Educational Data Mining and Learning Analytics: Past, Present and Future

Year 2020, , 121 - 131, 05.02.2020

Muhittin Şahin , Halil Yurdugül

https://doi.org/10.14686/buefad.606077

Cited By: 17

Abstract

Educational data mining and
learning analytics have recently emerged as two important fields aimed at
rendering e-learning environments more effective. Aim of this study seeks first
to reveal the differences between these two fields and then to discuss the
future of these concepts by evaluating how they changed throughout history.
Educational data mining refers to uncovering the patterns hidden in the big
data whilst learning analytics is the use of these patterns to optimize
e-learning environments. One of the purposes of the study is to add to the
literature on the future trends regarding these concepts. In the very near
future, it seems that studies will be performed on EDM and the Industry 4.0 and
one of its application areas, “(Internet of Things-IoT)” and EDM has the
potential to substantially help researchers in discovering the patterns in the
interaction data in the Learning Management Systems and in designing more
effective learning environments. The studies on the future of learning
analytics are categorized in five main headings: personalization of learning
processes, learning design, learning experience design, dashboard design and
the Industry 4.0 applications. 

Keywords

Educational data mining, Learning analytics, Past, present and future of educational data mining and learning analytics

References

• Aleven, V., Sewall, J., Popescu, O., Ringenberg, M., Van Velsen, M., & Demi, S. (2016). Embedding intelligent tutoring systems in MOOCs and e-learning platforms. In International Conference on Intelligent Tutoring Systems (pp. 409-415). Springer, Cham.
• Aleven, V., Sewall, J., Popescu, O., Xhakaj, F., Chand, D., Baker, R., ... & Gasevic, D. (2015). The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs. In International Conference on Artificial Intelligence in Education (pp. 525-528). Springer, Cham.
• Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (267-270). ACM.
• Baker, R. S. J. D. (2010). Data mining for education. International Encyclopedia of Education, 7(3), 112-118.
• Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM| Journal of Educational Data Mining, 1(1), 3-17.
• Baker, R.S. & Inventado, P.S. (2014). Learning analytics from research to practice. Johann Ari Larusson & Brandon White (Eds.). Educational data mining and learning analytics (61-75p.). Springer-Verlag New York.
• Baker, R.S.J.d., & Siemens, G. (2014). Educational data mining and learning analytics. In Sawyer, K. (Ed.) Cambridge Handbook of the Learning Sciences (2nd Edition). 253-274 p.
• Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., ... & Lockyer, L. (2016, April). A conceptual framework linking learning design with learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 329-338). ACM.
• Baneres, D., Caballé, S., & Clarisó, R. (2016). Towards a learning analytics support for intelligent tutoring systems on MOOC platforms. In 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS) (pp. 103-110). IEEE.
• Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
• Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1-57.
• Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4-16.
• Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
• G. Conole, Designing for learning in an open world. Springer Science & Business Media, 2012, vol. 4.
• Greller W, Ebner M, Schön M (2014) Learning analytics: from theory to practice–data support for learning and teaching. In: Computer assisted assessment. Research into e-assessment. Springer International Publishing, pp 79–87
• Hernández‐Leo, D., Martinez‐Maldonado, R., Pardo, A., Muñoz‐Cristóbal, J. A., & Rodríguez‐Triana, M. J. (2019). Analytics for learning design: A layered framework and tools. British Journal of Educational Technology, 50(1), 139-152.
• Horizon Report Preview 2019. Erişim adresi: https://library.educause.edu/-/media/files/library/2019/2/2019horizonreportpreview.pdf
• Huebner, R. A. (2013). A Survey of Educational Data-Mining Research. Research in higher education journal, 19, 1-13
• Ifenthaler, D. (2017). Learning analytics design. The sciences of learning and instructional design: Constructive articulation between communities, 202-211.
• Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2012). NMC Horizon Report: 2011 Edition. The New Media Consortium.
• Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
• LAK 2011 1st International Conference on Learning Analytics and Knowledge Banff, AB, Canada — February 27 - March 01, 2011.
• Lal, P. (2014). Online Tutor 2.0: Methodologies and Case Studies for Successful Learning. Gustavo Alves (Ed.). Designing online learning strategies through analytics (1-15p). IGI Global.
• Larnaca Declaration (2012). The Larnaca Declaration on Learning Design. www.larnacadeclaration.org
• Mangaroska, K., & Giannakos, M. N. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies.
• McKay, T., Miller, K., & Tritz, J. (2012, April). What to do with actionable intelligence: E 2 Coach as an intervention engine. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (88-91). ACM.
• Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., & Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, 703-714.
• Peña-Ayala, A., Cárdenas-Robledo, L. A., & Sossa, H. (2017). A landscape of learning analytics: An exercise to highlight the nature of an emergent field. In Learning analytics: Fundaments, applications, and trends (pp. 65-112). Springer, Cham.
• Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
• Şahin, M. (2018). Design and development of the intervention engine based on learning analytics for e-learning environments (PhD Dissertation). Hacettepe University, Ankara.
• Sanjeev, P., & Zytkow, J. M. (1995). Discovering enrollment knowledge in university databases. In KDD (pp. 246-251).
• Shabani, Zahra, and Mohammad Eshaghian (2014). Decision support system using for learning management systems personalization. American Journal of Systems and Software 2(5), 131-138.
• Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
• Siemens, G., & d Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
• Siemens, G., & Gasevic, D. (2012). Guest Editorial - Learning and knowledge analytics. Educational Technology & Society, 15(3), 1–2.
• Šimić, G., Gašević, D., & Devedžić, V. (2004). Semantic web and intelligent learning management systems. In Workshop on Applications of Semantic Web Technologies for e-Learning.
• Thuraisingham, B. (2014). Data mining: technologies, techniques, tools, and trends. CRC press.
• Tlili, A., Essalmi, F., & Jemni, M., Chang, M. & Kinshuk, (2018). iMoodle: An Intelligent Moodle Based on Learning Analytics.university databases. In KDD (pp. 246–251).
• Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., ... & Zhou, Z. H. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
• Y. Mor and B. Craft (2012). Learning design: reflections upon the current landscape. Research in Learning Technology, 20(1), 85-94.
• Yin, Y., Kaku, I., Tang, J., & Zhu, J. (2011). Data mining: Concepts, methods and applications in management and engineering design. Springer Science & Business Media.