The Experts below are selected from a list of 368994 Experts worldwide ranked by ideXlab platform
Kenneth R Koedinger - One of the best experts on this subject based on the ideXlab platform.
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discovering Student Models with a clustering algorithm using problem content
Educational Data Mining, 2013Co-Authors: William W Cohen, Kenneth R KoedingerAbstract:One of the key factors that affects automated tutoring systems in making instructional decisions is the quality of the Student Model built in the system. A Student Model is a Model that can solve problems in various ways as human Students. A good Student Model that matches with Student behavior patterns often provides useful information on learning task difficulty and transfer of learning between related problems, and thus often yields better instruction on intelligent tutoring systems. However, traditional ways of constructing such Models are often time consuming, and may still miss distinctions in content and learning that have important instructional implications. Automated methods can be used to find better Student Models, but usually require some engineering effort, and can be hard to interpret. In this paper, we propose an automated approach that finds Student Models using a clustering algorithm based on automaticallygenerated problem content features. We demonstrate the proposed approach using an algebra dataset. Experimental results show that the discovered Model is as good as one of the best existing Models, which is a Model found by a previous automated approach, but without the knowledge engineering effort.
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automated Student Model improvement
Educational Data Mining, 2012Co-Authors: Kenneth R Koedinger, Elizabeth A Mclaughlin, John C StamperAbstract:Student Modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of Student Models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational technology data sets from intelligent tutors to games in a variety of domains from math to second language learning. In at least ten of the eleven cases, the method discovers improved Models based on better test-set prediction in cross validation. The improvements isolate flaws in the original Student Models, and we show how focused investigation of flawed parts of Models leads to new insights into the Student learning process and suggests specific improvements for tutor design. We also discuss the great potential for future work that substitutes alternative statistical Models of learning from the EDM literature or alternative Model search algorithms.
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human machine Student Model discovery and improvement using datashop
Artificial Intelligence in Education, 2011Co-Authors: John C Stamper, Kenneth R KoedingerAbstract:We show how data visualization and Modeling tools can be used with human input to improve Student Models. We present strategies for discovering potential flaws in existing Student Models and use them to identify improvements in a Geometry Model. A key discovery was that the Student Model should distinguish problem steps requiring problem decomposition planning and execution from problem steps requiring just execution of problem decomposition plans. This change to the Student Model better fits Student data not only in the original data set, but also in two other data sets from different sets of Students. We also show how such Student Model changes can be used to modify a tutoring system, not only in terms of the usual Student Model effects on the tutor's problem selection, but also in driving the creation of new problems and hint messages.
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a machine learning approach for automatic Student Model discovery
Educational Data Mining, 2011Co-Authors: William W Cohen, Kenneth R Koedinger, Noboru MatsudaAbstract:Student Modeling is one of the key factors that affects automated tutoring systems in making instructional decisions. A Student Model is a Model to predict the probability of a Student making errors on given problems. A good Student Model that matches with Student behavior patterns often provides useful information on learning task difficulty and transfer of learning between related problems, and thus often yields better instruction. Manual construction of such Models usually requires substantial human effort, and may still miss distinctions in content and learning that have important instructional implications. In this paper, we propose an approach that automatically discovers Student Models using a state-of-art machine learning agent, SimStudent. We show that the discovered Model is of higher quality than human-generated Models, and demonstrate how the discovered Model can be used to improve a tutoring system’s instruction strategy.
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can an intelligent tutoring system predict math proficiency as well as a standardized test
Educational Data Mining, 2008Co-Authors: Mingyu Feng, Joseph E Beck, Neil T Heffernan, Kenneth R KoedingerAbstract:It has been reported in previous work that Students' online tutoring data collected from intelligent tutoring systems can be used to build Models to predict actual state test scores. In this paper, we replicated a previous study to Model Students' math proficiency by taking into consideration Students' response data during the tutoring session and their help-seeking behavior. To extend our previous work, we propose a new method of using Students test scores from multiple years (referred to as cross-year data) for determining whether a Student Model is as good as the standardized test to which it is compared at estimating Student math proficiency. We show that our Model can do as well as a standardized test. We show that what we assess has prediction ability two years later. We stress that the contribution of the paper is the methodology of using Student cross-year state test score to evaluate a Student Model against a standardized test.
Eva Millan - One of the best experts on this subject based on the ideXlab platform.
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bayesian networks for Student Model engineering
Computers in Education, 2010Co-Authors: Eva Millan, Tomasz D. Loboda, Joseluis PerezdelacruzAbstract:Bayesian networks are graphical Modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement Student Models. The Student Model is the key component of any adaptive tutoring system, as it stores all the information about the Student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical Student Modeling problems. A repertoire of Models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed.
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designing a dynamic bayesian network for Modeling Students learning styles
International Conference on Advanced Learning Technologies, 2008Co-Authors: Cristina Carmona, Gladys Castillo, Eva MillanAbstract:When using learning object repositories, it is interesting to have mechanisms to select the more adequate objects for each Student. For this kind of adaptation, it is important to have sound Models to estimate the relevant features. In this paper we present a Student Model to account for learning styles, based on the Model defined by Felder and Sylverman and implemented using dynamic Bayesian networks. The Model is initialized according to the results obtained by the Student in the index of learning styles questionnaire, and then fine-tuned during the course of the interaction using the Bayesian Model, The Model is then used to classify objects in the repository as appropriate or not for a particular Student.
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a bayesian diagnostic algorithm for Student Modeling and its evaluation
User Modeling and User-adapted Interaction, 2002Co-Authors: Eva Millan, Joseluis PerezdelacruzAbstract:In this paper, we present a new approach to diagnosis in Student Modeling based on the use of Bayesian Networks and Computer Adaptive Tests. A new integrated Bayesian Student Model is defined and then combined with an Adaptive Testing algorithm. The structural Model defined has the advantage that it measures Students' abilities at different levels of granularity, allows substantial simplifications when specifying the parameters (conditional probabilities) needed to construct the Bayesian Network that describes the Student Model, and supports the Adaptive Diagnosis algorithm. The validity of the approach has been tested intensively by using simulated Students. The results obtained show that the Bayesian Student Model has excellent performance in terms of accuracy, and that the introduction of adaptive question selection methods improves its behavior both in terms of accuracy and efficiency.
Cristina Conati - One of the best experts on this subject based on the ideXlab platform.
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discovering and recognizing Student interaction patterns in exploratory learning environments
Intelligent Tutoring Systems, 2010Co-Authors: Andrea Bernardini, Cristina ConatiAbstract:In a Exploratory Learning Environment users acquire knowledge while freely experiencing the environment. In this setting, it is often hard to identify actions or behaviors as correct or faulty, making it hard to provide adaptive support to Students who do not learn well with these environments. In this paper we discuss an approach that uses Class Association Rule mining and a Class Association Rule Classifier to identify relevant interaction patterns and build Student Models for online classification. We apply the approach to generate a Student Model for an ELE for AI algorithms and present preliminary results on its effectiveness.
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evaluating adaptive feedback in an educational computer game
Intelligent Virtual Agents, 2009Co-Authors: Cristina Conati, Micheline ManskeAbstract:In this paper, we present a study to evaluate the impact of adaptive feedback on the effectiveness of a pedagogical agent for an educational computer game. We compare a version of the game with no agent, and two versions with agents that differ only in the accuracy of the Student Model used to guide the agent's interventions. We found no difference in Student learning across the three conditions, and we report an analysis to understand the reasons of these results.
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building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game
Intelligent User Interfaces, 2004Co-Authors: Cristina Conati, Xiaohong ZhaoAbstract:Electronic educational games can be highly entertaining, but studies have shown that they do not always trigger learning. To enhance the effectiveness of educational games, we propose intelligent pedagogical agents that can provide individualized instruction integrated with the entertaining nature of the games. In this paper, we describe one such agent, that we have developed for Prime Climb, an educational game on number factorization. The Prime Climb agent relies on a probabilistic Student Model to generate tailored interventions aimed at helping Students learn number factorization through the game. After describing the functioning of the agent and the underlying Student Model, we report the results of an empirical study that we performed to test the agent's effectiveness.
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Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction, 2003Co-Authors: Andrea Bunt, Cristina ConatiAbstract:This paper presents the details of a Student Model that enables an open learning environment to provide tailored feedback on a learner's exploration. Open learning environments have been shown to be beneficial for learners with appropriate learning styles and characteristics, but problematic for those who are not able to explore effectively. To address this problem, we have built a Student Model capable of detecting when the learner is having difficulty exploring and of providing the types of assessments that the environment needs to guide and improve the learner's exploration of the available material. The Model, which uses Bayesian Networks, was built using an iterative design and evaluation process. We describe the details of this process, as it was used to both define the structure of the Model and to provide its initial validation.
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Probabilistic Plan Recognition for Cognitive Apprenticeship
2001Co-Authors: Cristina ConatiAbstract:Interpreting the Student’s actions and inferring the Student’s solution plan during problem solving is one of the main challenges of tutoring based on cognitive apprenticeship, especially in domains with large solution spaces. We present a Student Modeling framework that performs probabilistic plan recognition by integrating in a Bayesian network knowledge about the available plans and their structure and knowledge about the Student’s actions and mental state. Besides predictions about the most probable plan followed, the Bayesian network provides probabilistic knowledge tracing, that is assessment of the Student’s domain knowledge. We show how our Student Model can be used to tailor scaffolding and fading in cognitive apprenticeship. In particular, we describe how the information in the Student Model and knowledge about the structure of the available plans can be used to devise heuristics to generate effective hinting strategies when the Student needs help.
Phengann Heng - One of the best experts on this subject based on the ideXlab platform.
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dual teacher exploiting intra domain and inter domain knowledge with reliable transfer for cardiac segmentation
IEEE Transactions on Medical Imaging, 2021Co-Authors: Shujun Wang, Phengann HengAbstract:Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore the feasibility of concurrently leveraging both unlabeled data and cross-modality data for annotation-efficient cardiac segmentation. To this end, we propose a cutting-edge semi-supervised domain adaptation framework, namely Dual-Teacher++. Besides directly learning from limited labeled target domain data ( e.g. , CT) via a Student Model adopted by previous literature, we design novel dual teacher Models, including an inter-domain teacher Model to explore cross-modality priors from source domain ( e.g. , MR) and an intra-domain teacher Model to investigate the knowledge beneath unlabeled target domain. In this way, the dual teacher Models would transfer acquired inter- and intra-domain knowledge to the Student Model for further integration and exploitation. Moreover, to encourag reliable dual-domain knowledge transfer, we enhance the inter-domain knowledge transfer on the samples with higher similarity to target domain after appearance alignment, and also strengthen intra-domain knowledge transfer of unlabeled target data with higher prediction confidence. In this way, the Student Model can obtain reliable dual-domain knowledge and yield improved performance on target domain data. We extensively evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset. The experiments have demonstrated the superiority of our framework over other semi-supervised learning and domain adaptation methods. Moreover, our performance gains could be yielded in bidirections, i.e. , adapting from MR to CT, and from CT to MR. Our code will be available at https://github.com/kli-lalala/Dual-Teacher- .
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dual teacher exploiting intra domain and inter domain knowledge with reliable transfer for cardiac segmentation
arXiv: Image and Video Processing, 2021Co-Authors: Shujun Wang, Phengann HengAbstract:Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore the feasibility of concurrently leveraging both unlabeled data and cross-modality data for annotation-efficient cardiac segmentation. To this end, we propose a cutting-edge semi-supervised domain adaptation framework, namely Dual-Teacher++. Besides directly learning from limited labeled target domain data (e.g., CT) via a Student Model adopted by previous literature, we design novel dual teacher Models, including an inter-domain teacher Model to explore cross-modality priors from source domain (e.g., MR) and an intra-domain teacher Model to investigate the knowledge beneath unlabeled target domain. In this way, the dual teacher Models would transfer acquired inter- and intra-domain knowledge to the Student Model for further integration and exploitation. Moreover, to encourage reliable dual-domain knowledge transfer, we enhance the inter-domain knowledge transfer on the samples with higher similarity to target domain after appearance alignment, and also strengthen intra-domain knowledge transfer of unlabeled target data with higher prediction confidence. In this way, the Student Model can obtain reliable dual-domain knowledge and yield improved performance on target domain data. We extensively evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset. The experiments have demonstrated the superiority of our framework over other semi-supervised learning and domain adaptation methods. Moreover, our performance gains could be yielded in bidirections,i.e., adapting from MR to CT, and from CT to MR.
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dual teacher exploiting intra domain and inter domain knowledge with reliable transfer for cardiac segmentation
IEEE Transactions on Medical Imaging, 2020Co-Authors: Shujun Wang, Phengann HengAbstract:Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore the feasibility of concurrently leveraging both unlabeled data and cross-modality data for annotation-efficient cardiac segmentation. To this end, we propose a cutting-edge semi-supervised domain adaptation framework, namely Dual-Teacher++. Besides directly learning from limited labeled target domain data (e.g., CT) via a Student Model adopted by previous literature, we design novel dual teacher Models, including an inter-domain teacher Model to explore cross-modality priors from source domain (e.g., MR) and an intra-domain teacher Model to investigate the knowledge beneath unlabeled target domain. In this way, the dual teacher Models would transfer acquired inter- and intra-domain knowledge to the Student Model for further integration and exploitation. Moreover, to encourage reliable dual-domain knowledge transfer, we enhance the inter-domain knowledge transfer on the samples with higher similarity to target domain after appearance alignment, and also strengthen intra-domain knowledge transfer of unlabeled target data with higher prediction confidence. In this way, the Student Model can obtain reliable dual-domain knowledge and yield improved performance on target domain data. We extensively evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset. The experiments have demonstrated the superiority of our framework over other semi-supervised learning and domain adaptation methods. Moreover, our performance gains could be yielded in bidirections, i.e., adapting from MR to CT, and from CT to MR. Our code will be available at https://github.com/kli-lalala/Dual-Teacher-.
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uncertainty aware self ensembling Model for semi supervised 3d left atrium segmentation
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Shujun Wang, Phengann HengAbstract:Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Concretely, the framework consists of a Student Model and a teacher Model, and the Student Model learns from the teacher Model by minimizing a segmentation loss and a consistency loss with respect to the targets of the teacher Model. We design a novel uncertainty-aware scheme to enable the Student Model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. Experiments show that our method achieves high performance gains by incorporating the unlabeled data. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems.
Chuyang Ye - One of the best experts on this subject based on the ideXlab platform.
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semi supervised brain lesion segmentation with an adapted mean teacher Model
International Conference Information Processing, 2019Co-Authors: Yuxing Li, Xiangzhu Zeng, Tianle Wang, Xiuli Li, Yiming Li, Chuyang YeAbstract:Automated brain lesion segmentation provides valuable information for the analysis and intervention of patients. In particular, methods that are based on convolutional neural networks (CNNs) have achieved state-of-the-art segmentation performance. However, CNNs usually require a decent amount of annotated data, which may be costly and time-consuming to obtain. Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available. In this work, we propose a semi-supervised learning (SSL) approach to brain lesion segmentation, where unannotated data is incorporated into the training of CNNs. We adapt the mean teacher Model, which is originally developed for SSL-based image classification, for brain lesion segmentation. Assuming that the network should produce consistent outputs for similar inputs, a loss of segmentation consistency is designed and integrated into a self-ensembling framework. Self-ensembling exploits the information in the intermediate training steps, and the ensemble prediction based on the information can be closer to the correct result than the single latest Model. To exploit such information, we build a Student Model and a teacher Model, which share the same CNN architecture for segmentation. The Student and teacher Models are updated alternately. At each step, the Student Model learns from the teacher Model by minimizing the weighted sum of the segmentation loss computed from annotated data and the segmentation consistency loss between the teacher and Student Models computed from unannotated data. Then, the teacher Model is updated by combining the updated Student Model with the historical information of teacher Models using an exponential moving average strategy. For demonstration, the proposed approach was evaluated on ischemic stroke lesion segmentation. Results indicate that the proposed method improves stroke lesion segmentation with the incorporation of unannotated data and outperforms competing SSL-based methods.
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semi supervised brain lesion segmentation with an adapted mean teacher Model
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yuxing Li, Xiangzhu Zeng, Tianle Wang, Xiuli Li, Yiming Li, Chuyang YeAbstract:Automated brain lesion segmentation provides valuable information for the analysis and intervention of patients. In particular, methods based on convolutional neural networks (CNNs) have achieved state-of-the-art segmentation performance. However, CNNs usually require a decent amount of annotated data, which may be costly and time-consuming to obtain. Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available. In this work, we propose a semi-supervised learning (SSL) approach to brain lesion segmentation, where unannotated data is incorporated into the training of CNNs. We adapt the mean teacher Model, which is originally developed for SSL-based image classification, for brain lesion segmentation. Assuming that the network should produce consistent outputs for similar inputs, a loss of segmentation consistency is designed and integrated into a self-ensembling framework. Specifically, we build a Student Model and a teacher Model, which share the same CNN architecture for segmentation. The Student and teacher Models are updated alternately. At each step, the Student Model learns from the teacher Model by minimizing the weighted sum of the segmentation loss computed from annotated data and the segmentation consistency loss between the teacher and Student Models computed from unannotated data. Then, the teacher Model is updated by combining the updated Student Model with the historical information of teacher Models using an exponential moving average strategy. For demonstration, the proposed approach was evaluated on ischemic stroke lesion segmentation, where it improves stroke lesion segmentation with the incorporation of unannotated data.
