Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019

References

• Akçapınar, G. (2015). How automated feedback through text mining changes plagiaristic behavior in online assignments. Computers & Education, 87, 123–130. https://doi.org/10.1016/j.compedu.2015.04.007
   Web of Science ®Google Scholar
• Alario-Hoyos, C., Muñoz-Merino, P. J., Pérez-Sanagustín, M., Delgado Kloos, C., & Parada, G. H. A. (2016). Who are the top contributors in a MOOC? Relating participants’ performance and contributions. Journal of Computer Assisted Learning, 32(3), 232–243. https://doi.org/10.1111/jcal.12127
   Web of Science ®Google Scholar
• Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. https://doi.org/10.1016/j.tele.2019.01.007
   Web of Science ®Google Scholar
• AlJarrah, A., Thomas, M. K., & Shehab, M. (2018). Investigating temporal access in a flipped classroom: Procrastination persists. International Journal of Educational Technology in Higher Education, 15(1), 1. https://doi.org/10.1186/s41239-017-0083-9
   Google Scholar
• Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009 : A review and future visions. Journal of Educational Data Mining, 1(1), 3–16. https://doi.org/10.1109/ASE.2003.1240314
   Google Scholar
• Cechinel, C., Ochoa, X., Lemos dos Santos, H., Carvalho Nunes, J. B., Rodés, V., & Marques Queiroga, E. (2020). Mapping learning analytics initiatives in Latin America. British Journal of Educational Technology: Journal of the Council for Educational Technology, 51(4), 892–914. https://doi.org/10.1111/bjet.12941
   Web of Science ®Google Scholar
• Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating behavior in computer-based Learning environments to predict performance: A case study in moodle. Frontiers in Psychology, 8, 1403. https://doi.org/10.3389/fpsyg.2017.01403
   PubMed Web of Science ®Google Scholar
• Cheng, M.-T., Rosenheck, L., Lin, C.-Y., & Klopfer, E. (2017). Analyzing gameplay data to inform feedback loops in the Radix Endeavor. Computers & Education, 111, 60–73. https://doi.org/10.1016/j.compedu.2017.03.015
   Web of Science ®Google Scholar
• Conde, MÁ, García-Peñalvo, F. J., Gómez-Aguilar, D., & Therón, R. (2015). Exploring software engineering subjects by using visual learning analytics techniques. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 10(4), 242–252. https://doi.org/10.1109/RITA.2015.2486378
   Google Scholar
• Conijn, R., Van den Beemt, A., & Cuijpers, P. (2018). Predicting student performance in a blended MOOC. Journal of Computer Assisted Learning, 34(5), 615–628. https://doi.org/10.1111/jcal.12270
   Web of Science ®Google Scholar
• Cooper, H. M. (1988). Organizing knowledge syntheses: A taxonomy of literature reviews. Knowledge in Society, 1(1), 104–126. https://doi.org/10.1007/BF03177550
   Google Scholar
• Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, 231–240. https://doi.org/10.1145/2567574.2567585
   Google Scholar
• Dormezil, S., Khoshgoftaar, T., & Robinson-Bryant, F. (2020). Differentiating between educational data mining and learning analytics: A bibliometric approach. CEUR Workshop Proceedings, 2592(May), 17–22.
   Google Scholar
• Du, X., Yang, J., Hung, J. L., & Shelton, B. (2020). Educational data mining: A systematic review of research and emerging trends. Information Discovery and Delivery, 48(4), 225–236. https://doi.org/10.1108/IDD-09-2019-0070
   Web of Science ®Google Scholar
• Du, X., Yang, J., Shelton, B. E., Hung, J. L., & Zhang, M. (2021). A systematic meta-review and analysis of learning analytics research. Behaviour and Information Technology, 40(1), 49–62. https://doi.org/10.1080/0144929X.2019.1669712
   Web of Science ®Google Scholar
• ElSayed, A. A., Caeiro-Rodríguez, M., MikicFonte, F. A., & Llamas-Nistal, M. (2019). Research in learning analytics and educational data mining to measure self-regulated learning: A systematic review. World Conference on Mobile and Contextual Learning, 46–53.
   Google Scholar
• Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. ACM International Conference Proceeding Series, 56–65. https://doi.org/10.1145/3027385.3027396
   Google Scholar
• Fu, Q. K., & Hwang, G. J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers and Education, 119(July 2017), 129–143. https://doi.org/10.1016/j.compedu.2018.01.004
   Google Scholar
• Gasevic, D., Tsai, Y. S., Dawson, S., & Pardo, A. (2019). How do we start? An approach to learning analytics adoption in higher education. The International Journal of Information and Learning Technology, 36(4), 342–353. https://doi.org/10.1108/IJILT-02-2019-0024
   Web of Science ®Google Scholar
• Gelan, A., Fastré, G., Verjans, M., Martin, N., Janssenswillen, G., Creemers, M., Lieben, J., Depaire, B., & Thomas, M. (2018). Affordances and limitations of learning analytics for computer-assisted language learning: A case study of the VITAL project. Computer Assisted Language Learning, 31(3), 294–319. https://doi.org/10.1080/09588221.2017.1418382
   Web of Science ®Google Scholar
• Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research? British Journal of Educational Technology: Journal of the Council for Educational Technology, 50(3), 956–971. https://doi.org/10.1111/bjet.12770
   Web of Science ®Google Scholar
• Hwang, G. J., Chu, H. C., & Yin, C. (2017). Objectives, methodologies and research issues of learning analytics. Interactive Learning Environments, 25(2), 143–146. https://doi.org/10.1080/10494820.2017.1287338
   Web of Science ®Google Scholar
• Kitchenham, B. (2004). Procedures for performing systematic reviews, Keele, UK, Keele University, 33(2004), 1–26.
   Google Scholar
• Knobbout, J., & van der Stappen, E. (2020). Where is the learning in learning analytics? A systematic literature review on the operationalization of learning-related constructs in the evaluation of learning analytics interventions. IEEE Transactions on Learning Technologies, 13(3), 631–645. https://doi.org/10.1109/TLT.2020.2999970
   Web of Science ®Google Scholar
• Lee, J. E., & Recker, M. (2018). What do studies of learning analytics reveal about learning and instruction? A systematic literature review. Learning, Design, and Technology, 1–37. https://doi.org/10.1007/978-3-319-17727-4_116-1
   Google Scholar
• Liñán, L. C., & Pérez, ÁAJ. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98–112. https://doi.org/10.7238/rusc.v12i3.2515
   Google Scholar
• Manzanares, M. C. S., Sánchez, R. M., Osorio, C. I. G., & Díez-Pastor, J. F. (2017). How Do B-learning and learning patterns influence learning outcomes? Frontiers in Psychology, 8, 745. https://doi.org/10.3389/fpsyg.2017.00745
   PubMed Web of Science ®Google Scholar
• Marcu, D., & Danubianu, M. (2019). Learning analytics or educational data mining? This is the question. Brain-Broad Research in Artificial Intelligence and Neuroscience, 10(2), 1–14.
   Google Scholar
• Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., … Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6), 1–18. https://doi.org/10.14742/ajet.3058
   Web of Science ®Google Scholar
• Moissa, B., Gasparini, I., & Kemczinski, A. (2015). A systematic Mapping on the learning analytics field and Its analysis in the massive open online courses context. International Journal of Distance Education Technologies (IJDET), 13(3), 1–24. https://doi.org/10.4018/IJDET.2015070101
   Web of Science ®Google Scholar
• Munk, M., Drlik, M., Benko, L. U., & Reichel, J. (2017). Quantitative and qualitative evaluation of sequence patterns found by application of different educational data preprocessing techniques. IEEE Access, 5, 8989–9004. https://doi.org/10.1109/ACCESS.2017.2706302
   Web of Science ®Google Scholar
• Nawang, H., Makhtar, M., & Shamsudin, S. N. W. (2017). Classification model and analysis on students’ performance. Journal of Fundamental and Applied Sciences, 9(6S), 869–885. https://doi.org/10.4314/jfas.v9i6s.65
   Google Scholar
• Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systemic literature review of empirical evidence. Educational Technology and Society, 17(4), 49–64.
   Web of Science ®Google Scholar
• Pardo, A., Han, F., & Ellis, R. A. (2016). Combining University Student Self-regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance, (January 2017). https://doi.org/10.1109/TLT.2016.2639508
   Google Scholar
• Rabin, E., Kalman, Y. M., & Kalz, M. (2019). An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. International Journal of Educational Technology in Higher Education, 16(1), 14. https://doi.org/10.1186/s41239-019-0144-3
   Google Scholar
• Reich, J. (2015). Education research. Rebooting MOOC research. Science, 347(6217), 34–35. https://doi.org/10.1126/science.1261627
   PubMed Web of Science ®Google Scholar
• Reisman, A. (2005). Transfer of technologies: A cross-disciplinary taxonomy. Omega, 33(3), 189–202. https://doi.org/10.1016/j.omega.2004.04.004
   Web of Science ®Google Scholar
• Rienties, B., Tempelaar, D., Nguyen, Q., & Littlejohn, A. (2019). Unpacking the intertemporal impact of self-regulation in a blended mathematics environment. Computers in Human Behavior, 100(July), 345–357. https://doi.org/10.1016/j.chb.2019.07.007
   Google Scholar
• Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational Data Mining: a review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701–1717. https://doi.org/10.1016/j.tele.2018.04.015
   Web of Science ®Google Scholar
• Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews: A Publication of the IEEE Systems, Man, and Cybernetics Society, 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532
   Web of Science ®Google Scholar
• Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIRES Data Mining and Knowledge Discovery, 10(3), 3. https://doi.org/10.1002/widm.1355
   Web of Science ®Google Scholar
• Şahin, M., & Yurdugül, H. (2019). Educational data mining and learning analytics: Past, present and future Eğitsel Veri Madenciliği ve Öğrenme Analitikleri: Dünü, Bugünü ve Geleceği. Bartın University Journal of Faculty of Education, 9(1), 121–131. https://doi.org/10.14686/buefad.606077
   Google Scholar
• Salloum, S. A., Alshurideh, M., Elnagar, A., & Shaalan, K. (2020). Mining in educational data: Review and future directions. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), 92–102. https://doi.org/10.1007/978-3-030-44289-7_9
   Google Scholar
• Siemens, G., & Baker, R. S. J. D. (2012). Learning analytics and educational data mining: Towards communication and collaboration. ACM International Conference Proceeding Series, 252–254. https://doi.org/10.1145/2330601.2330661
   Google Scholar
• Sin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics–a literature review. ICTACT Journal on Soft Computing, 5(4), 1035–1049.
   Google Scholar
• Tempelaar, D., Rienties, B., & Nguyen, Q. (2018). A multi-modal study into students’ timing and learning regulation: Time is ticking. Interactive Technology and Smart Education, https://doi.org/10.1108/itse-02-2018-0015
   Google Scholar
• Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
   Web of Science ®Google Scholar
• Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991–1007. https://doi.org/10.1080/13562517.2017.1332026
   Web of Science ®Google Scholar
• Xia, L., & Zhong, B. (2018). A systematic review on teaching and learning robotics content knowledge in K-12. Computers and Education, 127(122), 267–282. https://doi.org/10.1016/j.compedu.2018.09.007
   Google Scholar
• Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers and Education, 140(July 2018), 103599. https://doi.org/10.1016/j.compedu.2019.103599
   Google Scholar
• Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119–129. https://doi.org/10.1016/j.chb.2015.12.007
   Web of Science ®Google Scholar
• Yang, C. L., Huang, C. Y., Kao, Y. S., & Tasi, Y. L. (2017). Disaster recovery site evaluations and selections for information systems of academic big data. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 4553–4589. https://doi.org/10.12973/eurasia.2017.00951a
   Web of Science ®Google Scholar