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Table 10 Primary studies references

From: Big data in education: a state of the art, limitations, and future research directions

R-ID 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.:
R2 Lia, Y., & Zhaia, X. (2018). Review and Prospect of Modern Education using Big Data. Procedia Computer Science, 129 (3), 341–347.:
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.:
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.:
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.:
R6 Leo Willyanto Santoso, & Yulia. (2017). Data Warehouse with Big Data Technology for Higher Education. Procedia Computer Science, 124 (1), 93–99.:
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.:
R8 Logica, B., & Magdalena, R. (2015). Using Big Data in the Academic Environment. Procedia Economics and Finance, 33 (2), 277–286.:
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.
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.
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.
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.
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.:
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.:
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.
R16 Ozgur, C., Kleckner, M., & Li, Y. (2015). Selection of Statistical Software for Solving Big Data Problems. SAGE Open, 5 (2), 59–94.:
R17 Sorensen, L. C. (2018). “Big Data” in Educational Administration: An Application for Predicting School Dropout Risk. Educational Administration Quarterly, 45 (1), 1–93:
R18 Yang, F., & Du, Y. R. (2016). Storytelling in the Age of Big Data. Asia Pacific Media, 26 (2), 148–162.:
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.:
R20 Gupta, D., & Rani, R. (2018). A study of big data evolution and research challenges. Journal of Information Science. 45 (3), 322–340.:
R21 Veletsianos, G., Reich, J., & Pasquini, L. A. (2016). The Life Between Big Data Log Events. AERA Open, 2 (3), 1–45.:
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.
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.:
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.:
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.:
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.:
R28 Sedkaoui, S., & Khelfaoui, M. (2019). Understand, develop and enhance the learning process with big data. Information Discovery and Delivery, 47 (1), 2–16.:
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.:
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.:
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:
R32 Zhang, M. (2015). Internet use that reproduces educational inequalities: from big data. Computers & Education, 86 (1), 212–223. doi:
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.:
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.:
R35 Pardos, Z. A. (2017). Big data in education and the models that love them. Current Opinion in Behavioral Sciences, 18 (2), 107–113.:
R36 Wassan, J. T. (2015). Discovering Big Data Modelling for Educational World. Procedia - Social and Behavioral Sciences, 176, 642–649.:
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.:
R38 Selwyn, N. (2014). Data entry: towards the critical study of digital data and education. Learning, Media and Technology, 40 (1), 64–82. doi:
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.:
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.: