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

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Title: The Continuous Hint Factory--Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
Language: English
Authors: Paassen, Benjamin (ORCID 0000-0002-3899-2450), Hammer, Barbara, Price, Thomas William, Barnes, Tiffany, Gross, Sebastian, Pinkwart, Niels
Source: Journal of Educational Data Mining. Jun 2018 10(1):1-35.
Availability: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
Peer Reviewed: Y
Page Count: 35
Publication Date: 2018
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1432156
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Intelligent Tutoring Systems, Cues, Educational Technology, Technology Uses in Education, Inferences, Data Collection, Prediction, Programming, Graphs, Access to Information, Computer Science, Foreign Countries, College Students
Geographic Terms: Germany
ISSN: 2157-2100
Abstract: 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 and 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.
Abstractor: As Provided
Entry Date: 2018
Accession Number: EJ1183785
Database: ERIC
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  Data: The Continuous Hint Factory--Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
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  Data: <searchLink fieldCode="AR" term="%22Paassen%2C+Benjamin%22">Paassen, Benjamin</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3899-2450">0000-0002-3899-2450</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hammer%2C+Barbara%22">Hammer, Barbara</searchLink><br /><searchLink fieldCode="AR" term="%22Price%2C+Thomas+William%22">Price, Thomas William</searchLink><br /><searchLink fieldCode="AR" term="%22Barnes%2C+Tiffany%22">Barnes, Tiffany</searchLink><br /><searchLink fieldCode="AR" term="%22Gross%2C+Sebastian%22">Gross, Sebastian</searchLink><br /><searchLink fieldCode="AR" term="%22Pinkwart%2C+Niels%22">Pinkwart, Niels</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Data+Mining%22"><i>Journal of Educational Data Mining</i></searchLink>. Jun 2018 10(1):1-35.
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  Data: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
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  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
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  Data: 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 and 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.
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      – Text: English
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        PageCount: 35
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    Subjects:
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Cues
        Type: general
      – SubjectFull: Educational Technology
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Inferences
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      – SubjectFull: Data Collection
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      – SubjectFull: Prediction
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      – SubjectFull: Germany
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      – TitleFull: The Continuous Hint Factory--Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
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