Table 10 Primary studies references

R1

Cantabella, M., Martínez-España, R., Ayuso, B., Yáñez, J. A., & Muñoz, A. (2019). Analysis of student behavior in learning management systems through a Big Data framework. Future Generation Computer Systems, 90 (2), 262–272.:https://doi.org/10.1016/j.future.2018.08.003

R2

Lia, Y., & Zhaia, X. (2018). Review and Prospect of Modern Education using Big Data. Procedia Computer Science, 129 (3), 341–347.: https://doi.org/10.1016/j.procs.2018.03.085

R3

Elia, G., Solazzo, G., Lorenzo, G., & Passiante, G. (2018). Assessing learners’ satisfaction in collaborative online courses through a big data approach. Computers in Human Behavior. 92, 589–599.:https://doi.org/10.1016/j.chb.2018.04.033

R4

Coccoli, M., Maresca, P., & Stanganelli, L. (2017). The role of big data and cognitive computing in the learning process. Journal of Visual Languages & Computing, 38, 97–103.:https://doi.org/10.1016/j.jvlc.2016.03.002

R5

Sledgianowski, D., Gomaa, M., & Tan, C. (2017). Toward integration of Big Data, technology and information systems competencies into the accounting curriculum. Journal of Accounting Education, 38 (1), 8193.:https://doi.org/10.1016/j.jaccedu.2016.12.008

R6

Leo Willyanto Santoso, & Yulia. (2017). Data Warehouse with Big Data Technology for Higher Education. Procedia Computer Science, 124 (1), 93–99.: https://doi.org/10.1016/j.procs.2017.12.134

R7

Ramos, T. G., Machado, J. C. F., & Cordeiro, B. P. V. (2015). Primary Education Evaluation in Brazil Using Big Data and Cluster Analysis. Procedia Computer Science, 55 (1), 1031–1039.:https://doi.org/10.1016/j.procs.2015.07.061

R8

Logica, B., & Magdalena, R. (2015). Using Big Data in the Academic Environment. Procedia Economics and Finance, 33 (2), 277–286.:https://doi.org/10.1016/s2212-5671(15)01712-8

R9

Qiu, R. G., Huang, Z., & Patel, I. C. (2015, June). A big data approach to assessing the US higher education service. In 2015 12th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1–6). New York: IEEE. https://doi.org/10.1109/ICSSSM.2015.7170149

R10

Nelson, M., & Pouchard, L. (2017). A pilot “big data” education modular curriculum for engineering graduate education: Development and implementation. Paper presented at the Frontiers in Education Conference (FIE), Indianapolis, USA (pp. 1–5). United States: IEEE. https://doi.org/10.1109/FIE.2017.8190688

R11

Hirashima, T., Supianto, A. A., & Hayashi, Y. (2017, September). Modelbased approach for educational big data analysis of learners thinking with process data. In 2017 International Workshop on Big Data and Information Security (IWBIS) (pp. 11–16). San Diego: IEEE. https://doi.org/10.1177/0165551518789880

R12

Roy, S., & Singh, S. N. (2017). Emerging trends in applications of big data in data mining and learning analytics. In 2017 7th Conference Cloud Computing, Data Science & Engineering-Confluence (pp. -198). New York: IEEE. https://doi.org/10.1109/confluence.2017.7943148

R13

Ong, V. K. (2015). Big Data and Its Research Implications for Higher Education: Cases from UK Higher Education Institutions. Paper presented at the 2015 IIAI 4th International Confress on Advanced Applied Informatics (pp. 487–491). IEEE.: https://doi.org/10.1109/IIAI-AAI.2015.178

R14

Su, Y. S., Ding, T. J., Lue, J. H., Lai, C. F., & Su, C. N. (2017, May). Applying big data analysis technique to students’ learning behavior and learning resource recommendation in a MOOCs course. In 2017 International conference on applied system innovation (ICASI) (pp. 1229–1230). IEEE.: https://doi.org/10.1109/ICASI.2017.7988114

R15

Muthukrishnan, S. M., & Yasin, N. B. M. (2018). Big Data Framework for Students’ Academic. Paper presented at the Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia (pp. 376–382). USA: IEEE. https://doi.org/10.1109/ISCAIE.2018.8405502

R16

Ozgur, C., Kleckner, M., & Li, Y. (2015). Selection of Statistical Software for Solving Big Data Problems. SAGE Open, 5 (2), 59–94.:https://doi.org/10.1177/2158244015584379

R17

Sorensen, L. C. (2018). “Big Data” in Educational Administration: An Application for Predicting School Dropout Risk. Educational Administration Quarterly, 45 (1), 1–93:https://doi.org/10.1177/0013161x18799439

R18

Yang, F., & Du, Y. R. (2016). Storytelling in the Age of Big Data. Asia Pacific Media, 26 (2), 148–162.:https://doi.org/10.1177/1326365x16673168

R19

Nie, M., Yang, L., Sun, J., Su, H., Xia, H., Lian, D., & Yan, K. (2018). Advanced forecasting of career choices for college students based on campus big data. Frontiers of Computer Science, 12 (3), 494–503.:https://doi.org/10.1007/s11704-017-6498-6

R20

Gupta, D., & Rani, R. (2018). A study of big data evolution and research challenges. Journal of Information Science. 45 (3), 322–340.:https://doi.org/10.1177/0165551518789880

R21

Veletsianos, G., Reich, J., & Pasquini, L. A. (2016). The Life Between Big Data Log Events. AERA Open, 2 (3), 1–45.:https://doi.org/10.1177/2332858416657002

R22

Martínez-Abad, F., Gamazo, A., & Rodríguez-Conde, M. J. (2018). Big Data in Education. Paper presented at the Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality - TEEM’18, Salamanca, Spain (pp. 145–150). New York: ACM. https://doi.org/10.1145/3284179.3284206

R23

Buffum, P. S., Martinez-Arocho, A. G., Frankosky, M. H., Rodriguez, F. J., Wiebe, E. N., & Boyer, K. E. (2014, March). CS principles goes to middle school: learning how to teach Big Data. In Proceedings of the 45th ACM technical Computer science education (pp. 151–156). ACM.:https://doi.org/10.1145/2538862.2538949

R24

Dinter, B., Jaekel, T., Kollwitz, C., & Wache, H. (2017). Teaching Big Data Management – An Active Learning Approach for Higher Education. Paper presented at the Proceedings of the Pre-ICIS 2017 SIGDSA. (pp. 1–17). AISeL.

R25

Chaurasia, S. S., & Frieda Rosin, A. (2017). From Big Data to Big Impact: analytics for teaching and learning in higher education. Industrial and Commercial Training, 49 (7), 321–328.:https://doi.org/10.1108/ict-10-2016-0069

R26

Chaurasia, S. S., Kodwani, D., Lachhwani, H., & Ketkar, M. A. (2018). Big academic and learning analytics. International Journal of Management, 32 (6), 1099–1117.:https://doi.org/10.1108/ijem-08-2017-0199

R27

Dubey, R., & Gunasekaran, A. (2015). Education and training for successful career in Big Data and Business Analytics. Industrial and Commercial Training, 47 (4), 174–181.:https://doi.org/10.1108/ict-08-2014-0059

R28

Sedkaoui, S., & Khelfaoui, M. (2019). Understand, develop and enhance the learning process with big data. Information Discovery and Delivery, 47 (1), 2–16.:https://doi.org/10.1108/idd-09-2018-0043

R29

Sooriamurthi, R. (2018, July). Introducing big data analytics in high school and college. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (pp. 373374). ACM.: https://doi.org/10.1145/3197091.3205834

R30

Petrova-Antonova, D., Georgieva, O., & Ilieva, S. (2017, June). Modelling of Educational Data Following Big Data Value Chain. In Proceedings of the 18th International Conference on Computer Systems and Technologies (pp. 88–95). ACM.:https://doi.org/10.1145/3134302.3134335

R31

Oi, M., Yamada, M., Okubo, F., Shimada, A., & Ogata, H. (2017). Reproducibility of findings from educational big data. Paper presented at the Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 536–537). ACM: https://doi.org/10.1145/3027385.3029445

R32

Zhang, M. (2015). Internet use that reproduces educational inequalities: from big data. Computers & Education, 86 (1), 212–223. doi:https://doi.org/10.1016/j.compedu.2015.08.007

R33

Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior, 80 (1), 179–196.:https://doi.org/10.1016/j.chb.2017.11.011

R34

Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92 (1), 578–588.:https://doi.org/10.1016/j.chb.2018.07.002

R35

Pardos, Z. A. (2017). Big data in education and the models that love them. Current Opinion in Behavioral Sciences, 18 (2), 107–113.:https://doi.org/10.1016/j.cobeha.2017.11.006

R36

Wassan, J. T. (2015). Discovering Big Data Modelling for Educational World. Procedia - Social and Behavioral Sciences, 176, 642–649.:https://doi.org/10.1016/j.sbspro.2015.01.522

R37

Dessì, D., Fenu, G., Marras, M., & Reforgiato Recupero, D. (2019). Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections. Computers in Human Behavior, 92 (1), 468–477.:https://doi.org/10.1016/j.chb.2018.03.004

R38

Selwyn, N. (2014). Data entry: towards the critical study of digital data and education. Learning, Media and Technology, 40 (1), 64–82. doi:https://doi.org/10.1080/17439884.2014.921628

R39

Troisi, O., Grimaldi, M., Loia, F., & Maione, G. (2018). Big data and sentiment analysis to highlight decision behaviours: a case study for student population. Behaviour & Information Technology, 37 (11), 1111–1128.:https://doi.org/10.1080/0144929x.2018.1502355

R40

Liang, J., Yang, J., Wu, Y., Li, C., & Zheng, L. (2016). Big Data Application in Education: Dropout Prediction in Edx MOOCs. Paper presented at the 2016 IEEE Second International Conference on Multimedia Big Data (BigMM) (pp. 440–443). IEEE.: https://doi.org/10.1109/BigMM.2016.70