Abstract
In this paper we present a proposal for introducing hint adaptive selection in an adaptive web-based testing environment. To this end, a discussion of some aspects concerning the adaptive selection mechanism for hints is presented, which results in the statement of two axioms that such hints must fulfil. Then, an empirical study with real students is presented, whose goal is to evaluate a tentative bank of items with their associated hints to determine the usefulness of such hints for different knowledge levels and to calibrate both test items and hints.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rudner, L.M.: An Examination of Decision-Theory Adaptive Testing Procedures. In: Annual meeting of the American Educational Research Association (2002)
Chua Abdullah, S.: Student Modelling by Adaptive Testing - A Knowledge-based Approach. PhD thesis, University of Kent, Canterbury (2003)
Hambleton, R.K., Swaminathan, J., Rogers, H.J.: Fundamentals of Item Response Theory. Sage publications, Thousand Oaks (1991)
Wainer, H.: Computerized Adaptive Testing: A Primer. Lawrence Erlbaum, Hillsdale (1990)
Conejo, R., Guzmán, E., Millán, E., Trella, M., Pérez de la Cruz, J.L., Ríos, A.: SIETTE: a Web-based tool for adaptive testing. Journal of Artificial Intelligence in Education 14, 29–61 (2004)
Guzmán, E., Conejo, R.: A brief introduction to the new architecture of SIETTE. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 405–408. Springer, Heidelberg (2004)
Vygotskii, L.: Mind in Society: The Development of Higher Psychological Processes. Harvard University Press, Cambridge (1978)
Wells, G.: Dialogic Inquiry: Towards a Socio-Cultural Practice and Theory of Education. Cambridge University Press, New York (1999)
Luckin, R., Hammerton, L.: Getting to Know Me: Helping Learners Understand Their Own Learning Needs through Metacognitive Scaffolding. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 759–771. Springer, Heidelberg (2002)
Aleven, V., McLaren, B., Roll, I., Koedinger, K.: Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004)
Gertner, A.S., Conati, C., VanLehn, K.: Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model. In: Proceedings of the 15th National Conference on Artificial Intelligence, Madison, Wisconsin (1998)
Arroyo, I., Beck, J.E., Woolf, B.P., Beal, C.R., Schultz, K.: Macroadapting Animalwatch to gender and cognitive differences with respect to hint interactivity and symbolism. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 574–614. Springer, Heidelberg (2000)
Hume, G.D., Michael, J., Rovick, A., Evens, M.W.: Hinting as a tactic in one-on-one tutoring. Journal of Learning Sciences 5(1), 23–47 (1996)
Birnbaum, A.: Some Latent Trait Models and Their Use in Inferring an Examinee’s Mental Ability. In: Statistical Theories of Mental Test Scores. Addison-Wesley, Reading (1968)
Thissen, D.: MULTILOG: Multiple, categorical item analysis and test scoring using item response theory (version 5.1) (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Conejo, R., Guzmán, E., de-la Cruz, JL.P., Millán, E. (2006). An Empirical Study About Calibration of Adaptive Hints in Web-Based Adaptive Testing Environments. In: Wade, V.P., Ashman, H., Smyth, B. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2006. Lecture Notes in Computer Science, vol 4018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_9
Download citation
DOI: https://doi.org/10.1007/11768012_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34696-8
Online ISBN: 978-3-540-34697-5
eBook Packages: Computer ScienceComputer Science (R0)