Abstract
Learner’s self-awareness of the breadth and depth of their expertise is crucial for self-regulated learning. Further, of learners report self-knowledge assessments to teaching systems, this can be used to adapt teaching to them. These reasons make it valuable to enable learners to quickly and easily create such models and to improve them. Following the trend to open these models to learners, we present an interface for interactive open learner modeling using expertise predictions so that these assist learners in reflecting on their self-knowledge while building their models. We report study results showing that predictions (1) increase the size of learner models significantly, (2) lead to a larger spread in self-assessments and (3) influence learners’ motivation positively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Apted, T., Kay, J., Lum, A., Uther, J.: Visualisation of ontological inferences for user control of personal web agents. In: IV 2003, pp. 306–311. IEEE (2003)
Boud, D.: Reflection: Turning experience into learning. Routledge (1985)
Brusilovsky, P., Millán, E.: User Models for Adaptive Hypermedia and Adaptive Educational Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007)
Bull, S., Gardner, P.: Highlighting learning across a degree with an independent open learner model. In: Artificial Intelligence in Education, pp. 275–282 (2009)
Bull, S., Kay, J.: Student Models that Invite the Learner In: The SMILI:() Open Learner Modelling Framework. IJAIED 17(2), 89–120 (2007)
Bull, S.: Supporting learning with open learner models. In: Proceedings of the 4th Hellenic Conference in Information and Communication Technologies in Education, Athens, Greece, pp. 47–61 (2004)
Bull, S., Kay, J.: Open Learner Models. Springer (to appear, 2012)
Flavell, J.: Metacognition and cognitive monitoring. American Psychologist 34(10), 906–911 (1979)
Hochmeister, M.: Spreading expertise scores in overlay learner models. In: Proceedings of CSEDU (to appear, 2012)
Kay, J.: Lifelong learner modeling for lifelong personalized pervasive learning. IEEE Transactions on Learning Technologies 1(4), 215–228 (2008)
Kay, J., Li, L., Fekete, A.: Learner reflection in student self-assessment. In: Proceedings of the Ninth Australasian Conference on Computing Education, vol. 66, pp. 89–95. Australian Computer Society, Inc. (2007)
Kay, J., Lum, A.: Exploiting readily available web data for reflective student models. In: Proceedings of AIED 2005. Artificial Intelligence in Education, pp. 338–345. IOS Press, Amsterdam (2005)
Kleitman, S.: Metacognition in the Rationality Debate: self-confidence and its Calibration. VDM Verlag (2008)
Mabbott, A., Bull, S.: Student Preferences for Editing, Persuading, and Negotiating the Open Learner Model. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 481–490. Springer, Heidelberg (2006)
Schraw, G., Crippen, K., Hartley, K.: Promoting self-regulation in science education. Research in Science Education 36(1), 111–139 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hochmeister, M., Daxböck, J., Kay, J. (2012). The Effect of Predicting Expertise in Open Learner Modeling. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds) 21st Century Learning for 21st Century Skills. EC-TEL 2012. Lecture Notes in Computer Science, vol 7563. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33263-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-33263-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33262-3
Online ISBN: 978-3-642-33263-0
eBook Packages: Computer ScienceComputer Science (R0)