Students’ Perception of Big Data Engineering in Higher Education

Curricula: Expectations, Interest and Ethical Implications

Ioana-Georgiana Ciuciu

and Manuela-Andreea Petrescu

Faculty of Mathematics y, Cluj-Napoca, Romania

{ioana.ciuciu, manuela.petrescu}@ubbcluj.ro

Keywords:

Big Data Engineering, Emerging Technologies, Higher Education, Curricula, Learning Path, Learner

Empowerment, Ethics, Collaborative and Interdisciplinary Approaches to Learning.

Abstract:

The study investigates students’ interest and expectations in a Big Data Engineering course integrated with a

Master curricula, as well as ethical implications of using Big Data. An anonymous online survey was con-

ducted with 42 of the 67 students enrolled in the Big Data course offered to Computer Science and Bioinfor-

matics Master’s programs. The responses were analyzed and interpreted using thematic analysis, highlighting

interesting aspects related to students’ expectations, interest, and their perspective of the ethical implications

of working with Big Data. The study concludes that, even though there is signiﬁcant difference in students’

background, the majority are interested in learning Big Data, for practical and personal reasons related to

the potential for career growth and their passion for the ﬁeld. The main expectation expressed is related to

enhancing their knowledge related to Big Data via practical activities. All students demonstrate awareness

of potential ethical threats related to security and privacy, while Computer Science students are aware of the

possibility of introducing bias in data during acquisition and analysis and of potential abusive data usage.

1 INTRODUCTION

If we look at the perceived value of Big Data today,

we can notice that Big Data, far from being a hype any

more, is prevalent in our daily lives throughout many

hype cycles

. Moreover, Big Data is an integral part

of the 4th Industrial Revolution, being used to offer

solutions to real-life problems in almost every possi-

ble domain. The concept is illustrated in (Mamadou

and Ernesto, 2020) with a parallel between the eco-

nomic and industrial digitalization known as Indus-

try 4.0 and the evolution of higher educational insti-

tutions in order to adapt and respond to the speciﬁc

needs of learners, introduced as University 4.0. In-

tegrating Software Engineering with emerging tech-

nologies of the present such as Artiﬁcial Intelligence

(AI), the Internet of Things (IoT) and Cloud Com-

puting, the ﬁeld of Big Data Engineering allows im-

plementing data ecosystems that make possible the

handling (i.e., storage and processing) of massive

amounts of heterogeneous data generated in real-time.

Most importantly, it offers the infrastructure to store,

https://orcid.org/0000-0002-7126-0585

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https://www.gartner.com/en, last accessed on

16.02.2025

process, and visualize data that is then served to vari-

ous applications for analysis and decision-making.

In this context, it is important that higher educa-

tion programs prepare students to reach their full po-

tential in the domain of Big Data Engineering by the

time of graduation. This is only possible by exposing

them early enough to theoretical and applied knowl-

edge from these emerging domains, through dedi-

cated and well-aligned curricula (Kim et al., 2023).

Students should be able to experiment with emerg-

ing data engineering technologies via collaborative,

hands-on projects with real-world interdisciplinary

case studies and continuous industry-academia col-

laborations, in order to build strong competencies and

prepare them for their future careers.

This study investigates the transformative power

of the Big Data technologies when included in the

Master program curricula, from the students’ perspec-

tive. We aim to empower students to reach their full

potential and desired careers in Big Data through ded-

icated, collaborative, interdisciplinary learning. Our

work contributes to the discussion of best practices

in software engineering education for emerging tech-

nologies, speciﬁcally Big Data.

We addressed the particular case of the students

enrolled in a Big Data course, titled ’Big Data Pro-

790

Ciuciu, I.-G. and Petrescu, M.-A.

Students’ Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications.

DOI: 10.5220/0013475000003928

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2025), pages 790-797

ISBN: 978-989-758-742-9; ISSN: 2184-4895

cessing and Applications’, given in English at Master

level from the Faculty of Mathematics and Computer

Science, Babes-Bolyai University, Cluj-Napoca, Ro-

mania. The main objective of the study is to illustrate

students’ expectations related to Big Data and their in-

terest in following a career path related to Big Data by

analyzing factors beyond their attraction and motiva-

tion in Big Data technologies. A secondary objective

of the study is to analyze ethical issues in Big Data.

The objectives of our study have been divided into the

following research questions that address the students

enrolled in the Master Programs in Computer Science

and Bioinformatics at Babes¸-Bolyai University after

completing two semesters:

RQ1. What is the students’ experience in IT?

RQ2. What are the students’ expectations from the

Big Data course?

RQ3. What is the students’ interest related to Big

Data

RQ4. What are the ethical issues in students’

opinion? Does background inﬂuence it?

The rest of the paper is organized as follows: Sec-

tion 2 describes related work, Section 3 addresses

the research methodology of the present study, Sec-

tion 4 presents the results of the thematic analysis,

while Section 5 interprets the results and highlights

important aspects with respect to the identiﬁed re-

search questions. Section 6 summarizes the potential

threats to validity and the actions taken to minimize

and mitigate them. Section 7 presents our conclusion

and future work.

2 LITERATURE REVIEW

Digitalization and emerging technologies demand

skilled professionals to manage real-world Data Sci-

ence projects and adapt to the evolving technolo-

gies landscape. Past and recent studies have shown

the need to bridge the gap between university cur-

ricula and employment regarding emerging technolo-

gies, and in particular Big Data. A 2014 study (Sig-

man et al., 2014) identiﬁed a signiﬁcant demand for

employees who are proﬁcient in Big Data technolo-

gies. The study emphasized the role of universities

in fulﬁlling this demand and proposed collaborative

initiatives between academia and industry to optimize

Big Data education.

In a research survey from the US by (Song and

Zhu, 2016), titled ”Big Data and Data Science: What

should we teach?”, the authors identiﬁed the biggest

bottleneck in the Big Data era as the production of

capable data scientists. The authors conclude that

technologies, tools, and languages can be learned,

but professionals who can do real-world Data Science

projects and possess the necessary Big Data skills and

knowledge are rare. Regarding the role of digitaliza-

tion in improving the skills and employability of en-

gineering students, the authors in (Xu et al., 2022)

identiﬁed that both hard and soft skills are important

in Big Data curricula, the latest having a greater im-

pact on employability. Students need to be taught fun-

damental concepts, algorithms, and architectures, but

most importantly they need to have hands-on experi-

ence with Big Data technologies.

A relatively recent study by (Baig et al., 2020)

reviewed the main research directions related to Big

Data in education. The integration of Big Data into

curriculum was the least represented direction in the

literature (only 10% of the reviewed studies). The

results show that integrating Big Data into the cur-

riculum requires an adaptation of the curriculum to

the learning environment and a good alignment be-

tween the training factor, learning objectives, and re-

sults, also conﬁrmed by other studies such as (Nel-

son and Pouchard, 2017; Sledgianowski et al., 2017).

The authors of these studies discuss the importance

for teachers to be able to measure students learn-

ing behavior and attitude simultaneously and adapt

their teaching strategy accordingly. The integration

of Big Data into the curriculum has been a challenge

in many ﬁelds of education (Dzuranin et al., 2018).

Big Data analysis also integrates generative artiﬁcial

intelligence (i.e., Chat GPT). In (Valle et al., 2024),

the authors analyze the students’ behavioral inten-

tion toward using Artiﬁcial Intelligence in education.

The results show that social inﬂuence and perceived

knowledge of the use of AI are signiﬁcant predictors

of the attitude toward the use of AI. In (Kumar et al.,

2024) authors provide a comprehensive examination

of the positive and negative impacts of generative AI

on higher education.

Similarly, in this study we propose to investi-

gate students’ attitude towards learning and using

Big Data by analyzing their interest and expecta-

tions in this ﬁeld. The aim is to continuously im-

prove and adapt the teaching strategy in a collab-

orative and dynamic setting where students prac-

tice research-informed hands-on project tasks within

multi-disciplinary project teams around real-world

use cases, in tight relation with the IT industry.

Research on the ethical implications of Big Data

and AI highlights the challenges and potential im-

pact on society in general and higher education in

particular (Komljenovic et al., 2024; Lundie, 2024).

Researchers emphasize the critical need to integrate

ethical frameworks of AI and Big Data into higher

education (Kumar et al., 2024). By exploring stu-

Students’ Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications

791

dent awareness of ethical considerations related to

Big Data in this paper, we aim to contribute to the crit-

ical aspect of responsible data handling and the ethi-

cal implications of its use within higher education.

3 STUDY DESIGN

The study’s design and data gathering details are ad-

dressed in this section. This study’s design complies

with recognized community guidelines for qualitative

surveys (Ralph, Paul (ed.), 2021). The following pil-

lars served as the foundation for the study:

Scope: The main scope of this paper was to ﬁnd the

student’s interest and expectations, as well as ethical

implications of big data.

Period: The study was based on an anonymous online

survey that remained open for the ﬁrst two weeks, at

the beginning of the course.

Method: We used a hybrid strategy for data analy-

sis. The methodology integrated qualitative analysis

of the data gathered for answers to open-ended ques-

tions with quantitative analysis for answers to closed

questions.

Tools: During the data analysis process, we stored in-

termediate data using various tools and we used Mi-

crosoft Teams for the survey as the students were al-

ready enrolled in the platform.

3.1 Survey Design

With a focus on the study’s research scope, each au-

thor elaborated a set of survey questions, both authors

analyzed the set of questions, discussed and agreed on

which questions to ask, and also agreed on the ﬁnal

version of each question. Although closed questions

allowed us to classify and group responses, open-

ended questions were designed to allow for a more

thorough examination of the student’s interests and

point of view. The closed questions - Q1, Q2, and Q3-

consisted of questions related to the student’s speci-

ﬁcity, background, and work experience in the IT do-

main. We did not ask for information that could help

us identify the participants. The open questions were

related to Big Data, the questions are listed in Table1.

Even a short period of work experience, as stu-

dents might have (internships, short-term contracts),

provides valuable practical insights that help deepen

one’s understanding of a domain, so we added work

experience-related questions (Q2 and Q3).

Table 1: Survey Questions.

Q1. Please specify your domain of study.

Q2. Do you work in the IT industry?

Q3. Please specify for how long you have worked

(years).

Q4. What are your expectations related to this Big Data

course?

Q5. What knowledge related to AI / Big Data do you

have?

Q6. Why are you interested in the ﬁeld of Big Data?

Q7. What do you consider to be the main ethic-related

issues when dealing with Big Data?

3.2 Participants

Our survey’s target audience was formed by second-

year Master students studying Bioinformatics (from

the Faculty of Biology) and Computer Science (from

the Faculty of Mathematics and Computer Science),

enrolled in the Big Data course. The set of partici-

pants was made up of 67 students, of whom 42 con-

sented to participate in the study. There was no par-

ticipant selection; everyone enrolled in the course was

informed and could take part in the survey. We paid

attention to ethics, so we informed the students that

the survey was anonymous and the participation was

voluntary. We also let them know how we are going

to use the collected data.

We identiﬁed and analyzed the two groups (Bioin-

formatics (BI) and Computer Science (CS)) of stu-

dents’ responses on an interdisciplinary basis. When

we started analyzing the data, we noticed that there

was a high degree of similarity in the answers re-

ceived from the students enrolled in Computer Sci-

ence different lines of study. For this reason, we con-

sidered that a comparative analysis of the responses

received from CS students is not relevant and does

not represent a scientiﬁc interest and that an inter-

disciplinary point of view would be more interesting.

The cohorts of CS students were also more similar

in terms of work experience: most of them had at

least one internship experience in different compa-

nies, compared to the BI group, where only one per-

son had IT working experience. In addition, having

too many groups for analysis could affect the pre-

sentation of the paper by increasing the complexity

without adding a scientiﬁc beneﬁt. In conclusion, we

consider that a comparative analysis of the results be-

tween the two groups, BI and CS, is more relevant for

the purpose of this study.

3.3 Methodology

We conducted a survey with open-ended and closed-

ended questions. Quantitative methods were used to

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792

analyze and interpret the answers to open-ended ques-

tions. The questionnaire surveys were in accordance

with the established standards of the empirical com-

munity (Ralph, 2021).

Data Collection. When we proposed that the

questions be set in English, we considered two as-

pects: the ﬁrst was that their ﬁeld of study is English-

based. The second aspect was to reduce any potential

risks associated with translation. The responses’ data

were gathered just as they were written by the stu-

dents, without any alterations. The survey remained

open for two weeks. The anonymous survey link was

sent through their MSTeams faculty account.

Data Analysis. We used thematic analysis to in-

terpret the responses received for the open questions

and performed the following next steps:

1. The authors independently attempted to ﬁnd

codes or key items within the responses re-

ceived.

2. The resulting key items were grouped into

themes according to their frequency using tech-

niques such as generalization, removal, and re-

assignment to move the key items with low oc-

currence to larger themes.

3. The ﬁnal phase involved a discussion among

the authors related to supporting information

for the categorization process, representations,

and other issues.

Although some students’ comments were brief,

many others contained up to four or ﬁve statements

or explanations (which could be categorized into 4-5

key items). A response may therefore include one or

more key items, as a consequence, the sum of all per-

centages in our analyses will be greater than 100%.

4 RESULTS

The subsequent analysis focuses on the evaluation of

the student responses to the survey in relation to the

four main research questions of the study. Each ques-

tion enables a comprehensive examination of the per-

ceptions, interest, expectations, and ethical issues of

the students based on the responses received. The

data collected are comparatively analyzed based on

students’ background, knowledge, and experience.

4.1 RQ1. What Is the Students’

Experience in IT?

To ﬁnd the answer to this question, we analyzed the

responses received for questions Q1, Q2, and Q3 from

the survey. The ﬁrst question was needed as there

were students from ﬁve lines of study enrolled in

this course, and we expected to have differences in

their responses based on the line of study, as fol-

lows: Bioinformatics - 13 students; Data Science -

20 students; High Performance Computing - 12 stu-

dents; Software Engineering - 20 students; and Ap-

plied Computational Intelligence - 2 students.

Figure 1: Students’ distribution by line of study and work

experience.

From second question answers, we found out that

only one of the students from Bioinformatics works in

an IT related domain. Students in Computer Science

have the majority of jobs in IT related ﬁelds. For ex-

ample, most of the students in Software Engineering

work in IT as can be seen in Figure 1. The values in

the ﬁgure represent the percentages of students who

work (in any domain) compared to the number of stu-

dents enrolled in a speciﬁc line of study.

Figure 2: Students’ distribution by years of work experience

in IT.

As we considered the IT work experience to be

relevant, even if students might not have work experi-

ence in Big Data, but have overall programming expe-

rience, so we asked for the number of worked years in

the IT domain. Most of them (with a few exceptions)

have up to 2 years of work experience (Figure 2).

Based on these answers, students from the Com-

puter Science lines of study seem to have more in

Students’ Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications

793

common as compared to students enrolled in Bioin-

formatics. As there are similarities between Com-

puter Science enrolled students, we decided to focus

in this research on two groups: Bioinformatics and

Computer Science students.

4.2 RQ2. What Are the Students’

Expectations from the Big Data

Course?

To ﬁnd the answer to this question, we analyzed the

responses received in the survey questions Q4 and Q5.

The purpose was not only to see their expectations,

but also to see if there is a correlation between their

experience and their expectations.

One student did not reply to this question, all the

other speciﬁed at least one thing they would like to

learn. When we analyzed the data, we took into con-

sideration all the ideas/ key items mentioned by stu-

dents, and we computed the percentages using the fol-

lowing ratio: number of appearances versus the to-

tal number of students in the speciﬁc cohort (Bioin-

formatics or CS). As most of the students mentioned

more than one, the sum of the percentages of all key

elements exceeds 100%. The same method of calcu-

lus will be used for all the percentages in the ﬁgures

from Subsections RQ2, RQ3 and RQ4. The students

enrolled in CS had more expectations and mentioned

more key items compared to Bioinformatics enrolled

students, but both cohorts scored high in practical as-

pects, in collecting, storing, and using Big Data. The

main classes of expectations differentiated by line of

study are presented in Figure 3.

Figure 3: Students’ expectations from the Big Data course.

Practical aspects represent the most mentioned ob-

jective and many times are correlated with the desire

to improve their knowledge: ”Find some knowledge

about big data and situations when it is useful and

how to use it.”, ”Learn how the data is gathered and

stored.”. The interest in learning more is sometimes

related to economical aspects and to career: ”I want

to learn more about this subject and understand how

can I apply it in my career”.

The second most mentioned objective is improv-

ing their knowledge: ”learn new ways to handle big

data in general”, ”to improve my skills”. However,

some students seem to be aware of the differences be-

tween the students from Computer Science and those

from Bioinformatics. CS students have a different set

of skills and IT related knowledge and it is difﬁcult

to cope with their learning pace: ”I do not person-

ally like the idea of multidisciplinary in this course.

In the past, it has not worked too well. People from

Bioinformatics have their own rhythm”.

Computer Science students were a little bit more

speciﬁc and knowledgeable in what they want to

learn, proving they already have some basic knowl-

edge or concepts. They mentioned: ”Data pipelines

and some automation”, ”improve my knowledge of

Big Data, the architecture and the analysis of it”.

Based on the key items found, the students’

knowledge oscillates between none/hobby level, to

basic, to intermediate: ”almost none”, ”I think that

my knowledge in this domain is at a basic to interme-

diate level”. Some students mention that they have

basic knowledge: ”Basic knowledge: visualization,

simple algorithms (clustering, classiﬁcation, decision

tree...), data cleaning, dashboard”, others just state

that they ”work with Big Data and AI”. However,

based on the results, the Bioinformatics students se-

lected this course based mainly on personal interest

and have less knowledge in the domain compared to

Computer Science students who have basic knowl-

edge in Big Data and have previously had AI courses

”I studied AI during University and Master courses

and did my bachelor’s degree in this domain”.

In conclusion, all students want to improve their

knowledge, even if the base level is different between

the two groups, Computer Science and Bioinformat-

ics students. The previous knowledge of AI/Big Data

is correlated with the line of study, suggesting that

even if there was a personal interest in AI/Big Data,

students do not learn by themselves. All students ex-

press a high interest in practical aspects of data gath-

ering, data storage, and data manipulation.

4.3 RQ3. What Is the Students’ Interest

Related to Big Data?

After analyzing the responses received to the survey

question Q6 (”Why are you interested in the ﬁeld

of Big Data?”), the practical aspects came through.

We grouped the selected items in two categories:

Practical reasons that includes ”Useful”, ”Relevant”

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794

and having ”Potential” domain of activity and Per-

sonal reasons which includes ”Interesting”, ”Increase

knowledge”, to know how to do ”Data Analysis”.

Other reasons such as ”how to keep the data clean

and secure” were less prevalent and were grouped

into ”Other” reasons as can be seen in Figure 4.

Figure 4: Students’ interest related to Big Data.

All students are aware of the potential and use-

fulness of Big Data, regardless of their background.

They perceive the importance of manipulating and

getting information from large data sets, in Bioin-

formatics ”because Bioinformatics deals with large

amount of data, and I would like to know how to pro-

cess them” and Computer Science ”I’m particularly

interested in data analysis, and extract capital infor-

mation from big batch of data”. Being able to extract

results is seen as a potential solution for problems

humans ﬁnd difﬁcult to solve: ”I’m curious about

how data can be used to uncover insights and solve

complex problems”, ”I am interested in Big Data be-

cause I believe it has the potential to drive innovation

and improve decision-making in various ﬁelds, from

healthcare to business”. Sometimes they refer to the

general impact: ”the world basically revolves around

large amounts of data being stored and processed”,

others are more focused on their own beneﬁt: ”Be-

cause I enjoy working with data and using it is a cru-

cial step in my industry (ﬁnance and trading)”. How-

ever, the largest majority of students (from both sets-

Bioinformatics and Computer Science) appreciate the

usefulness of the domain as the major factor for their

interest in Big Data.

A smaller subset of the respondents pointed out

their own preferences related to the domain: ”it’s a

very vast and interesting domain” or to their feelings:

”I am curious how is it to work with Big Data”.

In conclusion, the students’ interest related to Big

Data lay mainly in practical aspects: usefulness and

its potential for growth (implying more, better paid

jobs, more positive impact for society). For a smaller

percentage, an important factor are personal passions.

4.4 RQ4. What Are the Ethical Issues in

Students’ Opinion? Does

Background Inﬂuence It?

Based on the answers received to sruvey question Q7,

we found a set of key items related to students ethi-

cal concerns, and we grouped them into the following

classes:

• Security concerns: data privacy, data security,

ownership, consent to use personal data;

• Data collection: introduced bias, collection pro-

cess;

• Other: ecological issues, destructive potential

(manipulate people);

• No answer / unrelated answer.

The most mentioned ethical issues were raised by

all students, their lines of study did not inﬂuence the

results as can be seen in Figure 5.

Figure 5: Students’ ethical concerns related to Big Data.

Some students mentioned more than one concern

related to data security: ”data security, transparency,

data ownership”, ”the possibility of personal data

breach”. Security includes (sensitive) data storage:

”The main issue I consider is the careful storage of

sensitive data”, data privacy or using it without the

consent of the owner: ”The main ethical issues in Big

Data include privacy, consent, and security. Privacy

is a concern when personal data is used without con-

sent, and bias in algorithms can lead to unfair out-

comes”, ”conﬁdentiality issues especially if we think

of the medical ﬁeld”. There is a difference between

the Bioinformatics and Computer Science students, as

the latter are more aware that the collection process

and data processing could introduce bias that would

mislead and inﬂuence the results: ”potential bias in

data collection and analysis”.

Other ethical issues were mentioned only by Com-

puter Science students. The ecological impact was

Students’ Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications

795

mentioned by 9.68% of them: ”There is also ecologi-

cal problem because of the use of huge data-centers”.

The scope and use of data were mentioned by other

6.45%: ”Data can be used against people to manip-

ulate them or to abuse them”. We also got some un-

related answers and students that did not answer this

question (6.45% of Computer Science students) and

only one from Bioinformatics students.

In conclusion, the main ethical concerns related

to Big Data are relatively similar for all students, but

only Computer Science students mentioned ecologi-

cal or manipulative possible usage.

5 DISCUSSION

While examining the students’ backgrounds in our

participant set, we noticed a difference between their

work experience, as just one of the students from

Bioinformatics has work experience in IT while all

the students from Software Engineering have work

experience in IT. The percentages for the other groups

of Computer Science students were between these ex-

tremes. There were some exceptions, students that

have work experience in other domains, but their

prevalence was small in our participant set.

Based on the results in RQ1, Computer Science

students seem to ﬁnd more job opportunities and are

already involved in the job market. However, most

of the students are interested in practical aspects of

learning Big Data, as it is perceived to be useful, has

a lot of potential, and helps them perform at their

jobs. Practical aspects tend to be the main reasons for

choosing to enroll in this course. (Note that the course

is mandatory only for students in the High Perfor-

mance Computing Master Program, while for all the

others, it is optional). Even if the course is perceived

to be useful, practical and interesting, most of the stu-

dents have no previous experience in Big Data or have

mainly an experience due to the courses in their Bach-

elor and/or Master program. The Internet is abundant

in public courses and learning resources; however, the

students do not seem to have sufﬁciently developed

their skills to learn Big Data technologies by them-

selves, without a professional to lead their learning

process. The main question is how can we, as higher

education professionals, help students learn the auto-

didactic skills related to Big Data? How can we pre-

pare students to become more efﬁcient in learning by

themselves when they are interested in a speciﬁc Big

Data topic?

The results on RQ2 and RQ3 conﬁrm and align

with the results from the related work of the paper, re-

garding the importance of practical Big Data projects,

of team-work and collaborative learning involving

students with various levels of technical background

and the continuous academia-industry collaboration

in order to optimize the students’ learning path and to

better prepare them for their future jobs. The answers

to the survey also conﬁrm that students are aware of

what is highlighted in the literature, namely the urgent

need for professionals who possess the skills to learn

new technologies and adapt to any Big Data project.

The results from RQ4 on ethical issues show that

while all students are well aware of the main ethical

challenges (with small variations based on the stu-

dents’ line of study), only Computer Science students

mentioned possible non-ethical (manipulative) usage

of data.

6 THREATS TO VALIDITY

Our goal was to reduce any possible risks by analyz-

ing and implementing the community standards de-

scribed in (Ralph, 2021). Furthermore, we addressed

the possible validity risks that have been identiﬁed in

software engineering research (Ralph, 2021). Three

aspects have been identiﬁed and examined: construct

validity, internal validity, and external validity as a

result of the guidelines taken into account. We paid

particular attention to the participant related threats:

participant set, participant selection, dropout contin-

gency measures, and to author biases in order to en-

sure internal validity.

Construct Validity: The questions were created

using a multi-step procedure described in the Survey

Design in order to reduce the writers’ biases. The pro-

posed survey questions were in line with the stated

study objectives of the Introduction.

External Validity: We investigate the possibility

of extrapolating our study’s results. We point out that

since we looked at a particular cohort, no generaliza-

tions can be made to the entire society. Because of

this, we can, with some caution, extrapolate the re-

sults to the group of students enrolled in ﬁelds related

to IT/Computer Science.

Internal Validity: Based on (Ralph, 2021), we

determined the selection of participants and partici-

pants, the dropout rates, the subjectivity of the authors

and the ethics as possible internal threats. All par-

ticipants in the Big Data course have been informed

about the voluntary survey and invited to participate.

As a result, the target group of participants was all-

inclusive, removing any possible dangers related to

the participant selection or set of participants. Be-

cause the survey was voluntary, we had few options

to reduce dropout rates. We have taken into account

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796

and investigated the potential subjectivity of the au-

thor in data processing. By employing text analysis

in accordance with the recommended data processing

standards, we aimed to reduce this risk. We showed

participants that we were committed to ethics by in-

forming them of our goal, by using anonymous data

gathering methods, and letting them know how we in-

tended to use the data.

7 CONCLUSIONS AND FUTURE

WORK

The study is founded on four central research ques-

tions which we have methodologically approached

to investigate students’ perception of Big Data in

higher education. The results conﬁrm the main chal-

lenges related to Big Data in education, as identiﬁed

in the related work of the paper. As the ﬁrst impli-

cation of the study’s results, students need method-

ological guidance from university educators in order

to be able to learn and adapt to the emergent nature

of the Big Data domain. Most importantly, in the

ﬁeld of Big Data, students need curricula that align

with their learning objectives and expectations and

with their future professional careers. The curricula

should build competencies around fundamental con-

cepts and techniques, and most importantly around

hands-on activities. Students learn best in relation

with each other; therefore, we recommend that learn-

ing occur in groups or teams of students with diverse

backgrounds and experiences, in settings as close to

the real world as possible. Another important factor

to the success of students’ learning path is the con-

tinuous academia-industry collaboration. Integrating

guest lectures from industry professionals and prac-

tical workshops led by experts working daily with

emerging Big Data technologies, where students can

validate their competencies, is crucial for learning in

this domain.

A longitudinal study to reveal further insights into

the course’s lasting impact is in progress. Moreover,

it would be interesting to analyze how the students’

perceptions and interests evolve after the course. This

would imply another survey, possibly combined with

student evaluation results. The future approach could

also be used to assess learning outcomes and their cor-

relation with students’ professional trajectories upon

graduation. This would bring an important contribu-

tion towards the continuous improvement of the exist-

ing curriculum and the teaching approach, in order to

better prepare students for the challenges and oppor-

tunities of Big Data Engineering.

REFERENCES

Baig, M. I., Shuib, L., and Yadegaridehkordi, E. (2020). Big

data in education: a state of the art, limitations, and

future research directions. Int J Educ Technol High

Educ, 17(44).

Dzuranin, A. C., Jones, J. R., and Olvera, R. M. (2018). In-

fusing data analytics into the accounting curriculum:

A framework and insights from faculty. Journal of

Accounting Education, 43:24–39.

Kim, K., Kwon, K., and Ottenbreit-Leftwich, A. (2023).

Exploring middle school students’ common naive

conceptions of artiﬁcial intelligence concepts, and

the evolution of these ideas. Educ Inf Technol,

28:9827–9854.

Komljenovic, J., Sellar, S., and Birch, K. (2024). Turning

universities into data-driven organisations: seven di-

mensions of change. High. Educ.

Kumar, S., Rao, P., Singhania, S., Verma, S., and Kheterpal,

M. (2024). Will artiﬁcial intelligence drive the ad-

vancements in higher education? a tri-phased explo-

ration. Technological Forecasting and Social Change,

201.

Lundie, D. (2024). The ethics of research and teaching in

an age of big data. Journal of Comparative & Inter-

national Higher Education, 16.

Mamadou, L. and Ernesto, E. (2020). University 4.0: The

industry 4.0 paradigm applied to education. J. of the

Acad. Mark. Sci., 43:115–135.

Nelson, M. S. and Pouchard, L. (2017). A pilot “big data”

education modular curriculum for engineering gradu-

ate education: Development and implementation. In

2017 IEEE Frontiers in Education Conference (FIE),

pages 1–5.

Ralph, P. (2021). Acm sigsoft empirical standards for

software engineering research, version 0.2. 0. URL:

https://github. com/acmsigsoft/EmpiricalStandards.

Sigman, B. P., Garr, W., Pongsajapan, R., Selvanadin, M.,

Bolling, K., and Marsh, G. (2014). Teaching big data:

Experiences, lessons learned, and future directions.

Educ Inf Technol.

Sledgianowski, D., Gomaa, M., and Tan, C. (2017). Toward

integration of big data, technology and information

systems competencies into the accounting curriculum.

Journal of Accounting Education, 38:81–93.

Song, I. Y. and Zhu, Y. (2016). Big data and data science:

what should we teach? Expert Systems, 29:364–373.

Valle, N. N., Kilat, R. V., Lim, J., General, E., Cruz, J. D.,

Colina, S. J., Batican, I., and Valle, L. (2024). Mod-

eling learners’ behavioral intention toward using ar-

tiﬁcial intelligence in education. Social Sciences &

Humanities Open, 10.

Xu, L., Zhang, J., Ding, Y., Sun, G., Zhang, W., Philbin, S.,

and Guo, B. (2022). Assessing the impact of digital

education and the role of the big data analytics course

to enhance the skills and employability of engineering

students. Front. Psychol, 13.

Students’ Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications

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