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A spectral learning approach to knowledge tracing 6

A spectral learning approach to knowledge tracing

引用

6th International Conference on Educational Data Mining, EDM 2013

作者： Falakmasir, Mohammad H. Pardos, Zachary A. Gordon, Geoffrey J. Brusilovsky, Peter Intelligent Systems Program University of Pittsburgh 210 South Bouquet Street PittsburghPA United States Computer Science AI Lab Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States School of Information Sciences University of Pittsburgh 135 North Bellefield Ave. PittsburghPA15260 United States

ISBN: (纸本)9780983952527

Bayesian Knowledge Tracing (BKT) is a common way of determining student knowledge of skills in adaptive educational systems and cognitive tutors. The basic BKT is a Hidden Markov Model (HMM) that models student knowledge based on five parameters: prior, learn rate, forget, guess, and slip. Expectation Maximization (EM) is often used to learn these parameters from training data. However, EM is a time-consuming process, and is prone to converging to erroneous, implausible local optima depending on the initial values of the BKT parameters. In this paper we address these two problems by using spectral learning to learn a Predictive State Representation (PSR) that represents the BKT HMM. We then use a heuristic to extract the BKT parameters from the learned PSR using basic matrix operations. The spectral learning method is based on an approximate factorization of the estimated covariance of windows from students’ sequences of correct and incorrect responses;it is fast, local-optimum-free, and statistically consistent. In the past few years, spectral techniques have been used on real-world problems involving latent variables in dynamical systems, computer vision, and natural language processing. Our results suggest that the parameters learned by the spectral algorithm can replace the parameters learned by EM;the results of our study show that the spectral algorithm can improve knowledge tracing parameter-fitting time significantly while maintaining the same prediction accuracy, or help to improve accuracy while still keeping parameter-fitting time equivalent to EM. © 2013 International Educational Data Mining Society. All rights reserved.

关键词： Students

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Keyword annotation of biomedicai documents with graph-based similarity methods 2012

Keyword annotation of biomedicai documents with graph-based ...

引用

2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012

作者： Wang, Shuguang Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States Department of Computer Science University of Pittsburgh Pittsburgh PA United States

ISBN: (纸本)9781467325585

In this paper, we present a new approach that lets us extract, and represent relations among terms (concepts) in the documents and uses these relations to support various document analysis applications. Our approach works by building a graph of local co-occurrence relations among terms that are extracted directly from text and by defining a global similarity metric among these terms and sets of terms using the graph and its connectivity. We demonstrate the benefit of the approach on the problem of MeSH keyword annotation of documents based on their abstracts. © 2012 IEEE.

关键词： Graphic methods

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Factorized diffusion map approximation 15

Factorized diffusion map approximation

引用

15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012

作者： Amizadeh, Saeed Valizadegan, Hamed Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh PittsburghPA15213 United States Department of Computer Science University of Pittsburgh PittsburghPA15213 United States

Diffusion maps are among the most powerful Machine Learning tools to analyze and work with complex high-dimensional datasets. Unfortunately, the estimation of these maps from a finite sample is known to suffer from the curse of dimensionality. Motivated by other machine learning models for which the existence of structure in the underlying distribution of data can reduce the complexity of estimation, we study and show how the factorization of the underlying distribution into independent subspaces can help us to estimate diffusion maps more accurately. Building upon this result, we propose and develop an algorithm that can automatically factorize a high dimensional data space in order to minimize the error of estimation of its diffusion map, even in the case when the underlying distribution is not decomposable. Experiments on both the synthetic and realworld datasets demonstrate improved estimation performance of our method over the standard diffusion-map framework.

关键词： Diffusion

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Variational dual-tree framework for large-scale transition matrix approximation

Variational dual-tree framework for large-scale transition m...

引用

28th Conference on Uncertainty in Artificial Intelligence, UAI 2012

作者： Amizadeh, Saeed Thiesson, Bo Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15213 United States Microsoft Research Redmond WA 98052 United States Department of Computer Science University of Pittsburgh Pittsburgh PA 15213 United States

ISBN: (纸本)9780974903989

In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not scale well due to the quadratic complexity. In this paper, a new dual-tree based variational approach for approximating the transition matrix and efficiently performing the random walk is proposed. The approach exploits a connection between kernel density estimation, mixture modeling, and random walk on graphs in an optimization of the transition matrix for the data graph that ties together edge transitions probabilities that are similar. Compared to the de facto standard approximation method based on k-nearestneighbors, we demonstrate order of magnitudes speedup without sacrificing accuracy for Label Propagation tasks on benchmark data sets in semi-supervised learning.

关键词： Random processes

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Keyword annotation of biomedicai documents with graph-based similarity methods

Keyword annotation of biomedicai documents with graph-based ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuguang Wang Milos Hauskrecht Intelligent Systems Program University of Pittsburgh Pittsburgh USA Department of Computer Science University of Pittsburgh Pittsburgh USA

关键词： Measurement Kernel Resistance Immune system Standards Search engines Abstracts

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The impact of social performance visualization on students

The impact of social performance visualization on students

引用

12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012

作者： Falakmasir, Mohammad Hassan Hsiao, I.-Han Mazzola, Luca Grant, Nancy Brusilovsky, Peter Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States School of Information Sciences University of Pittsburgh Pittsburgh PA United States Faculty of Communication Sciences University of Lugano Lugano Switzerland Department of Computer Information Science Community College of Allegheny County West Mifflin PA United States

ISBN: (纸本)9780769547022

Over the last 10 years two major research directions explored the benefits of visualizing student learning progress. One stream of research on learning performance visualization attempts to build a visual presentation of students' learning progress, targeting the needs of instructors and academic advisors. The other stream of research on Open Student Modeling (OSM) attempts to visualize the state of individual student's knowledge and present the visualization directly to the student. The results of the studies in that area show that, presenting students with basic representation of their knowledge will result in facilitating their metacognitive activities and promoting self-reflection and awareness. This paper tries to study the impact of a more sophisticated form of performance visualization on students. We believe that our visualization tool can positively influence students by granting them the opportunity to get a view of their performance in the content of the class progress. Moreover, we tried to boost their motivation by building a positive sense of competition using a representation of average class performance. In this paper we present study comparing two groups of students, one using the visualization and another without visualization. The results of the study shows that: 1) the students are likely to use the social visualization tool during the whole semester to monitor their progress in comparison with their peers;2) the visualization tool encourages students to use the learning materials in a more continuous manner during the whole semester and 3) students will achieve a higher success rate in answering self-assessment quizzes. © 2012 IEEE.

关键词： Students

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A multivariate probabilistic method for comparing two clinical datasets 12

A multivariate probabilistic method for comparing two clinic...

引用

2nd ACM SIGHIT International Health Informatics Symposium, IHI'12

作者： Sverchkov, Yuriy Visweswaran, Shyam Clermont, Gilles Hauskrecht, Milos Cooper, Gregory F. Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA 15260 United States Department of Computer Science University of Pittsburgh Pittsburgh PA 15260 United States Department of Critical Care Medicine University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9781450307819

We present a novel method for obtaining a concise and mathematically grounded description of multivariate differences between a pair of clinical datasets. Often data collected under similar circumstances reect fundamentally different patterns. For example, information about patients undergoing similar treatments in different intensive care units (ICUs), or within the same ICU during different periods, may show systematically different outcomes. In such circumstances, the multivariate probability distributions induced by the datasets would differ in selected ways. To capture the probabilistic relationships, we learn a Bayesian network (BN) from the union of the two datasets. We include an indicator variable that represents the dataset from which a given patient record is obtained. We then extract the relevant conditional distributions from the network by finding the conditional probabilities that differ most when condi- tioning on the indicator variable. Our work is a form of explanation that bears some similarity to previous work on BN explanation;however, while previous work has mostly focused on justifying inference, our work is aimed at explaining multivariate differences between distributions. Copyright © 2012 ACM.

关键词： Bayesian networks

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The Impact of Social Performance Visualization on Students

The Impact of Social Performance Visualization on Students

引用

International Conference on Advanced Learning Technologies (ICALT)

作者： Mohammad Hassan Falakmasir I-Han Hsiao Luca Mazzola Nancy Grant Peter Brusilovsky Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA School of Information Sciences University of Pittsburgh Pittsburgh PA USA Faculty of Communication Sciences University of Lugano Lugano Switzerland Department of Computer Information Science Community College of Allegheny West Mifflin PA USA

Over the last 10 years two major research directions explored the benefits of visualizing student learning progress. One stream of research on learning performance visualization attempts to build a visual presentation of students' learning progress, targeting the needs of instructors and academic advisors. The other stream of research on Open Student Modeling (OSM) attempts to visualize the state of individual student's knowledge and present the visualization directly to the student. The results of the studies in that area show that, presenting students with basic representation of their knowledge will result in facilitating their metacognitive activities and promoting self-reflection and awareness. This paper tries to study the impact of a more sophisticated form of performance visualization on students. We believe that our visualization tool can positively influence students by granting them the opportunity to get a view of their performance in the content of the class progress. Moreover, we tried to boost their motivation by building a positive sense of competition using a representation of average class performance. In this paper we present study comparing two groups of students, one using the visualization and another without visualization. The results of the study shows that: 1) the students are likely to use the social visualization tool during the whole semester to monitor their progress in comparison with their peers; 2) the visualization tool encourages students to use the learning materials in a more continuous manner during the whole semester and 3) students will achieve a higher success rate in answering self-assessment quizzes.

关键词： Data visualization Adaptation models Educational institutions Visualization Artificial intelligence Communities

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An efficient framework for constructing generalized locally-induced text metrics

An efficient framework for constructing generalized locally-...

引用

22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Amizadeh, Saeed Wang, Shuguang Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh United States Department of Computer Science University of Pittsburgh United States

ISBN: (纸本)9781577355120

In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that let us capture global relations among terms and sets of terms, regardless of their complexity and size. To compute the metric efficiently for any two subsets of terms, we develop an approximation technique that relies on the precompiled term-term similarities. To scale-up the approach to problems with huge number of terms, we develop and experiment with a solution that subsamples the term space. We demonstrate the benefits of the whole framework on two text inference tasks: prediction of terms in the article from its abstract and query expansion in information retrieval.

关键词： Abstracting

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Does query-based diagnostics work?

Does query-based diagnostics work?

引用

8th Bayesian Modeling ApplicationsWorkshop, BMAW 2011

作者： Ratnapinda, Parot Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A 15-351 Bialystok Poland

Query-based diagnostics (Agosta, Gardos, & Druzdzel, 2008) offers passive, incremental construction of diagnostic models that rest on the interaction between a diagnostician and a computer-based diagnostic system. Effectively, this approach minimizes knowledge engineering, the main bottleneck in practical application of Bayesian networks. While this idea is appealing, it has undergone only limited testing in practice. We describe a series of experiments that subject a prototype implementing passive, incremental model construction to a rigorous practical test. We show that the prototype's diagnostic accuracy reaches reasonable levels after merely tens of cases and continues to increase with the number of cases, comparing favorably to state of the art approaches based on learning.

关键词： Bayesian networks

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