DOI : https://doi.org/10.18517/ijaseit.11.6.12539

Developing Big Data Analytics Course for Non-ICT Major University Students

JongBeom Lim (1), ChangHak Moon (2)
(1) Smart Contents Major, Division of ICT Convergence, Pyeongtaek University, 111 Yongyi-Dong, Pyeongtaek-si, Gyeonggi-do 17869, Korea
(2) College of Japanese, Hankuk University of Foreign Studies, 107 Imun-ro, Dongdaemun-gu, Seoul 02450, Korea
Fulltext View | Download
How to cite (IJASEIT) :
[1]
J. Lim and C. Moon, “Developing Big Data Analytics Course for Non-ICT Major University Students”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 6, pp. 2503–2508, Dec. 2021.
Citation Format :
In the fourth industrial revolution's education era, there is no boundary between majors or subjects, and it is common for university students to enrol in information and communication technology (ICT)-related courses as convergence education blends different disciplines. Today's job market is getting more competitive and requiring higher skills in ICT and computational thinking. Since non-ICT major students rarely have programming experiences and knowledge in regular classes, teaching a big data analytics course for non-ICT major university students is not easy. Thus, it is vital to develop a curriculum that comprises easy-to-follow and easy-to-understand modules. In this paper, we develop a big data analytics course for non-ICT major university students. The proposed big data analytics course for non-ICT major students comprises two parts: (1) basic programming skill modules with step-by-step guidelines and (2) extension to big data analytics modules with laboratory exercises, with the five principal programming modules based on the Python programming language. First, our investigation discusses the suggestions and limitations of the big data analytics course for non-ICT major university students. Then, we recommend programming languages, integrated development environments (IDEs), and useful tools that help learners perform programming exercises and milestone projects. The learning objectives and course design models are carefully selected based on Bloom's taxonomy with six thinking levels and five.

S. ElMassah and M. Mohieldin, "Digital transformation and localizing the Sustainable Development Goals (SDGs)," Ecological Economics, vol. 169, p. 106490, 2020, doi: https://doi.org/10.1016/j.ecolecon.2019.106490.

J. Kaur, R. Shedge, and B. Joshi, "Survey of Big Data Warehousing Techniques," Inventive Communication and Computational Technologies. Springer Singapore, Singapore, pp. 471-481, 2020.

H. B. Santoso and P. O. H. Putra, "Bridging the Gap between IT Graduate Profiles and Job Requirements: A Work in Progress," 2017 7th World Engineering Education Forum (WEEF). pp. 145-148, 2017, doi: 10.1109/WEEF.2017.8467146.

M. H. ur Rehman, I. Yaqoob, K. Salah, M. Imran, P. P. Jayaraman, and C. Perera, "The role of big data analytics in industrial Internet of Things," Future Generation Computer Systems, vol. 99, pp. 247-259, 2019, doi: https://doi.org/10.1016/j.future.2019.04.020.

S. Kim and H. Y. Kim, "A Computational Thinking Curriculum and Teacher Professional Development in South Korea," in Computational Thinking in the STEM Disciplines: Foundations and Research Highlights, M. S. Khine, Ed. Cham: Springer International Publishing, 2018, pp. 165-178.

A. Pí¼rbudak and E. Usta, "Collaborative group activities in the context of learning styles on web 2.0 environments: An experimental study," Participatory Educational Research, vol. 8, no. 2, pp. 407-420, 2021, doi: 10.17275/per.21.46.8.2.

S. Siva, T. Im, T. McKlin, J. Freeman, and B. Magerko, "Using Music to Engage Students in an Introductory Undergraduate Programming Course for Non-Majors," Proceedings of the 49th ACM Technical Symposium on Computer Science Education. ACM, Baltimore, Maryland, USA, pp. 975-980, 2018, doi: 10.1145/3159450.3159468.

P. Dempster, D. Onah, and L. Blair, "Increasing academic diversity and inter-disciplinarity of Computer Science in Higher Education," Proceedings of the 4th Conference on Computing Education Practice 2020. Association for Computing Machinery, Durham, United Kingdom, p. Article 10, 2020, doi: 10.1145/3372356.3372366.

A. v Arzhanovskaya, E. A. Eltanskaya, and L. M. Generalova, "Convergence of Technologies in Education: New Determinant of the Society Development," Lecture Notes in Networks and Systems, vol. 155. Springer Science and Business Media Deutschland GmbH, Volgograd State University, Volgograd, Russian Federation, pp. 619-624, 2021, doi: 10.1007/978-3-030-59126-7_69.

R. F. DeMara, T. Tian, and W. Howard, "Engineering assessment strata: A layered approach to evaluation spanning Bloom's taxonomy of learning," Education and Information Technologies, vol. 24, no. 2, pp. 1147-1171, 2019, doi: 10.1007/s10639-018-9812-5.

J. Q. Dawson, M. Allen, A. Campbell, and A. Valair, "Designing an Introductory Programming Course to Improve Non-Majors' Experiences," Proceedings of the 49th ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, Baltimore, Maryland, USA, pp. 26-31, 2018, doi: 10.1145/3159450.3159548.

A. Mohamed, "Designing a CS1 Programming Course for a Mixed-Ability Class," Proceedings of the Western Canadian Conference on Computing Education. Association for Computing Machinery, Calgary, AB, Canada, p. Article 8, 2019, doi: 10.1145/3314994.3325084.

H.-C. Hung, I.-F. Liu, C.-T. Liang, and Y.-S. Su, "Applying Educational Data Mining to Explore Students' Learning Patterns in the Flipped Learning Approach for Coding Education," Symmetry, vol. 12, no. 2, p. 213, 2020, [Online]. Available: https://www.mdpi.com/2073-8994/12/2/213.

J. S. Saltz and I. Shamshurin, "Exploring pair programming beyond computer science: a case study in its use in data science/data engineering," International Journal of Higher Education and Sustainability, vol. 2, no. 4, pp. 265-278, 2019, doi: 10.1504/IJHES.2019.103360.

T. Ketenci, B. Calandra, L. Margulieux, and J. Cohen, "The Relationship Between Learner Characteristics and Student Outcomes in a Middle School Computing Course: An Exploratory Analysis Using Structural Equation Modeling," Journal of Research on Technology in Education, vol. 51, no. 1, pp. 63-76, 2019, doi: 10.1080/15391523.2018.1553024.

A. Alammary, "Blended learning models for introductory programming courses: A systematic review," PloS one, vol. 14, no. 9, pp. 1-26, 2019, doi: 10.1371/journal.pone.0221765.

P. Atzeni, F. Bugiotti, L. Cabibbo, and R. Torlone, "Data modeling in the NoSQL world," Computer Standards & Interfaces, vol. 67, p. Article 103149, 2020, doi: https://doi.org/10.1016/j.csi.2016.10.003.

A. I. Sanka, M. H. Chowdhury, and R. C. C. Cheung, "Efficient High-Performance FPGA-Redis Hybrid NoSQL Caching System for Blockchain Scalability," Computer Communications, vol. 169, pp. 81-91, 2021, doi: 10.1016/j.comcom.2021.01.017.

M. T. í–zsu and P. Valduriez, "NoSQL, NewSQL, and Polystores," in Principles of Distributed Database Systems, M. T. í–zsu and P. Valduriez, Eds. Cham: Springer International Publishing, 2020, pp. 519-557.

I. Astrova, A. Koschel, N. Wellermann, and P. Klostermeyer, "Performance Benchmarking of NewSQL Databases with Yahoo Cloud Serving Benchmark," vol. 1289. Springer Science and Business Media Deutschland GmbH, Department of Software Science, School of IT, Tallinn University of Technology, Akadeemia Tee 21, Tallinn, 12618, Estonia, pp. 271-281, 2021, doi: 10.1007/978-3-030-63089-8_17.

R. R. Asaad, H. B. Ahmad, and R. I. Ali, "A Review: Big Data Technologies with Hadoop Distributed Filesystem and Implementing M/R," Academic Journal of Nawroz University, vol. 9, no. 1, 2020, [Online]. Available: http://journals.nawroz.edu.krd/index.php/ajnu/article/view/530.

B. Elghadyry, F. Ouardi, and S. Verel, "Composition of weighted finite transducers in MapReduce," Journal of Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-020-00397-4.

G. Cheng, S. Ying, B. Wang, and Y. Li, "Efficient Performance Prediction for Apache Spark," Journal of Parallel and Distributed Computing, vol. 149, pp. 40-51, 2021, doi: 10.1016/j.jpdc.2020.10.010.

D. Lunga, J. Gerrand, L. Yang, C. Layton, and R. Stewart, "Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 271-283, 2020, doi: 10.1109/JSTARS.2019.2959707.

J. I. Requeno, J. Merseguer, S. Bernardi, D. Perez-Palacin, G. Giotis, and V. Papanikolaou, "Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study," Information Systems Frontiers, vol. 21, no. 1, pp. 67-85, 2019, doi: 10.1007/s10796-018-9851-x.

A. Muhammad and M. Aleem, "A3-Storm: topology-, traffic-, and resource-aware storm scheduler for heterogeneous clusters," Journal of Supercomputing, vol. 77, no. 2, pp. 1059-1093, 2021, doi: 10.1007/s11227-020-03289-9.

A. Katiyar et al., "Timestamp Anomaly Detection Using IBM Watson IoT Platform," 2020, pp. 771-782.

K. Khalil et al., "Cognitive Computing for Human-Machine Interaction: An IBM Watson Implementation," vol. 1201 AISC. Springer, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan, pp. 400-406, 2021, doi: 10.1007/978-3-030-51041-1_53.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).