Abstract
Formal and informal learning settings overlap quite often. Since the arrival and consolidation of 24/7 Internet services, contents, authoring tools and free storage, informal settings have become a powerful resource for learning. The combination of both scenarios is a reality that must be tackled: one with regular academic programmes, and the other one with mostly unstructured learning and information unit. However, successful engagement between them is not that common. Regular academic programmes deployed with learning management systems (LMS) keep a track of user activity and user performance. Sometimes these inputs are stored and processed to provide some useful feedback in the form of recommendation or advice to the very same user. However, there is usually no tracking of user activity on social media, external fora or other online services. This lack of awareness about user activity outside the LMS prevents the educational methodology or the pedagogical model from considering all the available information. It also restricts the analysis to just one area: official education. To this extent, MOOCs have become a crucial part of combined educational models that move between formal and informal settings, where they play a key role in the learning path of every user. However, this does not seem enough. The current educational landscape requires disruption to boost the learning-teaching process. We call this disruption transgenic learning. It is based on the user’s behaviour and interactions, along with efficient monitoring and personalised counselling by a tutor. It is focused on improving the user’s awareness of their real academic status and their related performance. In this book chapter, we discuss the specific implementation of a personalised eLearning model for restricted social networks and learning management systems, called LIME, which supports this approach. LIME is focused on massive enrolment and large datasets. We also present a framework and software prototype that implements the model (iLIME). Lastly, we show a successful practical case study that makes use of iLIME integrated with the Sakai CLE LMS. This case study depicts the technical issues, challenges and solutions involved in a successful deployment of the LIME model into a real university scenario.
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References
Aberdour, M. (2007). Open source learning management systems: Emerging open source LMS markets. Epic White Paper.
Apolinario, R. M. (2015). Genetically modified organisms.
Bosch, H., Heinrich, J., Muller, C., Hoferlin, B., Reina, G., Hoferlin, M., Worner, M., & Koch, S. (2009). Innovative filtering techniques and customized analytics tools. In IEEE Symposium on Visual Analytics Science and Technology, 2009 (VAST 2009) (pp. 12–13).
Bry, F., Ebner, M., Pohl, A., Pardo, A., & Taraghi, B. (2014). Interaction in massive courses. Journal of Universal Computer Science, 20.
Burgos, D. (2013). L.I.M.E. A recommendation model for informal and formal learning, engaged. International Journal of Artificial Intelligence and Interactive Multimedia, 2, 79–86.
Burgos, D., Tattersall, C., & Koper, R. (2007). How to represent adaptation in eLearning with IMS learning design. Interactive Learning Environments, 15, 161–170.
Burton, M., Rigby, D., Young, T., & James, S. (2001). Consumer attitudes to genetically modified organisms in food in the UK. European Review of Agricultural Economics, 28(4), 479–498.
Chen, S. Y., & Magoulas, G. D. (2005). Adaptable and adaptive hypermedia systems. Hershey, PA: IRM Press.
Cheney, J., Lindley, S., & P. Wadler. (2013). “A practical theory of language-integrated query”. In Proceedings of the 18th ACM SIGPLAN International Conference on Functional Programming (ICFP ‘13). ACM, New York, NY, USA, 403–416.
Collins, A., & Halverson, R. (2010). The second educational revolution: Rethinking education in the age of technology. Journal of Computer Assisted learning, 26(1), 18–27.
Dabbagh, N., & Kitsantas, A. (2012). Personal learning environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3–8.
De-la-Fuente-Valentín, L., Carrasco, A., Konya, K., & Burgos, D. (2013). Emerging technologies landscape on education: A Review. IJIMAI, 2(3), 55.
Epelboin, Y. (2013). MOOC in Europe. Internal report, UPMC-Sorbonne Université.
Forment, M., Casany-Guerrero, M. J., Conde, M., García, F. J., & Severance, C. (2011). Interoperability for LMS: The missing piece to become the common place for e-learning innovation. International Journal of Knowledge and Learning, 6, 130–141.
Forment, M., Casany-Guerrero, M. J., Mayol, E., Piguillem, J., Galanis, N., García, F. J., & Conde, M. A. (2012). Docs4Learning: Getting google docs to work within the LMS with IMS BLTI. Journal of Universal Computer Science, 18(11).
García, F. J., Conde, M. A., Marc, M., & Casany-Guerrero, M. J. (2011). Opening learning management systems to personal learning environments. Journal of Universal Computer Science, 17(9).
Ghauth, K. I., & Abdullah, N. A. (2010). Learning materials recommendation using good learners’ ratings and content-based filtering. Education Technology Research and Development, 58, 711–727.
Gonzalez, M. A. C., Penalvo, F. J. G., Guerrero, M. J. C., & Forment, M. A. (2009). Adapting LMS architecture to the SOA: An architectural approach. In Internet and Web Applications and Services, 2009 (ICIW ‘09) (pp. 322–327).
Grigalis, T., & Cenys A. (2014). Unsupervised structured data extraction from template-generated web pages. Journal of Universal Computer Science, 20(2).
Günter, S., & Cleenewerk, T. (2010). Design principles for internal domain-specific languages: A pattern catalog illustrated by Ruby. In Proceedings of the 17th Conference on Pattern Languages of Programs.
Holmes, A., & Kellogg, M. (2006). Automating functional tests using Selenium. Agile Conference.
Hunter, P. (2013). Instant Nokogiri. Packt Publishing Ltd.
Kelly, D., & Thorn, K. (2013). Should instructional designers care about the tin can API?. eLearning Magazine, 2013(3).
Kerkiri, T., Manitsaris, A., & Mavridou, A. (2007). Reputation metadata for recommending personalized e-learning resources. In IEEE Computer Society, 2007.
Lenoy, D., Parada, H., Muñoz-Merino, P., Pardo, A., & Delgado, C. (2013). A generic architecture for emotion-based recommender systems in cloud learning environments. Journal of Universal Computer Science, 19(14).
Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing IEEE, 7, 76–80.
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing (2nd ed.).
Malik, S. K., & Rizvi, S. A. M. (2011). Information extraction using web usage mining, web scrapping and semantic annotation. In International Conference on, 2011 Computational Intelligence and Communication Networks (CICN) (pp. 465–469).
Marc M., Mayol E., Casany-Guerrero M.J., Piguillem J., Merriman J., Conde M. A., García F.J., Tebben W., Severance C., “Clustering Projects for eLearning Interoperability”. Journal of Universal Computer Science, vol.18, issue 11, 2012.
Marlin, B. (2003). Modeling user rating profiles for collaborative filtering. In S. Thrun, L. K. Saul, & B. Schölkopf (Eds.), Advances in neural information processing systems (pp. 627–634). Cambridge, MA: MIT Press.
Maximilien, E., Wilkinson, H., Desai, N., & Tai, S. (2007). A domain-specific language for web APIs and services mashups. In Service-Oriented Computing—ICSOC 2007, vol. 4749 (pp. 13–26).
McGreal, R., Kinuthia, W., Marshall, S., & McNamara, T. (2013). Open educational resources: Innovation, research and practice. Vancouver: Common wealth of Learning.
Millis, N. (2006). Genetically modified organisms.
Muhammad, H., & Ierusalimschy, R. (2007). CAPIs in extension and extensible languages. Journal of Universal Computer Science, 13(6).
New Media Consortium, & EDUCAUSE Learning Initiative. (2015). The NMC horizon report: 2015 Higher Education Edition. Austin, TX: The New Media Consortium.
Pardede, J., & Rahayu, W. (2009). SQL/XML hierarchical query performance analysis in an XML-enabled database system. Journal of Universal Computer Science, 15(10).
Paulo, J., & Queirós, R. (2011). Using the learning tools interoperability framework for LMS integration in service oriented architectures. In Conference Proceedings in Technology Enhanced Learning, TECH-EDUCATION ‘11. Springer.
Rocchio, J. J. (1971). Relevance feedback in information retrieval, in the SMART retrieval system. Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice Hall, Inc.
Romero, C., Ventura, S., De Bra, P. D., & Castro, C. D. (2003). Discovering prediction rules in AHA! courses. In 9th International User Modeling Conference, 2003.
Saigaonkar, S., & Rao, M. (2010). XML filtering system based on ontology. In Proc. of the 1st Amrita ACM-W Celebration on Women in Computing in India A2CWiC ’10.
Sielis, G., Mettouris, C., Papadopoulos, G., Tzanavari, A., Dols, R., & Siebers, Q. (2011). A context aware recommender system for creativity support tools. Journal of Universal Computer Science, 17(12).
Wrigley, T. (2009). Rethinking education in the era of globalization. Contesting Neoliberal Education: Public Resistance and Collective Advance, 61–82.
Acknowledgements
This research is partially funded by the Research Institute for Innovation & Technology in Education (UNIR iTED, http://ited.unir.net), the UNESCO Chair on eLearning and the ICDE Chair in Open Educational Resources (http://research.unir.net/unesco), at Universidad Internacional de La Rioja (UNIR, http://www.unir.net).
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Corbi, A., Burgos, D. (2020). How to Integrate Formal and Informal Settings in Massive Open Online Courses Through a Transgenic Learning Approach. In: Burgos, D. (eds) Radical Solutions and Learning Analytics. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-4526-9_11
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