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Debugging the Evidence Chain

Title: Debugging the Evidence Chain.
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Name(s): Almond, Russell, author
Kim, Yoon Jeon, author
Shute, Valerie J., author
Ventura, Matthew, author
Type of Resource: text
Genre: Text
Date Issued: 2013-01-01
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In Education (as in many other fields) it is common to create complex systems to assess the state of latent properties of individuals - the knowledge, skills, and abilities of the students. Such systems usually consist of several processes including (1) a context determination process which identifies (or creates) tasks - contexts in which evidence can be gathered,|(2) an evidence capture process which records the work product produced by the student interacting with the task, (3) an evidence identiffication process which captures observable outcome variables believed to have evidentiary value, and (4) an evidence accumulation system which integrates evidence across multiple tasks (contexts), which often can be implemented using a Bayesian network. In suchsystems, aws may be present in the conceptualization, identification of requirements or implementation of any one of the processes. In later stages of development, bugs are usually associated with a particular task. Tasks which have exceptionally high or unexpectedly low information associated with their observable variables may be problematic and merit further investigation. This paper identifies individuals with unexpectedly high or low scores and uses weight-of-evidence balance sheets to identify problematic tasks for follow-up.We illustrate these techniques with work on the game Newton's Playground : an educational game designed to assess a stu- dent's understanding of qualitative physics
Identifier: FSU_libsubv1_scholarship_submission_1472579448 (IID)
Keywords: Bayesian networks, Model construction, Mutual information, Weight of information, Debugging
Publication Note: Workshop from 2013 UAI Application Workshops: Big Data meet Complex Models and Models for Spatial, Temporal and Network Data (Association for Uncertainty in Artificial Intelligence)
Preferred Citation: Almond, R. G., Kim, Y. J., Shute, V. J., & Ventura, M. (2013). Debugging the Evidence Chain. In Almond, R. G., & Mengshoel, O. (Eds.), 2013 UAI Application Workshops: Big Data meet Complex Models and Models for Spatial, Temporal and Network Data (Association for Uncertainty in Artificial Intelligence) (pp. 1--10). CEUR. Retrieved from http://ceur-ws.org/Vol-1024/paper-01.pdf
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1472579448
Owner Institution: FSU
Is Part Of: 2013 UAI Application Workshops: Big Data meet Complex Models and Models for Spatial, Temporal and Network Data (Association for Uncertainty in Artificial Intelligence).

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Almond, R., Kim, Y. J., Shute, V. J., & Ventura, M. (2013). Debugging the Evidence Chain. 2013 Uai Application Workshops: Big Data Meet Complex Models And Models For Spatial, Temporal And Network Data (Association For Uncertainty In Artificial Intelligence). Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1472579448