Abstract
In the last few years, there has been a growing interest in learning analytics (LA) in technology-enhanced learning (TEL). LA approaches share a movement from data to analysis to action to learning. The TEL landscape is changing. Learning is increasingly happening in open and networked learning environments, characterized by increasing complexity and fast-paced change. This should be reflected in the conceptualization and development of innovative LA approaches in order to achieve more effective learning experiences. There is a need to provide understanding into how learners learn in these environments and how learners, educators, institutions, and researchers can best support this process. In this chapter, we discuss open learning analytics as an emerging research field that has the potential to deal with the challenges in open and networked environments and present key conceptual and technical ideas toward an open learning analytics ecosystem.
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References
Alexander, S., Berg, A., Clow, D., Dawson, S., Duval, E., et al.(2014), OLA Press Release. Retrieved from http://solaresearch.org/initiatives/ola/.
Benson, T. (2012). Why interoperability is hard. In Principles of Health Interoperability HL7 and SNOMED, Health Information Technology Standards (pp. 21–32). London: Springer.
Bomas, E. (2014). How to give students control of their data. Retrieved from http://www.laceproject.eu/blog/give-students-control-data/.
Brusilovsky, P., & Millan, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web, LNCS 4321 (pp. 3–53). Berlin: Springer.
Cavoukian, A. (2009). Privacy by Design The 7 Foundational Principles. Retrieved from https://www.privacybydesign.ca/content/uploads/2009/08/7foundationalprinciples.pdf.
Chatti, M. A. (2010). The LaaN theory. In Personalization in technology enhanced learning: A social software perspective (pp. 19–42). Aachen: Shaker Verlag.
Chatti, M. A., Dyckhoff, A. L., Thüs, H., & Schroeder, U. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318–331.
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., et al. (2014). Learning analytics: Challenges and future research directions. eleed, Iss.10. (urn:nbn:de:0009-5-40350).
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.
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695.
Cooper, A. (2013). Learning analytics interoperability—A survey of current literature and candidate standards. Retrieved from http://blogs.cetis.ac.uk/adam/wp-content/uploads/sites/23/2013/05/learninganalytics-interoperability-v1p1.pdf.
Cooper, A. (2014a). Open learning analytics network—Summit Europe 2014. Retrieved from http://www.laceproject.eu/open-learning-analytics-network-summit-europe-2014/.
Cooper, A. (2014b). Learning analytics and interoperability—The big picture in brief. Learning Analytics Review, March 2014, ISSN: 2057-7494.
Cooper, A. (2014c). Standards and specifications—Quick reference guide. Retrieved from http://www.laceproject.eu/dpc/standards-specifications-quick-reference-guide/.
Daniel, B., & Butson, R. (2014). Foundations of big data and analytics in higher education. In International Conference on Analytics Driven Solutions: ICAS2014 (pp. 39–47). Academic Conferences.
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 & Knowledge (pp. 231–240). New York: ACM.
Downes, S. (2007). Models for sustainable open educational resources. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 29–44.
Dringus, L. P. (2012). Learning analytics considered harmful. Journal of Asynchronous Learning Networks, 16(3), 87–100.
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. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317.
Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a personal learning environment. In Proceedings of the LAK ’11 Conference on Learning Analytics and Knowledge (pp. 104–109).
Fry, J., Schroeder, R., & den Besten, M. (2009). Open science in e-science: Contingency or policy? Journal of Documentation, 65(1), 6–32.
Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. San Francisco, CA: Elsevier.
Hardjono, T. (Ed.). (2015). User Managed Access (UMA) Profile of OAuth 2.0. Retrieved from http://docs.kantarainitiative.org/uma/draft-uma-core.html
Hilton, J., Wiley, D., Stein, J., & Johnson, A. (2010). The four R’s of openness and ALMS analysis: Frameworks for open educational resources. Open Learning: The Journal of Open and Distance Learning, 25(1), 37–44.
Hoel, T. (2014). Standards and learning analytics—current activities 2014. Retrieved from http://www.laceproject.eu/blog/standards-learning-analytics-current-activity-2014/.
Kay, J., & Kummerfeld, B. (2011). Lifelong learner modeling. In P. J. Durlach & A. M. Lesgold (Eds.), Adaptive technologies for training and education (pp. 140–164). Cambridge: Cambridge University Press.
Koedinger, K. R., Baker, R. S. J. D., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 43–56). Boca Raton, FL: CRC Press.
Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety, application delivery strategies. META Group. Retrieved March 6, 2015, from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-DataVolume-Velocity-and-Variety.pdf
Laney, D. (2012). The importance of ‘Big Data’: A definition. Gartner. Retrieved March 6, 2015, from http://www.gartner.com/resId=2057415.
Liu, B. (2006). Web data mining. Berlin: Springer.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H., (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
Mazza, R. (2009). Introduction to Information Visualization. London: Springer-Verlag.
McNamara, T. (2012). Open Education: Emergence and Identity. Retrieved February 5, 2015, from http://oh-institute.org/external_resources/pub/McNamara-OpenEd_Emergence_Identity-CC-by.pdf.
Moody, D. L. (2003). Measuring the quality of data models: An empirical evaluation of the use of quality metrics in practice. In Proceedings of 11th European Conference on Information Systems, 2003.
OECD. (2007). Giving Knowledge for Free: The Emergence of Open Educational Resources. Report. ISBN-978-92-64-03174-6. Retrieved February 5, 2015, from http://www.oecd.org/edu/ceri/givingknowledgeforfreetheemergenceofopeneducationalresources.htm.
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.
Peters, M. A. (2008). The history and emergent paradigm of open education. In Open education and education for openness (pp. 3–16). Rotterdam: Sense Publishers.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.
Romero, C., and S. Ventura. Educational data mining: A review of the state-of-the-art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6):601–618, 2010.
Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.
Sclater, N. (2014). Examining open learning analytics—report from the Lace Project meeting in Amsterdam. Retrieved from http://www.laceproject.eu/blog/examining-open-learning-analytics-reportlace-project-meeting-amsterdam/.
Siemens, G., & Baker, R. S. J. D. (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., Haythornthwaite, C., Dawson, S., Shum, S. B., Ferguson, R., et al. (2011). Open learning analytics: An integrated & modularized platform. Maidenhead: Open University Press.
Siemens, G.; Long, P.: Penetrating the Fog: Analytics in Learning and Education. In: EDUCAUSE Review, 46(5), September/October 2011.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1509–1528.
Thüs, H., Chatti, M. A., & Schroeder, U. (in review). Context capturing and modeling in open learning environments. International Journal of Artificial Intelligence in Education Society (IJAIED), IOS Press.
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011, February). 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., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133–148.
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.
Wiley, D. (2009). Introduction to Open Education. iTunesU. Lecture conducted from BYU, Provo.
Yuan, L., Powell, S. (2013). MOOCs and open education: Implications for higher education. A white paper. Retrieved February 5, 2015, from http://publications.cetis.ac.uk/2013/667.
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Chatti, M.A., Muslim, A., Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In: Kei Daniel, B. (eds) Big Data and Learning Analytics in Higher Education. Springer, Cham. https://doi.org/10.1007/978-3-319-06520-5_12
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