The Experts below are selected from a list of 179928 Experts worldwide ranked by ideXlab platform
Roger Nkambou - One of the best experts on this subject based on the ideXlab platform.
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Learning task models in ill-Defined Domain using an hybrid knowledge discovery framework
Knowledge-Based Systems, 2011Co-Authors: Roger Nkambou, Philippe Fournier-viger, Engelbert Mephu NguifoAbstract:Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant Domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given Domain which then extend Domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.
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MICAI (1) - An hybrid expert model to support tutoring services in robotic arm manipulations
Advances in Artificial Intelligence, 2011Co-Authors: Philippe Fournier-viger, Roger Nkambou, Engelbert Mephu Nguifo, André Mayers, Usef FaghihiAbstract:To build an intelligent tutoring system, a key task is to define an expertise model that can support appropriate tutoring services. However, for some ill-Defined Domains, classical approaches for representing expertise do not work well. To address this issue, we illustrate in this paper a novel approach which is to combine several approaches into a hybrid model to support tutoring services in procedural and ill-Defined Domains. We illustrate this idea in a tutoring system for operating Canadarm2, a robotic arm installed on the international space station. To support tutoring services in this ill-Defined Domain, we have developed a model combining three approaches: (1) a data mining approach for automatically building a task model from user solutions, (2) a cognitive model to cover well-Defined parts of the task and spatial reasoning, (3) and a 3D path-planner to cover all other aspects of the task. Experimental results show that the hybrid model allows providing assistance to learners that is much richer than what could be offered by each individual approach.
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AIED - Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains
2009Co-Authors: Philippe Fournier-viger, Roger Nkambou, Engelbert Mephu NguifoAbstract:In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions for a procedural and ill-Defined Domain where classic Domain knowledge acquisition approaches don't work well. In this paper, we describe in details how such a problem space can support important tutoring services such as (1) recognizing the plan of a learner, (2) providing hints and (3) estimating the profile of a learner including its expertise level and missing or misunderstandood skills.
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using knowledge discovery techniques to support tutoring in an ill Defined Domain
Intelligent Tutoring Systems, 2008Co-Authors: Roger Nkambou, Engelbert Mephu Nguifo, Philippe FourniervigerAbstract:Domain experts should provide relevant knowledge to a tutoring system so that it can guide a learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. As an alternative, this paper presents a framework to learn relevant knowledge related to procedural tasks from users' solutions in an ill-Defined procedural Domain. The proposed framework is based on a combination of sequential pattern mining and association rules discovery. The resulting knowledge base allows the tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted in CanadarmTutor.
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Intelligent Tutoring Systems - Intelligent Tutoring Systems
Lecture Notes in Computer Science, 2008Co-Authors: Beverley P. Woolf, Roger Nkambou, Esma Aïmeur, Susanne P. LajoieAbstract:Intelligent Tutoring Systems (ITS) are meant to provide useful tutoring services for assisting the student. These services include coaching, assisting, guiding, helping, and tracking the student during problem-solving situations. To offer high-quality tutoring services, an ITS must be able to establish the correct student profile, then understand and diagnose the student cognitive as well as its affective state. This special issue of Educational Technology & Society presents recent works dealing with those matters. Extracting Procedural Models Using Educational Data Mining The main goal of an intelligent tutoring system is to actively provide guidance to the student in problem-solving situations. Relevant feedback should be founded on a thorough understanding and diagnosis of student responses. Building such understanding and diagnosis model is a difficult issue that is also a time-intensive process involving human experts. This issue becomes even more difficult in ill-Defined Domains where an explicit representation of the training task is hard, if not impossible, to set up. Educational data-mining (EDM) brings some promising solutions to this issue. You will find in this special issue two EDM-based solutions proposed for coping with this problem. Each of these solutions consists of a model that can constantly learn from new learner or user data and thus, guaranties that the tutor provides an up-to-date feedback. In one hand, Barnes and Stamper propose a novel application of Markov decision processes (MDPs) to automatically generate hints for an intelligent tutor that learns. This approach eases the process of building the understanding and diagnosis model of student actions. The authors extracted MDPs from four semesters of student solutions created in a logic proof tutor, and calculated the probability of being able to generate hints for students at any point in a given problem. The results indicate that extracted MDPs and their proposed hint-generating functions are able to provide hints over 80% of the time. The results also indicate that they can provide valuable tradeoffs between hint specificity and the amount of data used to create an MDP. In the other hand, Fournier-Viger et al. present a novel framework for adapting the behavior of intelligent agents based on human experts' data. The framework consists of an extended sequential pattern-mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human learners of multiple profiles, executing a procedural task. The proposed framework has been integrated within CanadarmTutor, an intelligent tutoring system aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. CanadarmTutor acts in an ill-Defined Domain where the problem space associated with a given task consists of an infinite number of paths. The framework was used to improve the behavior of a cognitive agent that adapts its decision by learning from data gathered during past cognitive cycles. …
Engelbert Mephu Nguifo - One of the best experts on this subject based on the ideXlab platform.
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Learning task models in ill-Defined Domain using an hybrid knowledge discovery framework
Knowledge-Based Systems, 2011Co-Authors: Roger Nkambou, Philippe Fournier-viger, Engelbert Mephu NguifoAbstract:Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant Domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given Domain which then extend Domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.
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MICAI (1) - An hybrid expert model to support tutoring services in robotic arm manipulations
Advances in Artificial Intelligence, 2011Co-Authors: Philippe Fournier-viger, Roger Nkambou, Engelbert Mephu Nguifo, André Mayers, Usef FaghihiAbstract:To build an intelligent tutoring system, a key task is to define an expertise model that can support appropriate tutoring services. However, for some ill-Defined Domains, classical approaches for representing expertise do not work well. To address this issue, we illustrate in this paper a novel approach which is to combine several approaches into a hybrid model to support tutoring services in procedural and ill-Defined Domains. We illustrate this idea in a tutoring system for operating Canadarm2, a robotic arm installed on the international space station. To support tutoring services in this ill-Defined Domain, we have developed a model combining three approaches: (1) a data mining approach for automatically building a task model from user solutions, (2) a cognitive model to cover well-Defined parts of the task and spatial reasoning, (3) and a 3D path-planner to cover all other aspects of the task. Experimental results show that the hybrid model allows providing assistance to learners that is much richer than what could be offered by each individual approach.
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AIED - Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains
2009Co-Authors: Philippe Fournier-viger, Roger Nkambou, Engelbert Mephu NguifoAbstract:In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions for a procedural and ill-Defined Domain where classic Domain knowledge acquisition approaches don't work well. In this paper, we describe in details how such a problem space can support important tutoring services such as (1) recognizing the plan of a learner, (2) providing hints and (3) estimating the profile of a learner including its expertise level and missing or misunderstandood skills.
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using knowledge discovery techniques to support tutoring in an ill Defined Domain
Intelligent Tutoring Systems, 2008Co-Authors: Roger Nkambou, Engelbert Mephu Nguifo, Philippe FourniervigerAbstract:Domain experts should provide relevant knowledge to a tutoring system so that it can guide a learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. As an alternative, this paper presents a framework to learn relevant knowledge related to procedural tasks from users' solutions in an ill-Defined procedural Domain. The proposed framework is based on a combination of sequential pattern mining and association rules discovery. The resulting knowledge base allows the tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted in CanadarmTutor.
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Intelligent Tutoring Systems - Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain
Intelligent Tutoring Systems, 1Co-Authors: Roger Nkambou, Engelbert Mephu Nguifo, Philippe Fournier-vigerAbstract:Domain experts should provide relevant knowledge to a tutoring system so that it can guide a learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. As an alternative, this paper presents a framework to learn relevant knowledge related to procedural tasks from users' solutions in an ill-Defined procedural Domain. The proposed framework is based on a combination of sequential pattern mining and association rules discovery. The resulting knowledge base allows the tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted in CanadarmTutor.
Oscar Pastor - One of the best experts on this subject based on the ideXlab platform.
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using uml as a Domain specific modeling language a proposal for automatic generation of uml profiles
Conference on Advanced Information Systems Engineering, 2009Co-Authors: Giovanni Giachetti, Beatriz Marin, Oscar PastorAbstract:Nowadays, there are several MDD approaches that have Defined Domain-Specific Modeling Languages (DSML) that are oriented to representing their particular semantics. However, since UML is the standard language for software modeling, many of these MDD approaches are trying to integrate their semantics into UML in order to use UML as DSML. The use of UML profiles is a recommended strategy to perform this integration allowing, among other benefits, the use of the existent UML modeling tools. However, in the literature related to UML profile construction; it is not possible to find a standardized UML profile generation process. Therefore, a process that integrates a DSML into UML through the automatic generation of a UML profile is presented in this paper. This process facilitates the correct use of UML in a MDD context and provides a solution to take advantage of the benefits of UML and DSMLs.
Philippe Fournier-viger - One of the best experts on this subject based on the ideXlab platform.
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Learning task models in ill-Defined Domain using an hybrid knowledge discovery framework
Knowledge-Based Systems, 2011Co-Authors: Roger Nkambou, Philippe Fournier-viger, Engelbert Mephu NguifoAbstract:Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant Domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given Domain which then extend Domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.
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MICAI (1) - An hybrid expert model to support tutoring services in robotic arm manipulations
Advances in Artificial Intelligence, 2011Co-Authors: Philippe Fournier-viger, Roger Nkambou, Engelbert Mephu Nguifo, André Mayers, Usef FaghihiAbstract:To build an intelligent tutoring system, a key task is to define an expertise model that can support appropriate tutoring services. However, for some ill-Defined Domains, classical approaches for representing expertise do not work well. To address this issue, we illustrate in this paper a novel approach which is to combine several approaches into a hybrid model to support tutoring services in procedural and ill-Defined Domains. We illustrate this idea in a tutoring system for operating Canadarm2, a robotic arm installed on the international space station. To support tutoring services in this ill-Defined Domain, we have developed a model combining three approaches: (1) a data mining approach for automatically building a task model from user solutions, (2) a cognitive model to cover well-Defined parts of the task and spatial reasoning, (3) and a 3D path-planner to cover all other aspects of the task. Experimental results show that the hybrid model allows providing assistance to learners that is much richer than what could be offered by each individual approach.
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AIED - Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains
2009Co-Authors: Philippe Fournier-viger, Roger Nkambou, Engelbert Mephu NguifoAbstract:In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions for a procedural and ill-Defined Domain where classic Domain knowledge acquisition approaches don't work well. In this paper, we describe in details how such a problem space can support important tutoring services such as (1) recognizing the plan of a learner, (2) providing hints and (3) estimating the profile of a learner including its expertise level and missing or misunderstandood skills.
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Intelligent Tutoring Systems - Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain
Intelligent Tutoring Systems, 1Co-Authors: Roger Nkambou, Engelbert Mephu Nguifo, Philippe Fournier-vigerAbstract:Domain experts should provide relevant knowledge to a tutoring system so that it can guide a learner during problem-solving learning activities. However, for ill-Defined Domains this knowledge is hard to define explicitly. As an alternative, this paper presents a framework to learn relevant knowledge related to procedural tasks from users' solutions in an ill-Defined procedural Domain. The proposed framework is based on a combination of sequential pattern mining and association rules discovery. The resulting knowledge base allows the tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted in CanadarmTutor.
Catherine Blake - One of the best experts on this subject based on the ideXlab platform.
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learning user Defined Domain specific relations a situated case study and evaluation in plant science
Association for Information Science and Technology, 2015Co-Authors: Ana Lucic, Catherine BlakeAbstract:Although methods exist to identify well-Defined relations, such as is_a or part_of, existing tools rarely support a user who wants to define new, Domain-specific relations. We conducted a situated case study in plant science and introduce four new Domain-specific relations that are of interest to Domain scientists but have not been explored in information science. Results show that precision varies between relations and ranges from 0.73 to 0.91 for the manufacturer location category, 0.89 and 0.93 for the seed donor-bank relation, 0.29 and 0.67 for the seed origin location, and 0.32 and 0.77 for the field experiment location. The manufacturer location category recall varies from 0.91 to 0.94, the seed bank-donor location recall ranges between 0.93 and 1, the seed origin relation from 0.33 to 0.82 while the field experiment location from 0.67 to 0.83 depending on the classifier and using a combination of lexical and syntactic features in the background.
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ASIST - Learning user-Defined, Domain-specific relations: a situated case study and evaluation in plant science
Proceedings of the Association for Information Science and Technology, 2015Co-Authors: Ana Lucic, Catherine BlakeAbstract:Although methods exist to identify well-Defined relations, such as is_a or part_of, existing tools rarely support a user who wants to define new, Domain-specific relations. We conducted a situated case study in plant science and introduce four new Domain-specific relations that are of interest to Domain scientists but have not been explored in information science. Results show that precision varies between relations and ranges from 0.73 to 0.91 for the manufacturer location category, 0.89 and 0.93 for the seed donor-bank relation, 0.29 and 0.67 for the seed origin location, and 0.32 and 0.77 for the field experiment location. The manufacturer location category recall varies from 0.91 to 0.94, the seed bank-donor location recall ranges between 0.93 and 1, the seed origin relation from 0.33 to 0.82 while the field experiment location from 0.67 to 0.83 depending on the classifier and using a combination of lexical and syntactic features in the background.
