Abstract
Developing intelligent tutoring systems from student solution data is a promising approach to facilitating more widespread application of tutors. In principle, tutor feedback can be generated by matching student solution attempts to stored intermediate solution states, and next-step hints can be generated by finding a path from a student’s current state to a correct solution state. However, exact matching of states and paths does not work for many domains, like programming, where the number of solution states and paths is too large to cover with data. It has previously been demonstrated that the state space can be substantially reduced using canonicalizing operations that abstract states. In this paper, we show how solution paths can be constructed from these abstract states that go beyond the paths directly observed in the data. We describe a domain-independent algorithm that can automate hint generation through use of these paths. Through path construction, less data is needed for more complete hint generation. We provide examples of hints generated by this algorithm in the domain of programming.
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
Barnes, T., Stamper, J.: Toward automatic hint generation for logic proof tutoring using historical student data. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 373–382. Springer, Heidelberg (2008)
Blikstein, P.: Using learning analytics to assess students’ behavior in open-ended programming tasks. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 110–116 (2011)
Gross, S., Mokbel, B., Hammer, B., Pinkwart, N.: Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces. In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik, pp. 27–38 (2012)
Huang, J., Piech, C., Nguyen, A., Guibas, L.: Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC. In: AIED 2013 Workshops Proceedings Volume, pp. 25–32 (2013)
Mokbel, B., Gross, S., Paassen, B., Pinkwart, N., Hammer, B.: Domain-Independent Proximity Measures in Intelligent Tutoring Systems. In: Proceedings of the 6th International Conference on Educational Data Mining (EDM), pp. 334–335 (2013)
Rivers, K., Koedinger, K.R.: A Canonicalizing Model for Building Programming Tutors. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 591–593. Springer, Heidelberg (2012)
Vanlehn, K.: The behavior of tutoring systems. International Journal of Artificial Intelligence in Education 16(3), 227–265 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Rivers, K., Koedinger, K.R. (2014). Automating Hint Generation with Solution Space Path Construction. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-07221-0_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07220-3
Online ISBN: 978-3-319-07221-0
eBook Packages: Computer ScienceComputer Science (R0)