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Students' Perception of Big Data Engineering in Higher Education Curricula: Expectations, Interest and Ethical Implications
Ioana-Georgiana Ciuciu, Petrescu Manuela-Andreea
TL;DR
Higher education programs need aligned theoretical and applied preparation for emerging Big Data domains, including responsible data handling. This study examines students’ expectations, interest, and ethical perspectives in a Master-level Big Data course, finding strong interest in practical learning and awareness of ethical challenges.
Problem
The study addresses how higher education can prepare students for emerging Big Data domains while incorporating ethical considerations into curricula.
Method
The study analyzes survey responses from students enrolled in a Master-level Big Data course to examine their expectations, interest, experience, and ethical perspectives.
Results
Students primarily value Big Data for practical and personal reasons, while all recognize major ethical challenges and Computer Science students additionally identify manipulative data use.
Takeaways & Limitations
Big Data curricula should align with students’ objectives and career expectations, emphasizing fundamental concepts, techniques, and hands-on activities.
Takeaways & Limitations
Because the study examined a particular cohort, its results cannot be generalized to society as a whole and should be extrapolated only cautiously to IT-related students.
Abstract
from arXiv · showhide
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 conducted with 42 of the 67 students enrolled in the Big Data course offered to Computer Science and Bioinformatics 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
The introduction positions Big Data Engineering as an important emerging field and argues that higher education should prepare students through dedicated, practical curricula. The study examines students’ expectations, interest, and ethical perspectives in a Master-level Big Data course.
- Context: Big Data Engineering combines Software Engineering with AI, IoT, and Cloud Computing to handle massive heterogeneous data for applications, analysis, and decision-making.Its infrastructure supports data storage, processing, and visualization.
- Educational motivation: Higher education programs should expose students early to theoretical and applied knowledge through dedicated curricula aligned with emerging-domain competencies and careers.The proposed learning environment emphasizes collaborative, hands-on projects, interdisciplinary case studies, and industry-academia collaboration.
- Study purpose: The study investigates students’ perspectives on integrating Big Data technologies into Master curricula and aims to support collaborative, interdisciplinary learning.It contributes to discussion of software engineering education practices for emerging technologies.
- Research questions: The research examines students’ IT experience, course expectations, interest in Big Data, and views on ethical issues, including whether background influences ethical perspectives.The questions address students enrolled in Computer Science and Bioinformatics Master programs.
- Paper structure: The paper is organized around related work, methodology, thematic-analysis results, interpretation, validity threats, and conclusions with future work.This structure moves from study context and design to findings, limitations, and implications.
2 LITERATURE REVIEW
The literature identifies a need to align higher-education curricula with Big Data employment demands, practical skills, and ethical responsibilities. This study responds by examining students’ attitudes, expectations, and ethical awareness through collaborative, interdisciplinary, hands-on learning.
- Workforce needs: Digitalization creates demand for professionals who can manage real-world Data Science projects and adapt to evolving technologies, requiring stronger university–employment connections.Prior work highlights both Big Data technology proficiency and collaboration between academia and industry.
- Competency requirements: Big Data professionals need more than tools and languages: real-world project capability and broad technical and soft skills remain difficult to find.Related work reports that soft skills may have a greater impact on employability than hard skills.
- Curriculum gap: Only 10% of reviewed studies addressed integrating Big Data into curricula, indicating limited literature coverage of this educational direction.The review also emphasizes alignment among the learning environment, objectives, training, and results.
- Study response: The study investigates students’ interest and expectations through research-informed hands-on projects, multidisciplinary teams, real-world use cases, and close IT-industry relations.The stated aim is continuous, collaborative adaptation of teaching strategy.
- Ethical education: Research on Big Data and AI ethics emphasizes societal and higher-education impacts and the need to integrate ethical frameworks into education.The study examines student awareness to address responsible data handling and ethical use within higher education.
3 STUDY DESIGN
The study uses an anonymous online survey of Master’s students in Bioinformatics and Computer Science, combining closed and open-ended questions with thematic analysis. Responses were collected during the first two weeks of the course and analyzed collaboratively into themes.
- Study scope: The study investigates students’ interest, expectations, and ethical perspectives on Big Data using an anonymous online survey conducted during the course’s first two weeks.Its stated scope includes student interest and expectations alongside ethical implications.
- Survey design: The questionnaire combined closed questions about students’ specificity, background, and IT work experience with open-ended questions examining interests and viewpoints.The authors jointly developed and agreed on the final questions.
- Participants: The target population comprised 67 second-year Master’s students in Bioinformatics and Computer Science, with 42 consenting participants and no participant selection.Everyone enrolled in the course was informed and could participate.
- Analytic grouping: Responses from Bioinformatics and Computer Science students were analyzed interdisciplinarily because Computer Science lines of study showed highly similar answers.The authors therefore considered separate Computer Science comparisons scientifically uninformative.
- Analysis approach: The analysis combined quantitative treatment of survey responses with qualitative thematic analysis of open-ended answers.The questionnaire followed established empirical-community standards.
- Thematic procedure: Authors independently coded responses, grouped key items into frequency-based themes, and discussed categorization and supporting information before finalizing the analysis.Grouping used generalization, removal, and reassignment of low-occurrence items.
- Interpretive caveat: Because a response could contain multiple key items, reported percentages can sum to more than 100%.Students’ answers sometimes contained four or five separately categorizable statements.
4 RESULTS
Students entered the course with different academic and work backgrounds, but broadly expected practical Big Data learning and expressed interest driven mainly by usefulness, personal interest, and career potential. Ethical concerns centered on security and privacy across students, while Computer Science students additionally mentioned bias, ecological impact, and manipulative data use.
- IT experience: The course included students from five study lines, with 13 Bioinformatics, 20 Data Science, 12 High Performance Computing, 20 Software Engineering, and 2 Applied Computational Intelligence students.
- IT experience: Most students had up to 2 years of IT work experience, while Computer Science students generally had more IT employment than Bioinformatics students.
- Course expectations: Students primarily expected practical learning about collecting, storing, and using Big Data, alongside improving their general knowledge.
- Course expectations: Computer Science students expressed more specific expectations, including data pipelines, automation, architecture, and Big Data analysis.
- Interest: Students’ prior knowledge differed by background: Bioinformatics students reported less Big Data knowledge, whereas Computer Science students generally had basic knowledge and prior AI coursework.
- Interest: Interest in Big Data was driven mainly by usefulness and growth potential, with smaller groups emphasizing that the field was interesting or personally engaging.
- Ethical concerns: Security concerns included privacy, security, ownership, consent, and sensitive-data storage, and were raised by students across backgrounds.
- Ethical concerns: Computer Science students additionally mentioned acquisition bias, ecological impact, and possible manipulative or abusive uses of data.
5 Discussion
Students generally value practical Big Data learning, while ethical concerns are widespread and vary somewhat by academic background. Computer Science students additionally identify bias and manipulative data use as ethical risks.
- Student backgrounds: Students’ backgrounds differ substantially in IT work experience, with Software Engineering students most consistently reporting such experience and Bioinformatics students least often doing so.Only one Bioinformatics student had IT work experience, compared with all Software Engineering students.
- Student interest and expectations: Students are interested in practical Big Data learning because they perceive it as useful, promising, and relevant to their jobs.Most students have little prior Big Data experience beyond coursework and need professional guidance to develop independent learning skills.
- Learning implications: Practical projects, teamwork, collaborative learning, and continuing academia–industry collaboration align with students’ expectations and reported preparation needs.The discussion connects the survey responses with related work emphasizing adaptable skills for changing Big Data projects.
- Ethical implications: All students recognize major Big Data ethical challenges, but Computer Science students additionally mention bias during data collection and processing and possible manipulative data use.Security, privacy, consent, and confidentiality are common concerns across responses.
6 Threats to Validity
The study addressed validity risks through survey-design procedures, participant protections, and text-analysis practices. Its external validity remains limited because the evidence comes from a particular student cohort.
- Validity strategy: The study followed community standards for qualitative surveys and examined construct, internal, and external validity threats.The authors specifically considered participant selection, dropout, author subjectivity, and research ethics.
- Construct validity: Survey questions were developed through a multistep procedure and aligned with the study objectives to reduce author bias.The questions combined closed items for classification with open-ended items for deeper examination of student perspectives.
- External validity: Because the study examined a particular cohort, its findings cannot be generalized to society as a whole and can only be cautiously extrapolated to IT- and Computer-Science-related students.
- Internal validity: Anonymous data collection, voluntary participation, and text-analysis standards were used to address ethical, dropout, and processing-subjectivity risks.Participants were informed about the study’s purpose and intended data use.
7 CONCLUSIONS AND FUTURE WORK
The study concludes that Big Data curricula should combine methodological guidance, foundational knowledge, hands-on work, diverse teams, and sustained industry collaboration. Future longitudinal research is intended to assess lasting course impact and links with professional trajectories.
- Conclusions: Students need curricula aligned with their learning objectives and future careers, emphasizing fundamental Big Data concepts, techniques, and hands-on activities.The authors identify methodological guidance from university educators as important for learning an emergent domain.
- Conclusions: Learning should occur through real-world group or team activities involving students with diverse backgrounds and experiences.
- Conclusions: Continuous academia–industry collaboration, including expert-led workshops and industry guest lectures, is presented as crucial for students to validate their competencies.
- Future work: A longitudinal study is in progress to examine the course’s lasting impact, changing perceptions and interests, learning outcomes, and professional trajectories after graduation.The proposed follow-up would support continuous improvement of the curriculum and teaching approach.