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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.: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