Investigating Undergraduate Brazilians Students’ Performance in

STEM Courses

Laci Mary Barbosa Manhães

, Jorge Zavaleta

, Renato Cerceau

3,4 c

Raimundo José Macário Costa

and Sergio Manuel Serra da Cruz

Departamento de Ciências Exatas, Biológicas e da Terra (PEB), Fluminense Federal University, Estr. João Jasbick s/n,

Santo Antônio de Pádua, Brazil

Mathematics Institute (CCMN/NCE), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Departament of Education & Research, Instituto Nacional de Cardiologia (INC), Rio de Janeiro, Brazil

Telehealth Center, Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil

Department of Computer Science, Federal Rural University of Rio de Janeiro, Seropédica, Brazil

Keywords: Data Science, Data Analysis, Educational Data Mining, ENADE, STEM, Tertiary Education.

Abstract: This article presents the characteristics of final year students enrolled in science, technology, engineering, and

mathematics (STEM) degrees in tertiary education in Brazil. Public datasets maintained by National Institute

for Educational Studies and Research Anísio Teixeira (INEP) were essential to promote access to knowledge

we extracted. This paper presents an innovative methodology of analyses about the National Assessment of

Student Achievement (ENADE) datasets; we investigated all the STEM degree courses. The dataset contains

527,058 data about all final year students who performed the exams in 2005, 2008, 2011, 2014, and 2017.

Although the datasets present many attributes, we examined them to compare male and female academic

students’ performances against the mean age and the grades obtained thought the years. We have used the

software R to perform the analysis and discuss the differences between the groups.

1 INTRODUCTION

The U.S. system of graduate education in science,

technology, engineering, and mathematics (STEM)

has argued that STEM teaching and learning

opportunities continuously improve significant

contributions to the growth of the U.S. economy, its

national security, and the health and well-being of its

people (National Academies of Sciences,

Engineering, and Medicine, 2018).

Like the USA, Brazil also must increase

investments and concentrate efforts to increase the

number of students and decrease the dropout rates of

STEM tertiary degrees. Besides that, the country must

support STEM teachers, encourage students to

engage in STEM fields, and increase students from

underrepresented groups in STEM degrees.

https://orcid.org/0000-0002-0605-3117

https://orcid.org/0000-0002-4747-8613

https://orcid.org/0000-0003-3953-4715

https://orcid.org/0000-0002-1115-633X

https://orcid.org/0000-0002-0792-8157

In this research, we analysed official datasets

produced by the Brazilian Education System, which

is somewhat complex and dependent upon various

policy and administrative levels but generates large

amounts of semi-structured data every year.

The National Institute for Educational Studies and

Research Anísio Teixeira (INEP) is a Brazilian

Federal research agency coordinated by the Ministry

of Education (MEC) that gathers and maintains data

and detailed information about all public and private

educational institutions, ranging from primary

education to higher education (INEP, 2020). Briefly,

the INEP site and its datasets constitute a snapshot of

the Brazilian educational scenario (INEP, 2021).

INEP is in charge of organizing and applying the

Brazilian National Assessment of Student

Achievement (ENADE), which is the annual exam

122

Manhães, L., Zavaleta, J., Cerceau, R., Costa, R. and Serra da Cruz, S.

Investigating Undergraduate Brazilians Students’ Performance in STEM Courses.

DOI: 10.5220/0010495201220130

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 2, pages 122-130

ISBN: 978-989-758-502-9

that is part of the National Higher Education

Assessment System (SINAES).

Historically, ENADE was created to evaluate the

quality of the tertiary education system. They are

divided into universities, university centers, and

nonuniversity institutions (private and public)

throughout Brazil (OECD, 2013). It aims to measure

and evaluate either in class or distance learning

students’ performance related to program content,

skills, and competencies acquired during their

courses. The exam comprises questions about three

core components: institutional evaluation, degree

course evaluation, and student achievement

assessment (INEP, 2020).

We must highlight that significant differences

exist between the private and public tertiary sectors

regarding the quality of professors and attendance.

Public universities are usually better equipped and

organized than those in the private sector, and such

characteristics have a clear on ENADE´s results.

Each year, students of a set of disciplinary fields

are compulsorily registered by their institutions to do

the ENADE exam. The disciplinary fields are

classified into three broad groups: (1) Health,

agriculture, natural resources, and related fields; (2)

STEM, architecture, pedagogical, literature, and

related fields; and (3) Social science, humanities,

culture, and design fields. The institutions and the

students of each field are evaluated in a three-year

cycle (INEP, 2021).

Yearly, INEP publishes the results of the exams

of each cycle. A final grade (ranging from 1 to 5) is

attributed to each institution’s tertiary degree. The

higher the grade achieved by the students, the better

the degree. Finally, the ENADE datasets are publicly

available at INEP site following open data principles

(INEP, 2021).

Like Brito (2008) and Zoghbi, Rocha, and Mattos

(2013), we advocate that data analysis at ENADE

datasets can be used to estimate higher educational

institutions’ efficiency in Brazil and support teachers’

work. Besides, we believe that academic managers

who need to execute institutional or course

evaluations or compare incoming students and

graduating ones can take advantage of our work.

The professional ENADE datasets analysis can

bring information and insight about new Brazilian

professionals’ competencies. However, few

institutions can afford sophisticated analyses or learn

about the data despite the vast amounts of available

educational data. Due to the time restrictions and

production goals, many academic managers act as a

simple collector of data to fill in administrative forms.

This paper advocate using Educational Data Science

(EDS) to explore ENADE’s datasets to aid the

academic staff.

Romero and Ventura (2017) described EDS as a

specific application of Data Science in the Education

field. Cao (2017) also mentioned relevant concepts

related to Data Science, and he highlighted novel

opportunities to explore new data domains like

economy and education.

This paper aims to investigate the characteristics

of final year students enrolled in the STEM tertiary

degrees in Brazil who performed the ENADE exams

in 2005, 2008, 2011, 2014, and 2017.

This work is organized as follows: the next section

presents the Material and Methods; it describes the

procedures used to access the database of INEP; the

details about the datasets’ structure; the selection of

the attributes and analysis performed. In the Results

and Discussion section, the statistical analyses and

graphs are discussed. Finally, the conclusions and

future works are presented in the last section.

2 MATERIAL AND METHODS

The ENADE exam started to be performed by INEP

in 2004 in Brazil. The INEP offers its results as public

files and microdata, and each file corresponds to a

specific year exam (INEP, 2021). As mentioned in the

introduction section, the set of information about all

ENADE exams is available, allowing us to perform

various cross-analyses.

The datasets are in CSV format. The records

contain the data for each student who performed the

exam. Its columns hold the data attributes collected or

generated during the registration or execution of the

exam. The student's identification is anonymized.

Briefly, each record of the dataset is composed of

two parts. The first part indicates the evaluations

regarded to the general knowledge (10 questions).

The second part indicates the domain knowledge (30

multiple choice questions and the grades of three

additional essay-type questions).

The final grade (FG) of the exam is calculated

according to both parts. It comprises 25% of the

general knowledge (GK) and 75% of the domain

knowledge (DK).

In general, the records contain multiples students’

attributes, such as sex, age, grades, social and

financial information, the tertiary degree which the

student is enrolled, the university information, and

other attributes (INEP, 2021). The data dictionary,

which accompanies each microdata, is presented in

Portuguese language, and describes all attributes.

Investigating Undergraduate Brazilians Students’ Performance in STEM Courses

123

Previous researches explored minor parts of the

datasets. For instance, Crepalde and Silveira (2016)

used the ENADE 2014 datasets to investigate

students’ performance (originated from public and

private institutions) considering sex, race, and

financial income. Later, Silva et al. (2017) analyzed

Math and Science students’ performance based on

ENADE 2014 datasets; the variables were analyzed

using multiple linear regression techniques and the

Stepwise method.

Vista, Figueiró and Mozzaquatro (2017) analyzed

the dataset of the ENADE 2014; the authors did a

statistical analysis to verify the performance of

undergraduate students in the Computer Science

degree of the state of Rio Grande do Sul, Brazil.

Santos and Noro (2017) compared the students’

performance at ENADE 2010 that participated in a

specific multidisciplinary project called PET-Saúde.

Moimaz, Amaral, and Garbin (2017) focused on

the undergraduate course of dentistry (oral medicine),

the authors analysed the ENADE exams of the several

years (2004, 2007, 2010, and 2013) using simple

statistical methods.

Neto et al. (2018) investigated the factor that

affects student performance in the Brazilian

undergraduate medical programs, and they

considered seven variables associated with results

obtained at ENADE 2010. The authors applied a

multivariate analysis model of binary logistics

regression.

This article proposes a broader approach,

different from previous works. We analysed the

whole dataset of all Brazilian STEM degree courses

and considered all types of Brazilian academic

organizations (universities, university centers, and

non-university institutions), and we analysed the data

of 527,058 final year students, the ones who

performed the ENADE exam in 2005, 2008, 2011,

2014, and 2017. We stress that our investigation also

students in class and distance learning in STEM

degrees.

Besides, our experiments were planned as the

following steps:

(i) Access the INEP site and selected the

microdata to download;

(ii) Select the attributes to perform the analysis;

(iii) Select the STEM final year students according

to degree code attribute;

(iv) Perform data cleansing and data checks to

identify invalid data and missing values;

(v) Remove null data and outliers;

(vi) Perform data analysis, including statistics;

(vii) Generate the data visualization as graphics.

Specifically, in the computational experiments,

the following attributes were considered across the

datasets: degree code (co_grupo), sex (tp_sexo), age

of the student (nu_idade), grades in General

Knowledge (GK) (nt_fg), grades in the Domain

Knowledge (DK) (nt_ce) and final grades (FG)

(nt_ger).

We must highlight that many students have

boycotted the first three-cycles of the ENADE exam

due to a political movement, some of them give no

answers to the questions. Perhaps, this can justify null

values, especially in the 2005 and 2008 exams.

Besides, we stress that due to the COVID-19

pandemic, the ENADE was not applied in 2020.

The experiments were executed as R statistical

software that followed these computational steps:

(i) Obtain the ENADE public datasets (microdata)

for the years 2005, 2008, 2011, 2014, and 2017;

(ii) Select the attributes (columns);

(iii) Verify and clean the data;

(iv) Do data analysis, including statistics;

(v) Do data visualization.

Statistical analyses were performed using the

programming language R (R Core Team, 2020), the

Integrated Development Environment (IDE) RStudio

(RStudio Team, 2020), and Microsoft Excel (2018)

were used to perform the steps (ii) to (v).

Before the statistical analysis, the selection step

was carried out, whose objective was to filter all

students enrolled in the STEM tertiary degrees

present in the microdata.

3 RESULTS AND DISCUSSION

In this section, three core features were analysed

about the final year students enrolled in STEM

tertiary degrees:

(1) Distribution by sex;

(2) Distribution by age;

(3) Analysis of grades, specifying the GK, DK and,

consequently, the exam’s final grade FG.

The first set of analyses is intended to compare

males’ and females’ percent of students finishing the

tertiary education in STEM fields (Table 1).

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Table 1: The number of male and female final year students

in STEM degrees by ENADE exams.

ENADE

exams

Male

STEM

Female

STEM

Total of

students

2005 24,612 16,468 41,080

2008 29,469 17,178 46,647

2011 48,351 27,725 76,076

2014 102,098 56,080 158,178

2017 133,532 71,545 205,077

Figure 1 shows a histogram comparing the

percentage of male and female final year students

enrolled in STEM degrees during the years. The

percentage of the female is reducing year by year in

the STEM field. Such observation means that the

workforce of the STEM field is composed

predominately of males.

Christie et al. (2017) discussed the female’s

participation in STEM field are decreasing in the past

years. Papadakis, Tousia, and Polychronaki (2018)

also related a continuous under-representation of

woman in the field of computer science in Greece,

they analysed the data of the Computer Science

Department of Crete, from 1985 to 2017. Loyalka et

al. (2019) showed a similar result when comparing

degree programs in China, India, Russia, and the

United States.

The 2020 edition of Education at a Glance report

provides an explanation of the under-representation

of women in some fields could be that they fear they

will not have equal career opportunities in those

fields, after completing their education (OECD,

2020).

We stress that our research results emphasize that

women are being underrepresented in technology in

Brazil.

Figure 1: Percentage of male and female final year students

in STEM degrees.

The second analysis is dedicated to identifying the

ages of the final year students. The goal is to

determine the students’ average age in STEM degrees

by sex.

Figure 2 shows that females are younger than

males in all years. OECD (2020) presents indicators

about the age of students in many countries.

The mean ages of males are increasing. For

instance, in 2005, 2008, 2011, and 2017 were 26.4,

26.4, 27.5, 27.7, and 27.4 years old, respectively. The

means age of the female students is lower than males

in the same period.

Figure 2: Average age of final year students enrolled in

STEM degrees by sex.

The boxplot is a graphical depiction of numerical

data through their quantiles. It is a simple way to

visualize outliers. Figure 3 shows the third quartile of

the years (2011, 2014, and 2017) that many male

students over 30 years old are finishing STEM

degrees. We observed that the median age is

increasing year by year. The outliers show many

males finishing over 40 year.

Figure 3: Boxplot of the age of male final year students in

STEM degrees.

Investigating Undergraduate Brazilians Students’ Performance in STEM Courses

125

In 2005, 2008, 2011, and 2017 females’ mean age

was 25.7, 24.9, 26.0, 26.4, and 25.8 years old.

The boxplot in Figure 4 shows additional

information. The third quartiles of the years (2005

until 2017) are more regularity ranging from 26 to 28,

then 25% of student female are finishing the STEM

degree over 26 years old.

Figure 4: Boxplot of the age of female final year students in

STEM degrees.

The third analysis identifies the trends of students'

grades to foresee the future labour force performance

in the STEM field. In other words, estimate the level

of knowledge acquired during the undergraduate

course in STEM fields by using the grades in general

knowledge (GK), domain knowledge (DK), and final

grades (FG) where the range of the grades is [0,100].

The GK grades related to the level of knowledge

about broad themes related to student’s competencies

and skills. It is part of the overall level of professional

excellence and indicates the education quality.

Figure 5: Average grade in GK of final year students in

STEM degrees by sex.

Figure 5 shows GK grades for the years 2005,

2008, 2011, 2014, and 2017, representing the

difference between males and females. Notably, the

average of grades in general knowledge is decreasing

year by year in both cases.

Figure 6 presents the boxplots about the male

performance in GK. Those results demonstrate that

the average is around 60%. One reason for significant

number of outliers is the number of students that

boycotted the first ENADE exams.

Figure 6: Boxplot of male final year students in STEM

degrees by GK grades.

Figure 7 shows the boxplots about GK’s female

performance; the same results compared with male

performance in the GK, it is around 60%.

Figure 7: Boxplot of female final year students in STEM

degrees by GK grades.

The DK grades related to the level of knowledge

of students in the STEM disciplines; they indicate the

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Competency-Based STEM Curriculum learned by the

student. This can indicate the professional

competencies and education qualities in the STEM

field.

Figure 8 shows DK’s grades for the years 2005,

2008, 2011, 2014, and 2017, representing the

difference between male and female students’

performance. Notably, the average of grades in DK is

increasing, but the average is meager. It is under 42,

and the female students are even lower than males

students in every exam.

Figure 8: Average grades in DK of final year students in

STEM degrees by sex.

Figure 9 shows the boxplots about the male

performance in DK, and the outliers indicate the

number of students who obtained a grade over 80 in

DK, which is an excellent grade and demonstrates a

high level of the future STEM professional is

increasing in the labour force. However, around 50%

of the students are under 40 that demonstrates an

insufficient level of knowledge.

Figure 9: Boxplot of male final year students in STEM

degrees by DK grades.

This study examined a wide array of data about

students’ exams, more specialized analyses to review

the scholarly research on educational practices at the

graduate level are necessary to improve Brazilian

educations system.

Comparing the performance between male

(Figure 9) and female (Figure 10) students, the

boxplots show that female performance in the domain

knowledge is lower than male’s performance.

In this article’s scope, it is impossible to

determine the reason for those results, but this

information reveals the need to understand more

about female performance and the consequences for

the labour force in STEM fields.

Figure 10: Boxplot of female final year students in STEM

degrees by DK grades.

The final grade (FG) is a composition of results

(25% of the GK and 75% of the DK). It represents the

overall result of the ENADE exam.

Figure 11 shows the final grades of the years

2005, 2008, 2011, 2014, and 2017. It represents the

differences between male and female students’

Figure 11: Final grade average of final year students in

STEM degrees by sex.

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127

performance. Notably, the average of grades for both

sexes is under 45, and the difference between males

and females average is evident.

Figure 12 shows the boxplots about the male

performance in final grades. These graphics present

interesting information. The upper quartiles outliers

indicate the number of students obtained over 80 in

final grades.

The value represents an outstanding grade and

demonstrates high-level knowledge. On the other

hand, most students have unsatisfactory grades. The

comprehensive insight about the performance of the

students in the average is low.

Figure 12: Boxplot of male final year students in STEM

degrees by FG grades in the ENADE exams.

Figure 13: Boxplot of female final year students in STEM

degrees by FG grades in the ENADE exams.

4 CONCLUSIONS

Governments and academics are increasingly looking

to international comparisons of education system.

Due to the importance of education in helping people

acquire skills to participate in society and labour

market. Particularly the study of STEM fields has

become a priority in many countries, problem

solving, and quantitative analysis are considered

essential in economy and are in high demand in the

labour market (OECD, 2020).

Some studies reviewed the inclusion of adult

women into the labour market, focusing on unequal

occupation, leadership roles, and underrepresentation

in certain professions, and how these facts were

related to the historical and cultural expectations

(Christie et al., 2017; Papadakis, Tousia, and

Polychronaki, 2018; Loyalka et al., 2019; OECD,

2020).

These studies are fair but do not consider the in-

depth Data Science analytical investigations. Our

study shows pieces of evidence that without a STEM

degree, women are less likely to occupy certain

positions in the increasing STEM labour market.

Our study used Brazilian public datasets, they

were essential to extract knowledge and reproductive

research, inspire new studies and opinion, and enables

the exploration of topics not envisioned by the

previous investigators (Pilat and Fukasaku, 2007).

The INEP maintains a vast number of public

datasets providing information about Brazilian

education. Notably, despite the tecnhical difficulties

and limited investiments, the ENADE is the unique

approach used to Brazilian undergraduate students’

academic performance. However, despite that, up to

now, few Brazilian educational institutions use

datasets to extract valuable quantitative knowledge.

Our experiments analysed the characteristics of

final year students in STEM degrees. We investigated

the ENADE exams from 2005, 2008, 2011, 2014, and

2017 to select the records of students of the tertiary

education level in STEM fields who took the exams

in those years.

The analyses were focused on: (1) distribution by

sex; (2) distribution by age; and (3) the analysis of

grades, specifying the GK, DK and FG grades

revealed sex and preformance differences.

Our results showed the number of female students

is reducing year by year in STEM degrees compared

to males’ percentages. The second analysis results

showed the mean ages of males are increasing each

year, and the means age of the female students is

under males in all years. The third analysis identifies

the trends of students’ grades.

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Notably, the average of GK grades is decreasing

year by year in both males and females. The DK

average grades is increasing for both, but the average

is meager. Futhermore, the DK average grades for

female students are even lower comparing than that

of males students in every exam. We consider that

such observations are being reflected in the Brazilian

STEM labour market, mostly regarded as inequalities

to women.

The final grade (FG) is a composition of results

(25% of the GK and 75% of the DK). It represents the

overall result of the ENADE exam. The average of

grades for females is increasing but is lower than for

males. For both sexes the average is under 45,

representing most undergraduates have an

unsatisfactory grade.

The methods used for this study incorporated in

the various R language libraries (R Core Team,

2020). Make this a powerful tool libraries for

performing the statistical analyses presented in this

research.

As future work, we intend to develop new

investigations and consider the new datasets to

understand how sex differences originated in the high

school have historically developed into inequalities at

the tertiary level.

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