The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces

Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N (2018)
Journal of Educational Data Mining 10(1): 1-35.

Journal Article | Original Article | Published | English

No fulltext has been uploaded

Author
; ; ; ; ;
Abstract / Notes
Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past students' data. In particular, the Hint Factory (Barnes & Stamper, 2008) recommends edits that are most likely to guide students from their current state towards a correct solution, based on what successful students in the past have done in the same situation. Still, the Hint Factory relies on student data being available for any state a student might visit while solving the task, which is not the case for some learning tasks, such as open-ended programming tasks. In this contribution we provide a mathematical framework for edit-based hint policies and, based on this theory, propose a novel hint policy to provide edit hints in vast and sparsely populated state spaces. In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states. Because the space of possible weighted averages is continuous, we call this approach the Continuous Hint Factory. In our experimental evaluation, we demonstrate that the Continuous Hint Factory can predict more accurately what capable students would do compared to existing prediction schemes on two learning tasks, especially in an open-ended programming task, and that the Continuous Hint Factory is comparable to existing hint policies at reproducing tutor hints on a simple UML diagram task.
Publishing Year
eISSN
PUB-ID

Cite this

Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N. The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining. 2018;10(1):1-35.
Paaßen, B., Hammer, B., Price, T., Barnes, T., Gross, S., & Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), 1-35.
Paaßen, B., Hammer, B., Price, T., Barnes, T., Gross, S., and Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining 10, 1-35.
Paaßen, B., et al., 2018. The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), p 1-35.
B. Paaßen, et al., “The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces”, Journal of Educational Data Mining, vol. 10, 2018, pp. 1-35.
Paaßen, B., Hammer, B., Price, T., Barnes, T., Gross, S., Pinkwart, N.: The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining. 10, 1-35 (2018).
Paaßen, Benjamin, Hammer, Barbara, Price, Thomas, Barnes, Tiffany, Gross, Sebastian, and Pinkwart, Niels. “The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces”. Journal of Educational Data Mining 10.1 (2018): 1-35.
This data publication is cited in the following publications:
This publication cites the following data publications:
External Research Data:
Description
The BinaryAdder UML Data set

Software:
Description
Time Series Prediction for Relational and Kernel Data Toolbox

Export

0 Marked Publications

Open Data PUB

Sources

arXiv 1708.06564

Search this title in

Google Scholar