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Doroudi, Shayan; Brunskill, Emma – International Educational Data Mining Society, 2017
In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…
Descriptors: Bayesian Statistics, Research Problems, Models, Learning
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Doroudi, Shayan; Brunskill, Emma – Grantee Submission, 2017
In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…
Descriptors: Bayesian Statistics, Research Problems, Statistical Analysis, Models
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Sahebi, Shaghayegh; Lin, Yu-Ru; Brusilovsky, Peter – International Educational Data Mining Society, 2016
We propose a novel tensor factorization approach, Feedback-Driven Tensor Factorization (FDTF), for modeling student learning process and predicting student performance. This approach decomposes a tensor that is built upon students' attempt sequence, while considering the quizzes students select to work with as its feedback. FDTF does not require…
Descriptors: Data Analysis, Prediction, Models, Learning
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Griffiths, Thomas L.; Lewandowsky, Stephan; Kalish, Michael L. – Cognitive Science, 2013
Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that…
Descriptors: Culture, Information Dissemination, Mathematical Models, Prediction
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Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L. – Cognitive Science, 2008
This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…
Descriptors: Mathematics Education, Concept Formation, Models, Prediction
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Schulz, Laura E.; Bonawitz, Elizabeth Baraff; Griffiths, Thomas L. – Developmental Psychology, 2007
Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate…
Descriptors: Inferences, Young Children, Bayesian Statistics, Story Telling
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries