Learning Analytics in Online Learning Environment: A Systematic Review on the Focuses and the Types of Student-Related Analytics Data

• Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550.

  Article  Google Scholar 

• Aguiar, E., Chawla, N. V., Brockman, J., Ambrose, G. A., & Goodrich, V. (2014). Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 103–112).

• Aljohani, N. R., Daud, A., Abbasi, R. A., Alowibdi, J. S., Basheri, M., & Aslam, M. A. (2019). An integrated framework for course adapted student learning analytics dashboard. Computers in Human Behavior, 92, 679–690.

  Article  Google Scholar 

• Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers and Education, 141, 103612.

  Article  Google Scholar 

• Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In S. Buckingham Shum, D. Gasevic, & R. Ferguson (Eds.), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ′12) (pp. 267–270). New York: ACM.

• Baiocchi, R. R. (2019). Exploring data driven youth character education frameworks: A systematic literature review on learning analytics models and participatory design. Estudios Sobre Educacion, 37, 179–198.

  Article  Google Scholar 

• Chai, C. S., Deng, F., Tsai, P. S., Koh, J. H. L., & Tsai, C. C. (2015). Assessing multidimensional students’ perceptions of twenty-first-century learning practices. Asia Pacific Education Review, 16(3), 389–398.

  Article  Google Scholar 

• 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.

  Article  Google Scholar 

• Chen, B., Chang, Y. H., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37, 21–30.

  Article  Google Scholar 

• Choi, S. P., Lam, S. S., Li, K. C., & Wong, B. T. (2018). Learning analytics at low cost: At-risk student prediction with clicker data and systematic proactive interventions. Journal of Educational Technology and Society, 21(2), 273–290.

  Google Scholar 

• Colthorpe, K., Zimbardi, K., Ainscough, L., & Anderson, S. (2015). Know Thy Student! Combining Learning Analytics and Critical Reflections to Increase Understanding of Students’ Self-Regulated Learning in an Authentic Setting. Journal of Learning Analytics, 2(1), 134–155.

  Article  Google Scholar 

• Cooper, A. (2012). What is analytics? Definition and essential characteristics. CETIS Analytics Series, 1(5), 1–10.

  Google Scholar 

• Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In B. Hegarty, J. McDonald, & S. K. Loke (Eds.) Proceedings of the ascilite 2014 conference (pp.629–633). Dunedin, NZ.

• de Quincey, E., Briggs, C., Kyriacou, T., & Waller, R. (2019). Student centred design of a learning analytics system. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 353–362).

• Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.

  Article  Google Scholar 

• Fidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde, M. Á. (2015). Using Learning Analytics to improve teamwork assessment. Computers in Human Behavior, 47, 149–156.

  Article  Google Scholar 

• Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.

  Article  Google Scholar 

• Gasevic, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.

  Article  Google Scholar 

• Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education. Washington, DC: EDUCAUSE Center for Applied Research. Retrieve from http://net.educause.edu/ir/library/pdf/ecar_so/ers/ers0508/EKF0508.pdf.

• Gunn, C. (2014). Defining an agenda for learning analytics. In B. Hegarty, J. McDonald, & S. K. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings of the ascilite 2014 conference (pp.683–687). Dunedin, NZ.

• Haya, P. A., Daems, O., Malzahn, N., Castellanos, J., & Hoppe, H. U. (2015). Analysing content and patterns of interaction for improving the learning design of networked learning environments. British Journal of Educational Technology, 46(2), 300–316.

  Article  Google Scholar 

• Hershkovitz, A., de Baker, R. S. J., Gobert, J., Wixon, M., & Sao Pedro, M. (2013). Discovery with models a case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57(10), 1480–1499.

  Article  Google Scholar 

• Hoppes, C. R. (2015). Defining and differentiating personalized learning, adaptive learning and competency-based. Retrieved from http://www.helixeducation.com/wp-content/uploads/2015/08/Differentiated-Learning-_Helix-Education-White-Paper_Distance-Teaching-and-Learning-2015-.pdf.

• Howell, J. A., Roberts, L. D., & Mancini, V. O. (2018a). Learning analytics messages: Impact of grade, sender, comparative information and message style on student affect and academic resilience. Computers in Human Behavior, 89, 8–15.

  Article  Google Scholar 

• Howell, J. A., Roberts, L. D., Seaman, K., & Gibson, D. C. (2018b). Are we on our way to becoming a “helicopter university”? Academics’ views on learning analytics. Technology, Knowledge and Learning, 23(1), 1–20.

  Article  Google Scholar 

• Ibañez, P., Villalonga, C., & Nuere, L. (2019). Exploring student activity with learning analytics in the digital environments of the Nebrija University. Technology, Knowledge and Learning, 25(4), 769–787.

  Article  Google Scholar 

• Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1–2), 221–240.

  Article  Google Scholar 

• Ji, H., Park, K., Jo, J., & Lim, H. (2015). Mining students activities from a computer supported collaborative learning system based on peer to peer network. Peer-to-Peer Networking and Applications, 9(3), 465–476.

  Article  Google Scholar 

• Jo, I. H., Kim, D., & Yoon, M. (2015). Constructing proxy variables to measure adult learners’ time management strategies in LMS. Educational Technology and Society, 18(3), 214–225.

  Google Scholar 

• Joksimovic, S., Gasevic, D., Loughin, T. M., Kovanovic, V., & Hatala, M. (2015). Learning at distance: Effects of interaction traces on academic achievement. Computers and Education, 87, 204–217.

  Article  Google Scholar 

• Kennedy, G., Ioannou, I., Zhou, Y., Bailey, J., & O’Leary, S. (2013). Mining interactions in immersive learning environments for real-time student feedback. Australasian Journal of Educational Technology, 29(2), 172–183.

  Article  Google Scholar 

• Kew, S. N. & Tasir, Z. (2016). A framework for learning analytics intervention in e-learning. In 2016 Proceedings of Regional Conference on Engineering Education (pp. 24–30) Kuala Lumpur, Malaysia.

• Kew, S. N., & Tasir, Z. (2017a). A systematic review of learning analytics intervention contributing to student success in online learning. IEEE Conference Proceedings of LaTiCE, Hong Kong, 2017, 62–68. https://doi.org/10.1109/LaTiCE.2017.183

  Article  Google Scholar 

• Kew, S. N. & Tasir, Z. (2017). Identifying at-risk students in online learning by analysing learning behaviour: a systematic review. In IEEE Conference Proceedings of Big Data and Analytics (ICBDA), Kuching 2017 (pp. 118–123). doi: https://doi.org/10.1109/ICBDAA.2017.8284117

• Kim, D., Park, Y., Yoon, M., & Jo, I. H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. The Internet and Higher Education, 30, 30–43.

  Article  Google Scholar 

• Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers and Education, 127, 233–251.

  Article  Google Scholar 

• Koç, M. (2005). Implications of learning theories for effective technology integration and pre-service teacher training: A critical literature review. Journal of Turkish Science Education, 2(1), 2–18.

  Google Scholar 

• Land, R., & Bayne, S. (2005). Screen or monitor? Surveillance and disciplinary power in online learning environments. In R. Land & S. Bayne (Eds.), Education in Cyberspace (pp. 165–178). RoutledgeFalmer.

  Google Scholar 

• Lim, L. A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2019). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2019.04.003

  Article  Google Scholar 

• Lin, C. J., & Hwang, G. J. (2018). A learning analytics approach to investigating factors affecting EFL students’ oral performance in a flipped classroom. Journal of Educational Technology and Society, 21(2), 205–219.

  Google Scholar 

• Long, P., & Siemens, G. (2011). Penetrating the fog: analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.

  Google Scholar 

• Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computer in Human Behavior, 47, 90–97.

  Article  Google Scholar 

• Lotsari, E., Verykios, V. S., Panagiotakopoulos, C., & Kalles, D. (2014). A learning analytics methodology for student profiling. In A. Likas, K. Blekas, & D. Kalles (Eds.), 8th Hellenic Conference on AI (pp. 300–312). Springer International Publishing.

  Google Scholar 

• Lowes, S., Lin, P., & Kinghorn, B. (2015). Exploring the link between online behaviours and course performance in asynchronous online high school courses. Journal of Learning Analytics, 2(2), 169–189.

  Article  Google Scholar 

• Lu, O. H., Huang, A. Y., Huang, J. C., Lin, A. J., Ogata, H., & Yang, S. J. (2018). Applying learning analytics for the early prediction of Students’ academic performance in blended learning. Journal of Educational Technology & Society, 21(2), 220–232.

  Google Scholar 

• Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26–34.

  Article  Google Scholar 

• Mangaroska, K., & Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516–534.

  Article  Google Scholar 

• Martin, F., & Whitmer, J. C. (2016). Applying learning analytics to investigate timed release in online learning. Technology, Knowledge and Learning, 21(1), 59–74.

  Article  Google Scholar 

• Matcha, W., Gašević, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245.

  Article  Google Scholar 

• Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., Atkins, D., Barbour, V., Barrowman, N., & Berlin, J. A. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. J. Chin. Integr. Med, 7, 889–896.

  Article  Google Scholar 

• Norris, D. M., & Baer, L. L. (2013). Building organizational capacity for analytics. EDUCAUSE. Retrieved from https://net.educause.edu/ir/library/pdf/PUB9012.pdf

• Pekrun, R. (2014). Emotions and Learning. Belley: International Academy of Education.

  Google Scholar 

• Prasad, D., Totaram, R., & Usagawa, T. (2016). Development of open textbooks learning analytics system. International Review of Research in Open and Distributed Learning: IRRODL, 17(5), 215–234.

  Article  Google Scholar 

• Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Leony, D., & Kloos, C. D. (2015). ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior, 47, 139–148.

  Article  Google Scholar 

• Şahin, M., & Yurdugül, H. (2020). Learners’ needs in online learning environments and third generation learning management systems (LMS 3.0). Technology, Knowledge and Learning https://doi.org/10.1007/s10758-020-09479-x

• Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2016). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30–41.

  Article  Google Scholar 

• Seufert, S., Meier, C., Soellner, M., & Rietsche, R. (2019). A Pedagogical Perspective on Big Data and Learning Analytics: A Conceptual Model for Digital Learning Support. Technology, Knowledge and Learning, 24(4), 599–619.

  Article  Google Scholar 

• Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167.

  Article  Google Scholar 

• Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2017). Towards actionable learning analytics using dispositions. IEEE Transactions on Learning Technologies, 10(1), 6–16.

  Article  Google Scholar 

• van Harmelen, M., & Workman, D. (2012). Analytics for learning and teaching. CETIS Analytics Series, 1(3), 1–41.

  Google Scholar 

• Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2015). Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics. Computers and Education, 90, 80–94.

  Article  Google Scholar 

• West, D., Tasir., Luzeckyj, A., Kew., Toohey, D., Abdullah, Z., Searle, B., Jumaat, N. F., & Price, R. (2018). Learning analytics experience among academics in Australia and Malaysia: A comparison. Australasian Journal of Educational Technology https://doi.org/10.14742/ajet.3836

• Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. In K. E. Arnold, S. Teasley & A. Pardo (Eds.), Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (LAK '14) (pp. 203–211). New York: ACM.

• Wise, A. F., Zhao, Y., & Hausknecht, S. N. (2014). Learning analytics for online discussions: Embedded and extracted approaches. Journal of Learning Analytics, 1(2), 48–71.

  Article  Google Scholar 

• Wise, A. F., Zhao; Y. & Hausknecht, S. N. (2013). Learning analytics for online discussions: A pedagogical model for intervention with embedded and extracted analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (Leuven, Belgium) (pp. 48–56). New York, NY: ACM

• Wong, B. T. M., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7–28.

  Article  Google Scholar 

• Wu, F., Huang, L., & Zou, R. (2015). The design of intervention model and strategy based on the behavior data of learners: A learning analytics perspective. In S. K. S. Cheung, L. Kwok, H. Yang, J. Fong, & R. Kwan (Eds.), International Conference on Hybrid Learning (pp. 294–301). Springer International Publishing.

  Google Scholar 

• Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181.

  Article  Google Scholar 

• Zhang, J. H., Zhang, Y. X., Zou, Q., & Huang, S. (2018). What learning analytics tells us: Group behavior analysis and individual learning diagnosis based on long-term and large-scale data. Journal of Educational Technology and Society, 21(2), 245–258.

  Google Scholar 

Download references