Abstract
With the increase of online education programs, learning analytics (LA) tools have become a popular addition to many learning management systems (LMS). As a tool for supporting learning in an educational context, LA has generated some controversy among scholars. Therefore, in this text, we aim to provide a theoretical and analytical understanding of the approach and its implications for teaching and learning. To achieve this, we apply McLuhan’s semiotic analysis of media (1988). The “Laws of Media” questions are asked about LA tools: What do they enhance, make obsolete, retrieve, and reverse into. By answering these questions, we outline which practices of teaching and learning are more likely to become common when LA tools are taken into use more widely and which others will be relegated. In the analysis, we point out that LA tools enhance prediction and personalization of learning, while they displace certain teachers’ skills, personal interaction between teachers and students, and qualitative interpretation and assessment of learning. Simultaneously, LA retrieves behaviourist views of learning and urges discussion about data literacy. Taken to the limits, LA reverses its effects and becomes a tool for supporting awareness and reflection in teaching and learning. We consider these contributions relevant for understanding and reflecting on the type of pedagogies that LA supports, the implicit values it holds, and the changes it introduces into educational practice.
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References
Beer, D., & Burrows, R. (2013). Popular culture, digital archives and the new social life of data. Theory, Culture & Society, 30(4), 47–71.
Bloxham, S., & Boyd, P. (2012). Accountability in grading student work: Securing academic standards in a twenty-first century quality assurance context. British Educational Research Journal, 38(4), 615–634.
Cleary, T. J., & Zimmerman, B. J. (2004). Self-regulation empowerment program: A school-based program to enhance self-regulated and self-motivated cycles of student learning. Psychology in the Schools, 41(5), 537–550.
Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 134–138). ACM, April 2012.
Coates, H. (2010). Defining and monitoring academic standards in Australian higher education. Higher Education Management and Policy, 22(1), 1–17.
Coffrin, C., Corrin, L., de Barba, P., & Kennedy, G. (2014). Visualizing patterns of student engagement and performance in MOOCs. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 83–92). ACM.
Combs, A. W. (1979). Myths in education: Beliefs that hinder progress and their alternatives. Boston, MA: Allyn and Bacon.
Crick, R. D., Broadfoot, P., & Claxton, G. (2004). Developing an effective lifelong learning inventory: The ELLI project. Assessment in Education: Principles, Policy & Practice, 11(3), 247–272.
Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 231–240). ACM, March 2014.
De Liddo, A., Shum, S. B., Quinto, I., Bachler, M., & Cannavacciuolo, L. (2011). Discourse-centric learning analytics. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 23–33). ACM.
Dietrichson, A. (2013). Beyond clickometry: Analytics for constructivist pedagogies. International Journal on E-Learning, 12(4), 333–351.
Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., et al. (2010). Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning. Procedia Computer Science, 1(2), 2849–2858.
Drachsler, H., & Greller, W. (2012). Confidence in learning analytics. In LAK12: 2nd International Conference on Learning Analytics & Knowledge.
Durall, E., & Gros, B. (2014). Learning analytics as a metacognitive tool. In Proceedings of the 6th International Conference on Computer Supported Education (pp. 380–384).
Durall, E., & Toikkanen, T. (2013). Feeler: Feel good and learn better. A tool for promoting reflection about learning and well-being. In Proceedings of the 3rd Workshop on Awareness and Reflection in Technology-Enhanced Learning (pp. 83–89). CEUR.
Duval, E. (2011). Attention please!: Learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9–17). ACM.
Ferguson, R., & Shum, S. B. (2011). Learning analytics to identify exploratory dialogue within synchronous text chat. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 99–103). ACM.
Gandy, O. (2012). Statistical Surveillance. In D. Lyon, K. Haggerty, & K. Ball (Eds.), Routledge handbook of surveillance studies (pp. 125–132). New York: Routledge.
Graf, S., Ives, C., Rahman, N., & Ferri, A. (2011). AAT: A tool for accessing and analysing students’ behaviour data in learning systems. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 174–179). ACM.
Halford, S., Pope, C., & Weal, M. (2013). Digital futures? Sociological challenges and opportunities in the emergent semantic web. Sociology, 47(1), 173–189.
Hallinan, M. T. (1994). Tracking: From theory to practice. Sociology of Education, 79–84.
Knox, D. (2010). Spies in the house of learning: A typology of surveillance in online learning environments. Edge2010, Memorial University of Newfoundland, St Johns, Newfoundland, Canada.
Kruse, A. N. N. A., & Pongsajapan, R. (2012). Student-centered learning analytics. CNDLS Thought Papers, pp. 1–9.
Land, R., & Bayne, S. (2005). Issues of surveillance and disciplinary power in online learning environments. In R. Land & S. Bayne (Eds.), Education in cyberspace (pp. 165–178). London: Routledge.
Leinonen, T. (2012). Towards p2p learning: What media and whose peer? In A. Botero, A. Paterson, J. Saad-Sulonen (Eds.) Towards peer-production in public services: Cases from Finland (pp. 51–59). Helsinki: Aalto University Publications/Croosover 15.
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459.
Manovich, L. (2011). Trending: The promises and the challenges of big social data. In M. Gold (Ed.), Debates in the digital humanities (pp. 460–475). Minneapolis, MN: University of Minnesota Press.
Mazza, R., & Dimitrova, V. (2004). Visualising student tracking data to support instructors in web-based distance education. In Proceedings of the 13th International World Wide Web conference on Alternate track papers & posters (pp. 154–161). ACM.
McLuhan, M. (1964). Understanding media. The extensions of man. London: Sphere Books.
McLuhan, M., & McLuhan, E. (1988). Laws of media: The new science (Vol. 1). Toronto: University of Toronto Press.
Monahan, T. (2010). The future of security? Surveillance operations at homeland security fusion centers. Social Justice, 37(2–3), 84–98.
Rosenzweig, P. (2012). Whither privacy? Surveillance & Society, 10(3/4), 344–347.
Rust, C., O’Donovan, B., & Price, M. (2005). A social constructivist assessment process model: how the research literature shows us this could be best practice. Assessment & Evaluation in Higher Education, 30(3), 231–240.
Selwyn, N. (2014). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology, (ahead-of-print), 1–19.
Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3–26.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30.
Skinner, B. F. (1965). Review lecture: The technology of teaching. Proceedings of the Royal Society of London, Series B: Biological Sciences, 427–443.
Slade, S., & Prinsloo, P. (2013). Learning analytics ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.
Society for Learning Analytics. (2013). Retrieved March 30, 2015 from http://www.solaresearch.org/mission/about/
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2), 177–193.
Taylor, E. (2013). Surveillance schools: Security, discipline and control in contemporary education. London: Palgrave Macmillan.
van Harmelen, M., & Workman, D. (2012). Analytics for learning and teaching. CETIS Analytics Series, 1(3).
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 44–53). ACM.
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499–1514.
Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133–148.
Wesley, D. (2002). A critical analysis on the evolution of e-learning. International Journal on E-learning, 1(4), 41–48.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, 329–339.
Zimmerman, B. J. (2000). Attaining self-regulation: A social-cognitive perspective. In M. Boekaerts, P. Pintrich, & M. Seidner (Eds.), Self-regulation: Theory, research, and applications (pp. 13–39). Orlando, FL: Academic Press.
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Durall Gazulla, E., Leinonen, T. (2016). Why Do We Want Data for Learning? Learning Analytics and the Laws of Media. In: Gros, B., Kinshuk, ., Maina, M. (eds) The Future of Ubiquitous Learning. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47724-3_4
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