Abstract
Providing adaptive support in Exploratory Learning Environments is necessary but challenging due to the unstructured nature of interactions. This is especially the case for complex simulations such as the DC Circuit Construction Kit used in this work. To deal with this complexity, we evaluate alternative representations that capture different levels of detail in student interactions. Our results show that these representations can be effectively used in the user modeling framework proposed in [2], including behavior discovery and user classification, for student assessment and providing real-time support. We discuss trade-offs between high and low levels of detail in the tested interaction representations in terms of their ability to evaluate learning and inform feedback.
Cristina Conati, Lauren Fratamico, Samad Kardan, Ido Roll—All authors have contributed equally to this work and are listed alphabetically.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O.R.: Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions on Knowledge and Data Engineering. 21, 759–772 (2009)
Kardan, S., Conati, C.: A framework for capturing distinguishing user interaction behaviours in novel interfaces. In: Proc. of the 4th Int. Conf. on Educational Data Mining. pp. 159-168. Eindhoven, the Netherlands (2011)
Mavrikis, M., Gutierrez-Santos, S., Geraniou, E., Noss, R.: Design requirements, student perception indicators and validation metrics for intelligent exploratory learning environments. Personal and Ubiquitous Computing, pp. 1–16
Gobert, J.D., Pedro, M.A.S., Baker, R.S.J.D., Toto, E., Montalvo, O.: Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds. JEDM - Journal of Educational Data Mining. 4, 111–143 (2012)
Kardan, S., Roll, I., Conati, C.: The usefulness of log based clustering in a complex simulation environment. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 168–177. Springer, Heidelberg (2014)
Westerfield, G., Mitrovic, A., Billinghurst, M.: Intelligent augmented reality training for assembly tasks. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 542–551. Springer, Heidelberg (2013)
Kardan, S., Conati, C.: Providing adaptive support in an interactive simulation for learning: an experimental evaluation. In: Proceedings of CHI 2015, (to appear)
Hussain, T.S., Roberts, B., Menaker, E.S., Coleman, S.L., Pounds, K., Bowers, C., Cannon-Bowers, J.A., Murphy, C., Koenig, A., Wainess, R. et al.: Designing and developing effective training games for the US Navy. In: The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC). NTSA (2009)
Borek, A., McLaren, B.M., Karabinos, M., Yaron, D.: How much assistance is helpful to students in discovery learning? In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 391–404. Springer, Heidelberg (2009)
Roll, I., Aleven, V., Koedinger, K.R.: The invention lab: using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. In: Intelligent Tutoring Systems, pp. 115–124. Springer (2010)
Leelawong, K., Biswas, G.: Designing Learning by Teaching Agents: The Betty’s Brain System. International Journal of Artificial Intelligence in Education. 18, 181–208 (2008)
Wieman, C.E., Adams, W.K., Perkins, K.K.: PhET: Simulations That Enhance Learning. Science. 322, 682–683 (2008)
Roll, I., Yee, N., Cervantes, A.: Not a magic bullet: the effect of scaffolding on knowledge and attitudes in online simulations. In: Proc. of Int. Conf. of the Learning Sciences, pp. 879–886 (2014)
Kardan, S., Conati, C.: Evaluation of a data mining approach to providing adaptive support in an open-ended learning environment: a pilot study. In: AIED 2013 Workshops Proceedings vol. 2, pp. 41–48 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Conati, C., Fratamico, L., Kardan, S., Roll, I. (2015). Comparing Representations for Learner Models in Interactive Simulations. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-19773-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19772-2
Online ISBN: 978-3-319-19773-9
eBook Packages: Computer ScienceComputer Science (R0)