Abstract
Personalisation, user profiling and the use of machine learning techniques from the computer science arena fall under the umbrella of Artificial Intelligence or AI. Rather then going through all the technical details of machine learning and AI we will be looking into the conceptual application of such techniques , as well as the educational undertones of doing so. Personalisation features as a main component in this chapter due to its exceptional and remarkable property of improving a service or a product. We shall be looking into how such a widely employed technique in industry can be similarly applied to education that promises to alleviate and add-value to e-learning as we know them. The main concept behind such a technique is the capturing and representation of the specific user model or profile. This user representation is a living model that evolves over time and requires constant updating to ensure the profile realistically embodies the user or the learner in our case. As we shall investigate in the next sections the user profile is generally generated and trained using the user patterns and trends but also the interests, needs and choices that all indicate something specific about the user in isolation as well as in combination together. In another section we will also take an in-depth analysis of how user profiling can be optimised in the case of education in a similar attempt to encapsulate the specific and characteristic learner profile. We close this chapter with a look at recommender systems and how all the different parts mentioned above come together to the cause of enhancing education and the e-learning medium.
The shoe that fits one person pinches another;
there is no recipe for living
that suits all cases.
Carl Jung
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Postma, O., Brokke, M.: Personalisation in practice: the proven effects of personalisation. J. Database Mark. 9(2), 137142 (2002)
Johnson, J.A.: Freedom and Control. Psychology Today, Sussex, 30 (2011). https://www.psychologytoday.com/blog/cui-bono/201104/freedom-and-control. Cited 21 January 2015
Carmody, D.P., Lewis, M.: Brain activation when hearing ones own and others names. Brain Res. 1116(1), 153158 (2006)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations Item-to-Item Collaborative Filtering, Internet Computing, pp. 76–80 (2003)
Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. Trans. Manag. Inf. Syst. 6(4), 1–15 (2015)
Melinat, P., Kreuzkam, T., Stamer, D.: Information overload: a systematic literature review. In: Johansson, B., Andersson, B., Holmberg, N. (eds.) Perspectives in Business Informatics Research. [S.l.], pp. 72–86. Springer (2014)
Montebello, M.: Metasearch \(+\) Machine Learning \(=\) WWW Information Overload. J. Comput. Inf. (JCI), Canada, 3(1) (1999). ISSN 1201-8511
Educause: Personalized Learning (2016). https://library.educause.edu/topics/teaching-and-learning/personalized-learning. Cited 15 September 2016
Lonn, S., Nixon, A., Morgan, G., VanDenBlink, C., Dahlstrom, E.: Moving the Red Queen Forward: Maturing Learning Analytics Practices, Educause15 (2015)
Siemens, G.: SenseMaking Artefacts, Connectivism (2012). www.connectivism.ca. Cited 12 Nov 2015
Knewton: Pearson and Knewton Team Up to Personalize Math Education, Knewton in the News (2016). https://www.knewton.com/resources/press/pearson-and-knewton-team-up-to-personalize-math-education/. Cited 20 Dec 2016
CogBooks: Using adaptive learning tools An educators perspective (2015). https://www.cogbooks.com/2015/09/15/using-adaptive-learning-tools-an-educators-perspective/. Cited 18 Nov 2016
Lawlor, O.: Metacog releases open ended rubric based machine scoring service (2015). http://www.metacog.com/blog/files/category-assessment.html. Cited 30 June 2016
CogBooks: Improve Student Success and Retention with Adaptive Courseware (2016). https://www.cogbooks.com/2016/02/04/improve-student-success-and-retention-with-adaptive-courseware/. Cited 12 Dec 2016
Reddy, D.M.: U-Pace. University of Wisconsin-Milwaukee. Educase (2014)
MIT: MITx, Free online courses from MIT (2016). https://www.edx.org/school/mitx. Cited 8 Nov 2016
IMS: Learning Measurement for Analytics Whitepaper, IMS Global Learning Consortium, Inc. (2013). https://www.imsglobal.org/sites/default/files/caliper/IMSLearningAnalyticsWP.pdf. Cited 15 Sept 2016
Ined: Institute of Neo Education, iClass Learning Management System (2016). http://iclass.ined.uitm.edu.my/. Cited 9 Feb 2017
OUP: Oxford Universtiy Press (2016). https://www.oupchina.com.hk/elt/events/20160305-iclass-seminar. Cited 12 Dec 2016
Bykau, S., Koutrika, G., Velegrakis, Y.: Coping with the Persistent Cold-start Problem. In: Personalized Access, Profile Management, and Context Awareness in Database, PersDB 2013 (2013)
Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: Scalable collaborative ltering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)
Li L., Tang X.: A solution to the cold-start problem in recommender systems based on social choice theory. In: Lavangnananda K., Phon-Amnuaisuk S., Engchuan W., Chan J. (eds.) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol. 5. Springer, Cham (2016)
Li, L., Zheng, L., Yang, F., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 31683177 (2014)
Burke, R.: Hybrid systems for personalized recommendations. Intelligent Techniques for Web Personalization, p. 133152 (2005)
Chu, W., Park, S.: Personalized recommendation on dynamic content using predictive bilinear models. In: Proceedings of the 18th international conference on world wide web, p. 691700. ACM (2009)
Albanese, M., Chianese, A., d’Acierno, A., Moscato, V., Picariello, A.: A multimedia recommender integrating object features and user behavior. Multimed. Tools Appl. 50(3), 563585 (2010)
Hakulinen, L., Auvinen, T., Korhonen, A.: The effect of achievement badges on students behavior: an empirical study in a university-level computer science course. Int. J. Emerg. Technol. Learn. 10(1), 18–29 (2015)
Henze, N., Nejdl, W.: A logical characterization of adaptive educational hypermedia. New Rev. Hypermedia Multimed. (NRHM) 10(1), 77–113 (2004)
Brusilovsky, P.: Developing adaptive educational hypermedia systems: From design models to authoring tools. In: Murray, T., Blessing, S., Ainsworth, S. (eds.) Authoring Tools for Advanced Technology Learning Environment, pp. 377–409. Kluwer Academic Publishers, Dordrecht (2003)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Montebello, M. (2018). User Profiling and Personalisation. In: AI Injected e-Learning. Studies in Computational Intelligence, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-67928-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-67928-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67927-3
Online ISBN: 978-3-319-67928-0
eBook Packages: EngineeringEngineering (R0)