques presented in Figure 4 are mainly characteristics

from later PPC coursework, which students are encouraged to learn in order to facilitate their conversations

with external partners about their given problems. Professors are easily able to perceive when students learn

class contents beforehand, and use their class time as a problem-solving opportunity, in which they can clarify

their doubts and facilitate their decision-making process, as discussed earlier.

In any case, the most promising results are seen in projects where the team did not postpone any tasks until

the final project phase, maintaining instead a constant level of performance, especially during the Intermediate

stage.

As to the correlation matrix presented in Table 2, considering non-zero p-value criteria, some concluding

remarks are shown. For a < 0.1 p-value, only the relationship between time management (N3) and communication

management (N4), and between the latter and the final grade (N8) could be considered uncorrelated; while for

a < 0.05 p-value, it is possible to disregard the correlations seen between scope (N2) and communications (N4),

and between the latter and risks (N6).

Overall, it can be said that among the project planning areas, there is no significant correlation between

the planning of the project’s communications and the other areas of knowledge analyzed in the course, aside

from stakeholder management (N5). Since stakeholder management is basically done by communicating with

such actors (Project Management Institute, 2013), there is a consistency in affirming that such information

correlates, although it was expected that all knowledge areas analyzed would maintain strong relationships

among each other, in terms of results.

It is clear from the data that, although there is a correlation between all knowledge areas covered and the

grades attributed to the project management plan (PMP), the correlation between the communication plan (N4)

and the PMP (N1) is lower than between N1 and the other variables. The communication plan is still the only

area where there is no correlation with the final project grade (N8), and presents the least significant p-value

in relation to Intermediary and Final project grades.

Analyzing the grades attributed to groups, one can say that in all 45 PSP4 projects, the management of the

project’s communications was not as influential as the other areas addressed in the planning phase. This does not

mean that communications planning is not important, but that the other areas analyzed have a greater impact.

There is a particular interest in evaluating the correlations between the grades attributed to the project

planning elements and the overall project grade, as it is considered that a satisfactory initial planning phase

results in a successful project. This is, in fact, corroborated by the results, since there is indeed a strong correlation

between N1 and NG. However, which planning area has a greater impact on an optimal result?

Table 2. Correlations between the

grades and the total grade.

N1 N2 N3 N4 N5 N6 N7 N8 NG

100.000 0.85541**** 0.77438**** 0.49983**** 0.67207**** 0.84629**** 0.64630**** 0.44299*** 0.73546****

100.000 0.72881**** 0.28369* 0.57552**** 0.67531**** 0.53959**** 0.34383* 0.65107****

100.000 0.16283 0.38741** 0.69495**** 0.48166**** 0.37012* 0.55490****

100.000 0.79848**** 0.29286* 0.31686* 0.23878 0.42612***

100.000 0.46317*** 0.37369* 0.31450* 0.53098****

100.000 0.63907**** 0.38571** 0.63402****

100.000 0.55595**** 0.85121****

100.000 0.82793****

100.000

(

Prob> |r| under H

= 0

); Note: * p < 0.10; ** p < 0.01; *** p < 0.005; **** p < 0.001.

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 11/16

The initial assumption is that the areas related to better results are those linked to the project´s triple

constraints: scope, time and cost. They should be the closest related knowledge areas to projects´ success.

In the kind of project here analysed, the general grade represents project success (Mulcahy, 2013; Jugend et al.,

2014; Verzuh, 2011). On the other hand, recent literature has suggested that risks, stakeholders and the resource

management have a similar impact on the success of projects (Rabechini Junior & Carvalho, 2013; Project

Management Institute, 2013).

Therefore, the data shows that, from the N2 to the N6 variables, the scope (N2;

= 0.65107) has a greater

correlation with NG, which is coherent with the concept of the triple constraint. This result is strongly linked

to the characteristics of PSP4 projects in general, in which the members of a project team are not experts in

the field of knowledge related to a real organization, but are nonetheless expected to define, as precisely as

possible, the assumptions and constraints in the particular case study. This is a typical issue regarding scope

planning, and usually becomes a critical problem that can affect the success of a project. The evident correlations

exemplify the described situation, and are consistent with the situation proposed in the PSP4 course and other

courses structured around active learning.

The second highest correlation between planning elements and the overall grade is seen among the elements

of risk management, time, stakeholders and communications.

These results may suggest a preponderance of the risk aspect over the time management variable, which is

also consistent in the case of the evaluated projects, as the proposed schedule is seen as an external constraint,

because the deadlines are set in the class syllabus itself. Conversely, risks are more dependent on the type of

project, the techniques that will be applied by the team, especially since they have not yet studied the contents

that they will use, as well as the difficulty in communicating with stakeholders. Finally, it is considered that the

most critical steps for successful projects developed in PSP are those related to having a well-delimited scope

and effective risk identification and planning.

In reality, costs are not an important element for these projects, as any delays are typically compensated

with additional overtime efforts – which, in a real company, could result in higher costs, reducing the perceived

success achieved by the project, or even in further limitations in scope. Therefore, time constrains become a less

important factor for achieving success rates, justifying the greater emphasis given to the risk variable.

As already mentioned, quality is not incorporated into the planning phase, due to uncertainties that the

lack of knowledge related to the scope of the projects causes on the initial planning process, and the fact that

quality control is dependent on scope definition (Project Management Institute, 2013). However, it could be

interesting to incorporate such elements of planning, as a way to test a hypothesis of overlapping problems

related to scope with ones related to quality, in order to understand better the problem of restrictions on the

types of projects developed by undergraduate students in active learning courses.

Analyzing the correlation between the planning variables, it is observed that the scope and risk elements

are areas of knowledge with the strongest correlation with relation to the PMP grade. The strongest correlation

related to scope is presented by the time variable; the communications plan is most strongly correlated to the

stakeholders plan, and finally the strongest correlation with risks is seen with the time variable.

Thus, for the evaluated projects structured around an active learning format, satisfactory planning is

dependent on a well-defined scope, through which an appropriate time planning is seen. The project schedule

is the main input for risk analysis, which influences proper planning in a manner similar to the scope definition.

Additionally, the stakeholders and communications elements have a strong relationship with each other, as

stakeholder identification and engagement are seen as more influential for the success of projects than the

communication planning itself.

A cluster analysis of the 45 projects was conducted, as shown in Figure 6. This procedure allowed for the

identification and grouping of similar variables within a classificatory structure, according to the data of the

case analyzed here. As a result, all cases are attributed to a number of homogeneous groups. Therefore, the

resulting groups have the least variance among their starting elements. The analysis was performed considering

the N1 to N8 grades, the NG grade and the technical contents covering X1 to X36.

Through this analysis, it was possible to identify two distinct groups of projects, according to Figure 6, in

which the clusters are presented as Dendrograms, also called tree graphs. The vertical axis represents groups

clustered in decreasing order of similarity. The position of the arrows (

Semi-Partial R-Squared

) denotes how

far these clusters are from each other, with greater distances denoting an increasing degree of dissimilarity.

The first group (cluster 1) is represented by projects 36, 11, 12, 27, 25, 31, 14, 10, 9 and 7. All other projects

are present in the other clusters. Table 3 presents the characteristics of the projects included in cluster 1.

The analysis of data from Table 3 shows that, of the 10 projects evaluated, half are related to public

organizations and the rest to private entities, from which it is concluded that there is no dependence on the

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 12/16

Figure 6. Dendrograms resulting from the Cluster Analysis of grades N

to N

, NG and variables X

to X

Table 3. Characteristics of elements in Cluster 1.

External

Partner Type

Size

Public

or Private

Grades

Total

elements

% of

elements

Service Large Public From 7 to 8 5 13.9%

Industry Large Private From 7 to 8 7 19.4%

Health V.Small Public From 7 to 8 5 13.9%

Transportation Large Public From 6 to 7 4 11.1%

Logistic Stock Large Public From 7 to 8 3 8.3%

Health Large Public From 8 to 9 3 8.3%

Industry Small Private From 9 to 10 6 16.7%

Service Small Private From 9 to 10 6 16.7%

Industry V.Small Private From 9 to 10 5 13.9%

Service Median Private From 8 to 9 5 13.9%

Average 4.9 13.6%

origin of the organization’s capital. The projects represent organizations from all economic sectors, as well as

all classifications in terms of company size. Finally, the projects are also not characterized specifically by their

attributed grades.

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 13/16

From Table 5, it is possible to observe that the techniques X6, X13 and X36, which represent “process

technology identification and planning”, “capacity analysis” and “time analysis”, respectively, differentiate

cluster 1 from the general set of projects, as they are techniques that are more significantly represented by this

cluster than in projects as a whole. These elements make it possible to characterize this cluster as representing

projects where the scope is directly linked to long-term capacity planning (Vollmann et al., 2004), the purpose

of which is to analyze the current capacity and suggest new production structures.

Considering that the analysis of current capacity is dependent on data linked to the manufacturing interval

for each step of the process, a time analysis must be conducted by the students in order to extract it. Most

commonly, these projects incorporate production bottleneck analyses (Goldratt, 2014) and also demand analyses,

in order to identify a starting point for increments in capacity, and also for aligning this increase to demand

forecasts. This system has, in fact, been used in various sectors, as shown in Table 3.

The second cluster differs from all other projects only due to technique X23, which represents “control / inventory

management”. In fact, as previously shown, 35 of 45 projects have seen the application of inventory management

techniques, which constitute the second project cluster. This is indeed quite close to the general profile of the

activities performed by the students during the course, only differing by one single technique. As previously

mentioned, the use of ABC curves is common among projects, as it already characterizes an initial analysis of

inventories.

In an attempt to identify the representative cluster variation, we included all PPC techniques used during

the projects, extracting the percentage of techniques used in relation to the entire set of tools taught during

the course. The result is shown in Table 4.

Table 4. Cluster analysis.

Total Cluster 1 Cluster 2

Sum of the techniques used 4.89 4.90 4.73

% of the techniques 14% 14% 14%

It was further observed that the sum of the techniques and their percentages do not represent a difference

between the clusters and the overall numbers found in the projects. Another analysis was performed in relation

to the number of times that the technique was used in each cluster, in comparison to the overall number

presented in the projects themselves. The mean and deviation values were calculated, in order to identify the

most significant techniques for each cluster. The results are presented in Table 5.

The first analysis shown in Table 5 is related to all projects, as well as the techniques that are most commonly

applied: X4, X5, X9 and X12, representing “demand forecast”, “ABC curve”, “process mapping” and “control

tools”, respectively.

Considering the record of projects developed during the course, there has been a strong emphasis on working

with demand data, which is used for both to construct an ABC curve and to analyze companies’ inventory data.

Consequently, the processes are mapped, either to understand the company’s capacity, or in order to apply

inventory or process controls. Finally, there has also been a focus on building control instruments with widely

used software tools, such as MS EXCEL spreadsheets or HTML modules, in order to ensure that external agents

receive some effective results from the project, seeing as they have shown a great transparency in sharing their

data in order to facilitate the realization of the students’ projects.

It is noted that, in both clusters identified in Table 5, these variables remain significant in terms of utilization,

which is consistent with the cluster analysis.

Table 5. Techniques used for each cluster.

PPC Techniques Mean Deviation

Most used techniques - mean

X4 X5 X6 X9 X12 X13 X23 X36

Total 0.162 0.136 0.622 0.556 0.511 0.444

Cluster 1 0.161 0.136 0.400 0.500 0.300 0.600 0.400 0.400 0.400

Cluster 2 0.172 0.136 0.686 0.571 0.486 0.457 0.314

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 14/16

5. Final considerations

This paper involves an analysis of a PBL course in the Industrial Engineering program of a Brazilian public

university. Initially, the constituent elements of the course and its grading structure were presented. Unlike

most research on this topic, which discusses the experiences of active learning through the use of perception

and satisfaction assessment, in this research we have evaluated the relationships between the course’s content

and the results achieved by students in terms of formal evaluations.

In this sense, this research served to identify that there is a set of project types that is closely related to more

effective results in terms of grades. These are the projects carried out in very small and small enterprises, private

in nature, whose issues are related mostly to material management, even in those companies that are mostly

geared towards services. It was observed that there are two major groups of projects with a focus on planning

techniques and production control: analyses focused on capacity planning and those focusing on inventories.

There is a set of basic techniques used in these projects, regardless of the approach: demand forecasting, ABC

curve, process mapping and control tools.

From a scientific perspective, the study allowed for the exploration of a few theoretical questions, as well

as raising new ones. In terms of project management, it was noted that for the set of undergraduate students

involved in active learning projects, the scope serves as a greater influence for success than the time variable,

this being the main element of the triple constraint in the evaluated situations. In this context, it was found

that proper risk planning is also more effective than a carefully defined schedule.

In the evaluated case, although project planning shows a significant effect on the final results, the delivery

of the Intermediate Project was found to have the greatest impact on the final grade, among the other grades

given throughout the semester. As the intermediate delivery is located chronologically in the middle of the

project cycle, it appears that a planning stage, which is more evenly distributed, or more closely aligned with

the middle of the cycle, would translate into better results than the traditional situation represented by the

“student syndrome”.

Consequently, this correlation profile, and the fact that the anchor discipline does not possess a timetable that

is aligned with the PSP4 course, makes excellence a factor that is based on the students efforts in anticipating

the main contents needed to solve problems presented by external partners. If they do not put in the effort

to learn the content before classes, and in accordance with the partner’s demands, they will probably fail to

both provide results and to be evaluated as excellent by the course faculty. This result demonstrates not only

the effectiveness of learning by doing, but also suggests the best outcomes of this approach when comparing

groups of students with different knowledge acquisition profiles. As the research protocol was not designed for

this kind of conclusion, this suggestion can be used as a hyphotesis for future investigations.

From a PPC point of view, one situation found in almost all the projects was a lack of process and

management structure, and the fact that decision-making was not based on data. In a situation where data is

available to management and there is a systematic manufacturing process, it is likely that the application of

more sophisticated techniques would be most appropriate, even considering projects performed by students in

active learning. A factor which could be approached in future studies.

From a practical point of view, planning PBL-type projects developed by undergraduate students can present

some interesting aspects. Firstly, the type of company that participates as an external agent in this type of

project should be considered. Some have inherent difficulties, such as the degree of bureaucratization needed

in order to provide information, or the difficulty in gaining access to process operators, due to the size of the

organization. The profile of the most successful projects needs to be considered, even as an initial hypothesis.

As the course is undertaken with the concept of being a co-requisite to an anchor course, the situation

presented to students involves various elements of novelty. Despite reports from literature, in which PBL

experiments are present as occurring in a pre-requisite format, that is, in which students have already been

trained in the subjects being evaluated in ongoing projects, the results obtained here demonstrate that both the

theoretical content, as well as practical results, have been successfully achieved in projects. The case described

here can be used as a reference for such approaches.

An important aspect to be considered in this study refers to the possibility of the student facing different real

cases. Students need to develop, in addition to the knowledge of the anchor discipline, skills in project management,

teamwork, leadership, communication skills and negotiation or solving conflicts, in other words, transverse

competencies are acquired and necessary for course completion. This case suggests that transversal knowledge

related to scope and risk planning, such as teamwork, leadership, conflict resolution, and decision-making are

more effective for this kind of project than communication skills. However, this topic demands deeper analysis,

with the application of specific research protocols in future works.

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 15/16

There are important limitations seen in the current research methods. On the one hand, the fact that the

same professor assessed all projects may have generated a certain bias, potentially minimized by the fact that

the final grades result from an average of the scores from two professors, and that there have been changes in

one over the seven evaluated semesters.

From the point of view of the projects carried out by students, as discussed, there is an evaluation of aspects

related to scope, time, stakeholders, communication and risks. Still, the overall grade is attributed in accordance

to the quality of students’ delivery. That is, “quality” is expected without first being required as one of the

elements of planning. Even when quality planning and specific accomplishments are difficult to be achieved

by students in a satisfactory manner, as they lack specific knowledge at the beginning of the course, it would

be interesting to analyze the impact of this variable in order to investigate more critical aspects of project

knowledge areas in active learning experiences.

References

Aquere, A. L., Mesquita, D., Lima, R. M., Monteiro, S. B. S., & Zindel, M. (2012). Coordination of student teams focused on project.

International Journal of Engineering Education

, 859. Retrieved in 24 August 2016, from http://hdl.handle.net/1822/18818

Balve, P., & Albert, M. (2015). Project-based learning in production engineering at the Heilbronn Learning Factory.

Procedia CIRP

104-108. http://dx.doi.org/10.1016/j.procir.2015.02.215.

Bassily, H., Sekhon, R., Butts, D. E., & Wagner, J. (2007). A mechatronics educational laboratory: programmable logic controllers and

material handling experiments.

Mechatronics

(9), 480-488. http://dx.doi.org/10.1016/j.mechatronics.2007.06.004.

Bezerra, E. C., Costa, A. L. M., & Riffel, D. B. (2010). An Engineering Curriculum for the XXI century. In

Proceedings of the Brazilian

Congress of Engineering Education (COBENGE)

, Brazil.

Coyle, E. J., Jamieson, L. H., & Oakes, W. C. (2005). EPICS: engineering projects in community sevice.

International Journal of Engineering

Education

(1), 139-150. Retrieved in 24 August 2016, from http://www.ijee.ie/contents/c210105.html

Crosthwaite, C., Cameron, I., Lant, P., & Litster, J. (2006). Balancing curriculum processes and content in a project centred curriculum.

Education for Chemical Engineers

(1), 39-48. http://dx.doi.org/10.1205/ece.05002.

Damgrave, R. G. J., & Lutters, E. (2016). Designing individual education in a group setting.

Procedia CIRP

, 733-738. http://dx.doi.

org/10.1016/j.procir.2016.04.187.

Dane, F. C. (1990).

Research methods

. Pacific Grove: Brooks/Cole Publishing Company.

De losRíos-Carmenado, I., López, F. R., & García, C. P. (2015). Promoting professional project management skills in engineering higher

education: project-based learning (PBL) strategy.

International Journal of Engineering Education

(1), 184-198. Retrieved in 24

August 2016, from http://www.ijee.ie/contents/c310115B.html

Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning.

Journal of

Engineering Education

(1), 103-120. http://dx.doi.org/10.1002/j.2168-9830.2005.tb00832.x.

Frank, M., Lavy, I., & Elata, D. (2003). Implementing the project-based learning approach in an academic engineering course.

International

Journal of Technology and Design Education

(3), 273-288. http://dx.doi.org/10.1023/A:1026192113732.

Gillette, D. D., Lowham, E., & Haungs, M. (2014). When the hurly-burry’s done, of battles lost and won: how a hybrid program of study

emerged from the toil and trouble of stirring liberal arts into an engineering cauldron at a public polytechnic.

Engineering Studies

(2), 108-129. http://dx.doi.org/10.1080/19378629.2014.944186.

Goldratt, E. M. (2014).

Corrente crítica: teoria das restrições em gerenciamento de projetos

. São Paulo: Nobel.

Goldratt, E. M., & Cox, J. (2014).

A meta

. São Paulo: Nobel.

Jeon, K., Jarrett, O. S., & Ghin, H. D. (2014). Project-based learning in engineering education: is it motivational?

International Journal

of Engineering Education

(2), 438-448. Retrieved in 24 August 2016, from http://www.ijee.ie/contents/c300214.html

Jugend, D., Barbalho, S. C. M., & Silva, S. L. (2014).

Gestão de projetos: teoria, prática e tendências

. Rio de Janeiro: Campus-Elsevier.

Kadlowec, J., Bhatia, K., Chandrupatla, T. R., Chen, J. C., Constans, E., Hartman, H., Marchese, A. J., Lockette, P., & Zhang, H. (2007).

Design integrated in the mechanical engineering curriculum: assessment of the engineering clinics.

Journal of Mechanical Design

129

(7), 682-691. http://dx.doi.org/10.1115/1.2722788.

Kolmos, A., Fink, F. K., & Krogh, L. (2004). The Aalborg model: problem-based learning and project-organized learning. In A. Kolmos, F.

K. Fink & L. Krogh (Eds.),

The Aalborg PBL model: progress, diversity and challenges

(pp. 9-18). Aalborgpp: Aalborg University Press.

Lima, R. L., Carvalho, D., Flores, M. A., & Van Hattum-Janssen, N. (2007). A case study on project led education in engineering: students’

and teachers’ perceptions.

European Journal of Engineering Education

(3), 337-347. http://dx.doi.org/10.1080/03043790701278599.

Lima, R. M., Mello da Silva, J., Hattum-Janssen, N., Monteiro, S. B. S., & Souza, J. C. F. (2012). Project-based learning course design: a

service design approach.

International Jornal of Services and Operations Management

(3), 292-313. http://dx.doi.org/10.1504/

IJSOM.2012.045660.

Lin, W., Yueh, H., & Chou, J. (2014). Electronic pet robots for mechatronics engineering education: a project-based learning approach.

International Journal of Engineering Education

(1), 231-239. Retrieved in 24 August 2016, from http://www.ijee.ie/contents/

c300114.html

Miranda, M. A. (2004). The grounding of a discipline: cognition and instruction in technology education.

International Journal of

Technology and Design Education

(1), 61-77. http://dx.doi.org/10.1023/B:ITDE.0000007363.44114.3b.

Mulcahy, R. (2013).

PMP® Exam Prep: accelerated learning to pass PMI’s PMP Exam: on you first try

. Minnesota: RMC Publications Inc.

Prince, M. J., & Felder, R. M. (2006). Inductive teaching and learning methods: definitions, comparisons, and research base.

Journal of

Engineering Education

(2), 123-138. http://dx.doi.org/10.1002/j.2168-9830.2006.tb00884.x.

Production, 27(spe), e20162259, 2017 | DOI: 10.1590/0103-6513.225916 16/16

Project Management Institute – PMI. (2013).

PMBOK: project management body of knowledge

(5th ed.). Pennsylvania: PMI.

Rabechini Junior, R., & Carvalho, M. M. (2013). Relationship between risk management and successful projects.

Produção

(3), 570-

581. http://dx.doi.org/10.1590/S0103-65132012005000091.

Ragusa, G., & Lee, C. T. (2012). The impact of focused degree projects in chemical engineering education on students’ research

performance, retention, and efficacy.

Education for Chemical Engineers

(3), 69-77. http://dx.doi.org/10.1016/j.ece.2012.03.001.

Ras, E., Carbon, R., Decker, B., & Rech, J. (2007). Experience management wikis for reflective practice in software capstone projects.

IEEE Transactions on Education

(4), 312-320. http://dx.doi.org/10.1109/TE.2007.904580.

Ríos, I., Cazorla, A., Díaz-Puente, J. M., & Yagüe, J. L. (2010). Project–based learning in engineering higher education: two decades of

teaching competences in real environments.

Procedia: Social and Behavioral Sciences

(2), 1368-1378. http://dx.doi.org/10.1016/j.

sbspro.2010.03.202.

Sipper, D., & Bulfin, R.L. (1997).

Production: planning, control and integration

. EUA: Mcgraw-Hill College.

Slack, N., Chambers, S., & Johnston, R. (2009).

Administração da Produção

. São Paulo: Atlas.

Soares, F. O., Sepúlveda, M. J., Monteiro, S., Lima, R. M., & Dinis-Carvalho, J. (2013). An integrated project of entrepreneurship and

innovation in engineering Education.

Mechatronics

(8), 987-996. http://dx.doi.org/10.1016/j.mechatronics.2012.08.005.

Taajamaa, V., Kirjavainen, S., Repokari, L., Sjöman, H., Utriainen, T., & Salakoski, T. (2013). Dancing with Ambiguity Design thinking

in interdisciplinary engineering education. In

Proceedings of the2013 IEEE Tsinghua International Design Management Symposium

(TIDMS)

, Shenzhen, China. http://dx.doi.org/10.1109/TIDMS.2013.6981258.

Thomas, J. W. (2000).

A review of research on project-based learning

. San Rafael: The Autodesk Foundation.

Tobin, K., Kahle, J. B., & Fraser, B. J. (1990).

Windows into science classrooms: problems associated with higher-level cognitive learning

New York: The Falmer Press.

Verzuh, E. (2011).

The fast forward MBA in project management

. Hoboken: John Wiley & Sons.

Vollmann, T.E., Berry, W.L., Whybark, D.C., & Jacobs, F. R. (2004).

Manufacturing planning and control for supply chain management

EUA: McGraw-Hill Companies.

Yin, R. K. (2010).

Case study: design and methods

. Porto Alegre: Bookmann.

Zhoua, C., Chen, H., & Luo, L. (2014). Students’ perceptions of creativity in learning Information Technology (IT) in project groups.

Computers in Human Behavior

, 454-463. http://dx.doi.org/10.1016/j.chb.2014.09.058.

Zindel, M. L., Mello da Silva, J., Souza, J. C. F., Monteiro, S. B. S., & Oliveira, E. C. (2012). A new approach in engineering education:

the design-centric curriculum at the University of Brasília-Brazil.

International Journal of Basic

Applied Sciences

(5), 97-102.

Retrieved in 24 August 2016, from http://www.ijens.org/Vol_12_I_05/127105-8585-IJBAS-IJENS.pdf

Received: Oct. 14, 2016

Accepted: June 23, 2017