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

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Abstract

The application of learning analytics in an online learning environment is increasing among researchers in educational fields because it can assist in providing standard and measurable decision making about student success. In this regard, there is a need for the online learning society and practitioners to be informed about how learning analytics is applied in the online learning environment. Therefore, this systematic review article aims to offer the reader essential details regarding the practical usage of learning analytics techniques in online learning environments to improve the quality of teaching and learning practices. The focal point of this review is threefold: to ascertain the focus of learning analytics research in online learning environments and its significant results, to identify the types of student-related analytics data in online learning environments and the issues related to analytics data, and to inform about the extent of interventions that have been applied in the learning analytics context, if any. Four procedures suggested by PRISMA were applied when conducting a systematic review. A total of 34 articles were chosen according to the review selection guideline by searching through online databases, including ACM Digital Library, LearnTechLib, ERIC, International Forum of Educational Technology & Society, ScienceDirect, Web of Science, Scopus, Society for Learning Analytics Research, and SpringerLink. The search keywords used were learning analytics data and online learning, learning analytics data and learning analytics intervention. The results were analysed based on the three focuses mentioned earlier and were interpreted accordingly. It is found that the applied focus of learning analytics is more oriented towards monitoring/analysis and prediction/intervention, and the commonly used types of student-analytics data are learning behaviour data and learning level data. Additionally, the intervention is still in the process of being developed. Lastly, the directions for future studies and limitations are also provided so as to further develop this emerging research area, and the findings can be a good reference point for other researchers.

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References

  • 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 

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Funding

The authors would like to thank the Ministry of Higher Education (MOHE) for their support in making this project possible. This research was supported by Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2020/SSI0/UTM/02/11).

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  1. Language Academy, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Johor, Malaysia

    Si Na Kew

  2. School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Johor, Malaysia

    Zaidatun Tasir

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Kew, S.N., Tasir, Z. Learning Analytics in Online Learning Environment: A Systematic Review on the Focuses and the Types of Student-Related Analytics Data. Tech Know Learn 27, 405–427 (2022). https://doi.org/10.1007/s10758-021-09541-2

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