摘要 :
This paper starts with the analysis of the current situation of learner model, and analyzes the construction ideas of the learner characteristic model. First, the learner information specification of the learner information base i...
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This paper starts with the analysis of the current situation of learner model, and analyzes the construction ideas of the learner characteristic model. First, the learner information specification of the learner information base is defined, and the learner information is divided into two categories: basic information and learning behavior information. Then, the author studies the learner characteristic model from four dimensions: basic characteristics, cognitive level, learning style and academic emotion.
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摘要 :
This paper starts with the analysis of the current situation of learner model, and analyzes the construction ideas of the learner characteristic model. First, the learner information specification of the learner information base i...
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This paper starts with the analysis of the current situation of learner model, and analyzes the construction ideas of the learner characteristic model. First, the learner information specification of the learner information base is defined, and the learner information is divided into two categories: basic information and learning behavior information. Then, the author studies the learner characteristic model from four dimensions: basic characteristics, cognitive level, learning style and academic emotion.
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摘要 :
The era of "big data" technology has penetrated into all areas of society. In the field of education, personalized teaching has gradually become a new development direction. With the widespread promotion of online education, the a...
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The era of "big data" technology has penetrated into all areas of society. In the field of education, personalized teaching has gradually become a new development direction. With the widespread promotion of online education, the adaptive learning system has attracted attention because of its good course recommendation function. As an important part of the general model, the student model reflects the individual characteristics, knowledge status and cognitive ability of the students. The traditional student model judges students based solely on the student's basic information and simple test scores. This paper proposes a CD-CAT hybrid model based on the actual scene of the learner, retains the original static question bank, and dynamically updates the student model based on the test database of the learner's different knowledge structure. The knowledge field is divided into multiple categories, and the dynamic allocation of the question bank between categories is carried out. After testing, this dynamic model has a good effect.
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摘要 :
The era of "big data" technology has penetrated into all areas of society. In the field of education, personalized teaching has gradually become a new development direction. With the widespread promotion of online education, the a...
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The era of "big data" technology has penetrated into all areas of society. In the field of education, personalized teaching has gradually become a new development direction. With the widespread promotion of online education, the adaptive learning system has attracted attention because of its good course recommendation function. As an important part of the general model, the student model reflects the individual characteristics, knowledge status and cognitive ability of the students. The traditional student model judges students based solely on the student's basic information and simple test scores. This paper proposes a CD-CAT hybrid model based on the actual scene of the learner, retains the original static question bank, and dynamically updates the student model based on the test database of the learner's different knowledge structure. The knowledge field is divided into multiple categories, and the dynamic allocation of the question bank between categories is carried out. After testing, this dynamic model has a good effect.
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摘要 :
The tendency to informatization of the learning process is increasing in the present. Electronic textbooks and learning systems are being developed also virtual school environment is being created. Last but not least, e-Iearning a...
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The tendency to informatization of the learning process is increasing in the present. Electronic textbooks and learning systems are being developed also virtual school environment is being created. Last but not least, e-Iearning and automated learning systems have started to develop significantly. The basic role in developing and improving learning systems is to create a customized system. Under this concept, the ability of the system to adapt to the individual features, capabilities, user needs, and also the ability to respond to changes in the area and the external environment. The role of developing an adaptive learning system is addressed in a variety of ways and methods for which there are no clear recommendations. The adaptability can be determined in a variety of ways for different types of systems. For the implementation of the adaptive environment, it is necessary to define the characteristics of the adaptive model, the characteristics of the user as well as the essential information about the processes important for building the personal interface. The characteristics and parameters that define the basic requirements for the development of adaptive learning systems are defined at work.
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摘要 :
The tendency to informatization of the learning process is increasing in the present. Electronic textbooks and learning systems are being developed also virtual school environment is being created. Last but not least, e-Iearning a...
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The tendency to informatization of the learning process is increasing in the present. Electronic textbooks and learning systems are being developed also virtual school environment is being created. Last but not least, e-Iearning and automated learning systems have started to develop significantly. The basic role in developing and improving learning systems is to create a customized system. Under this concept, the ability of the system to adapt to the individual features, capabilities, user needs, and also the ability to respond to changes in the area and the external environment. The role of developing an adaptive learning system is addressed in a variety of ways and methods for which there are no clear recommendations. The adaptability can be determined in a variety of ways for different types of systems. For the implementation of the adaptive environment, it is necessary to define the characteristics of the adaptive model, the characteristics of the user as well as the essential information about the processes important for building the personal interface. The characteristics and parameters that define the basic requirements for the development of adaptive learning systems are defined at work.
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The use of the PC and Internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. This paper investigates how to best exploit this data for speaker...
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The use of the PC and Internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. This paper investigates how to best exploit this data for speaker-dependent speech recognition. Supervised and unsupervised experiments in acoustic model and language model adaptation are presented. Using one hour of automatically transcribed speech per speaker with a word error rate of 36.0percent, unsupervised adaptation resulted in an absolute gain of 6.3percent, equivalent to 70percent of the gain from the supervised case, with additional adaptation data likely to yield further improvements. LM adaptation experiments suggested that although there seems to be a small degree of speaker idiolect, adaptation to the speaker alone, without considering the topic of the conversation, is in itself unlikely to improve transcription accuracy.
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Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and hig...
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Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and high cost efficiency, it becomes generally applicable algorithm for curve tracking model. But, the target mobility is very high in practice, Single target tracking model is no longer applicable curve tracking model. To improve the accuracy of tracking, the adaptive grid interacting multiple model (AGIMM) algorithm was given. The algorithm has two fatal weaknesses in the practical application. First, the process of maneuvering target tracking, when the model changes and gradient, the tracking precision is not high. Second, because the changing model structure is very large model sets, the algorithm is complexity and system processing speed is very slow, which cannot be widely used. In order to improve the algorithm and its scope of application, The paper proposed the adaptive Kalman filter adaptive interacting multiple model algorithm (AKFAIMM).The algorithm introduced the parameter in the adaptive Kalman filter, and adjusted parameter in maneuvering target tracking, the parameter was adjusted Continuously in the curve motion model, it could greatly improve the tracking precision and the application of the model. Second, to improve the algorithm complexity. The paper improved on turning curve. The angular velocity estimation method replaced centripetal acceleration estimation method. The estimation method reduced the number of model set and reduced greatly of computation.
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摘要 :
Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and hig...
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Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and high cost efficiency, it becomes generally applicable algorithm for curve tracking model. But, the target mobility is very high in practice, Single target tracking model is no longer applicable curve tracking model. To improve the accuracy of tracking, the adaptive grid interacting multiple model (AGIMM) algorithm was given. The algorithm has two fatal weaknesses in the practical application. First, the process of maneuvering target tracking, when the model changes and gradient, the tracking precision is not high. Second, because the changing model structure is very large model sets, the algorithm is complexity and system processing speed is very slow, which cannot be widely used. In order to improve the algorithm and its scope of application, The paper proposed the adaptive Kalman filter adaptive interacting multiple model algorithm (AKFAIMM).The algorithm introduced the parameter in the adaptive Kalman filter, and adjusted parameter in maneuvering target tracking, the parameter was adjusted Continuously in the curve motion model, it could greatly improve the tracking precision and the application of the model. Second, to improve the algorithm complexity. The paper improved on turning curve. The angular velocity estimation method replaced centripetal acceleration estimation method. The estimation method reduced the number of model set and reduced greatly of computation.
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A hypermedia application offers its users a lot of freedom to navigate through a large hyperspace, described by a domain model. Adaptive hypermedia systems (AHS) aim at overcoming possible navigation and comprehension problems by ...
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A hypermedia application offers its users a lot of freedom to navigate through a large hyperspace, described by a domain model. Adaptive hypermedia systems (AHS) aim at overcoming possible navigation and comprehension problems by providing adaptive navigation support and adaptive content. The adaptation is based on a user model that represents relevant aspects about the user. In this paper, we concentrate on the adaptation engine (AE) that is responsible for performing the adaptation according to the adaptation rules specified in the adaptation model. We analyze the dependencies between the authoring process and the functionality of the adaptation engine. From this we conclude how the authoring process can be simplified by a more powerful AE. In particular, a well-designed AE should be general purpose (i.e., not application domain specific) and should guarantee that the interpretation of the rules is deterministic, always terminates and produces the results desired by the author.
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