Personalized course generation and evolution based on genetic algorithms

Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.     

Resource reconstruction algorithms for on-demand allocation in virtual computing resource pool

Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and the analysis of the resource status transition, the resource allocation process and the necessity of resource reconstruction are presented. Resource reconstruction algorithms are designed to determine the resource reconstruction types, and it is shown that they can achieve the goal of resource on-demand allocation through three methodologies: resource combination, resource split, and resource random adjustment. The effects that the resource users have on the resource reconstruction results, the deviation between resources and requirements, and the uniformity of resource distribution are studied by three experiments. The experiments show that resource reconstruction has a close relationship with resource requirements, but it is not the same with current distribution of resources. The algorithms can complete the resource adjustment with a lower cost and form the logic resources to match the demands of resource users easily.     

A Hybrid Structure of Fuzzy Neural Networks Generation via Q-Learning

In this paper, a hybrid approach termed Biased Dynamic Self-Generated Fuzzy Q-Learning (BDSGFQL) for automatically generating Fuzzy Neural Networks (FNNs) is proposed. In the proposed method, an FNN is generated via the Q-learning and the embedded human expert knowledge. The human expert knowledge is embedded as a bias of the system according to the condence level of the knowledge. The novel BDSGFQL methodology can also automatically create, delete and adjust fuzzy rules according to the evaluations of the entire system as well as the individual fuzzy rules. The salient characteristics of the BDSGFQL approach are: 1) Capable of embedding expert knowledge according to the condence level; 2) Capable of structure self-identication and automatic parameter estimation and modication; 3) FNNs can be quickly generated without supervised learning; 4) Fuzzy rules can be created, adjusted and deleted dynamically; 5) Membership functions of an FNN can be dynamically adjusted according to the evaluation of reinforcement learning. Simulation studies of a wall-following task by a mobile robot demonstrate the superiority of the proposed method.     

Use Case for the Development of a Mobile Learning Application

This paper presents use cases developed for a mobile learning application named MLEA （mobile learning environment adapter）, developed in collaboration with the Universidad do Vale do Rio dos Sinos-Unisinos, Brazil and UTP （Universidad Tecnologica de Panama）. MLEA allows users to access, through a single platform, courses in a content management system--Moodle. The objective of this project is to create a communication platform that allows users of Android based mobile devices to use services of the virtual campus of UTP. The work team is composed of six professors and researchers in Panama and three in Brazil, with voluntary students in both countries. A complete, step by step use case is documented, including requirement specifications, interface designs based on Android patterns, as well as programming patterns used for the communication between mobile devices and Moodle platform.     

Design and Application of a Robust Vision Tracking System for Unmanned Rotorcraft

In order to satisfy the requirements of UAV’s aerial safety monitoring and surveillance of sensitive areas,a robust vision system for the rotor UAV is designed and implemented,which includes visual airborne subsystem,ground station subsystem and wireless communication subsystem.Complete sky-ground and human-computer interaction loops are constructed.Based on the developed UAV vision platform,a real-time target tracking algorithm under the mean shift tracking framework is developed.The joint color-texture histogram is used to represent the target robustly.With the help of moment information,the scale and the orientation of the tracked target is estimated adaptively during the tracking process.A model updating scheme for the target and the background is introduced to reduce the interferences from background and the locating biases.Numerical simulation and real flight tracking experiments demonstrate that the overall visual tracking system is effective and has superior performance against several state-of-the-art algorithms.     

Identification Scheme for Fractional Hammerstein Models With the Delayed Haar Wavelet

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix(HWOM). To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.     

Knowledge assessment-practical example in testing

Knowledge assessment is inseparable part of current e-learning technologies. It can be used for self-assessment of students to give them feedback about their progress in a study or for an intermediate or final grading for tutors. However, knowledge tests are not developed with the adequate care. Author＇s experience in the area of knowledge assessment led to a confidence the ＂unstructured＂ testing is usually used in this process. It means that many of knowledge tests are not designed to reveal the reached level of knowledge. Moreover, testing suites are reviewed very seldom regarding their validity and items correlation. This paper presents experiences gained during the design and implementation of specific software focused on teaching several principles of the Unix-like operating systems. The structure of the specific assignment follows the Bloom＇s taxonomy of educational objectives.     

Unsupervised motion detection with background update and shadow suppression

An algorithm is developed to detect moving object and suppress shadow.According to motion variations caused by some moving objects in a scene,a background update approach is proposed.The developed update method efficiently prevents undesired corruption of background and does not consider the adaptation coefficient or the learning rate used in some existing algorithms.A multi-scale wavelet transform methodology is used to segment foreground from a clutter background.The optimal selection of threshold value is automatically determined which does not require any complex supervised training or manual calibration.According to photometric invariant,a color ratio difference is proposed to suppress shadow.Some complete foreground motion object regions are extracted by integrating moving object segmentation in the multi-scale wavelet with shadow suppression in the color ratio difference.The mentioned method is less affected by the presence of moving objects in a scene.Experimental results show that the proposed approach is efficient in detecting motion objects and suppressing shadows by comparisons.     

螺旋式方法在软件工程学习中的应用(英文)

The spiral learning method aims at improving study interest and setting up a learning target and changing passive learning to change into active learning.In software engineering study,the realization of the spiral learning method sets up a learning target according to some items or knowledge and divides a big target into a few of small targets in order to easily make them and helps students better learning in the process of the realization.     

Tensor semantic model for an audio classification system

Audio resources are a very important part of multimedia information.The classification effect of audio is directly related to the service mode of personal resource management systems.At present,vector features have been widely used in audio classification systems.However,some semantic correlations among different audio information can not be completely expressed by simple vector representation.Tensors are multidimensional matrices,and their mathematical expansion and application can express multi-semantic information.The tensor uniform content locator(TUCL) is proposed as a means of expressing the semantic information of audio,and a three-order tensor semantic space is constructed according to the semantic tensor.Tensor semantic dispersion(TSD) can aggregate some audio resources with the same semantics and,at the same time,its automatic classification can be accomplished by calculating the TSD.In order to effectively utilize TSD classification information,a radial basis function tensor neural network(RBFTNN) is constructed and used to train an intelligent learning model.Experimental results show that the tensor model can significantly improve the classification precision under multi-semantic classification requests within an information resource management system.     

一种基于Max-Flow的协同学习活动调度算法

远程教育中的在线学习过程并非简单的资源检索过程,而是由一系列学习活动组成的。现有的远程教育平台往往过于注重教学内容呈现的个性化,而忽视了对学习者活动的引导。从教育技术学的角度,不同学习目标和情景下的在线学习活动通常可以分为多种模式,针对其中的协同学习模式,提出了一种实现学习活动引导服务的方法。该方法将活动引导以调度生成的方式加以实现,提出了具体的调度模型和求解算法。该算法基于Max-Flow设计,具有较小的时间复杂度,能够有效地为学习活动引导服务提供支撑。     

基于Petri网的网络学习流程模型研究

网络学习流程是一个学习流程业务过程的全部或部分自动执行。通过分析现实世界学习流程业务过程,提出了一套符合教学活动特性和要求的、针对网络学习的学习流程摔制模型,并采用Petri网对其进行动态建模。文中详细论述了学习活动中各环节的相互协作关系,并使其在ELS网络学习平台中得以具体实现。     

改进的E-learning社区自组织算法

为了解决E-learning过程中缺乏对学习者有针对性指导的难题,该文提出一种构建E-learning社区的改进算法。该算法采用P2P的架构,通过改进的Hebbian学习法则不断加强具有类似兴趣的学习者之间的联系,从而自动调整社区结构,最终将具有类似兴趣的学习者组织在一起。实验结果证明,与传统的P2P架构的Hebbian学习算法相比,该算法达到了更好的社区建设质量和更高的社区建设速度。     

A fully personalization strategy of E-learning scenarios

The personalization in E-learning systems has been the subject of many recent research efforts. While a large number of systems have been implemented, many of these systems allow the application of very few if not just one predefined personalization strategy. This is a constraint for providing effective E-learning experience and for rationalizing the personalization needs of the pedagogues, the professors and the learners. In this paper, we propose a new approach for personalization of learning scenarios based on two levels: The first level allows the personalization of learning scenarios according to a predefined personalization strategy. The second level allows teachers to select personalization parameters and combine them flexibly to define different personalization strategies according to the specifics of courses. The proposed solution is a step to federate the research efforts on the E-learning personalization by integrating and combining the personalization parameters. Concerning the technological aspect, Web service technology constitutes an operational solution for implementing our approach and for the interoperability with other E-learning personalization systems. Beside the implementation of an interoperable solution, we also aim to enable teachers to provide proper personalized learning scenarios.     

一种基于模糊理论的个性化网络学习系统

在信息社会中,学习已经成为人们日常生活中很重要的组成部分。网络学习是一种集计算机网络技术、卫星通信技术和多媒体技术于一体的学习方式,它对人们的终身学习起到非常重要的作用。提出了一种基于模糊集理论的个性化网络学习系统,利用模糊集理论知识构建和描述学习资源数据库模型和学习者数据库模型。这种系统既能形成描述网络课程知识的模糊结构图,又能针对不同的学习者形成学习者的模糊结构子图,并能根据学习者的学习进度和能力水平,提供不同的学习内容和导航策略,从而满足个性化网络学习的需求。     

Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning

This paper aims to identify individual characteristics that motivate learners to use mobile-learning. It sheds light on our current knowledge by a) examining an m-learning adoption model which accounts for learners’ characteristics (learning style and personal innovativeness) in addition to previously studied mobile platform characteristics and b) considering the context in which learning occurs (formal and informal). A framework has been introduced and empirically tested. Results suggest that individuals’ learning style and perceived playfulness influence m-learning usage in both learning situations; while performance expectancy and personal innovativeness are only influential in specific learning contexts. This study highlights the role of learners’ characteristics in m-learning adoption and emphasizes the importance of distinguishing between various types of m-learning. This multi-disciplinary research enriches m-learning literature and offers practical implications for educators using mobile technologies as well as developers of virtual learning platforms.     

Generalized metrics for the analysis of E-learning personalization strategies

For personalizing E-learning, several different strategies and characteristics can be used and considered by teachers and course authors/designers. In order to make appropriate decisions on how to best implement personalized E-learning, this paper focuses on the question: How to foresee personalization strategies that are appropriate for particular courses? To answer this question, we present an approach for recommending personalization strategies based on the learning objects included in the course as well as on how well they support particular combinations of learners’ characteristics. In particular, the paper presents generalized metrics which support teachers for analyzing and comparing personalization strategies, as well as deciding which one should be applied for personalizing each course. The approach was validated through experiments in order to test its feasibility and success when applied to a large number of learning objects and learners’ characteristics.     

Analysis of E-learning innovation and core capability using a hypercube model

New information and communication technologies and emerging learning models have triggered a new wave of educational innovation-electronic learning (E-learning). This study utilizes a hypercube innovation model to analyze the differences in technology and learning models (instruction model/environment) used in traditional classroom learning versus E-learning environments. The results show that the innovation from traditional classroom learning to E-learning is radical for both the learner and instructor, leading to drastic changes in the technology and learning model. For educational institutions, the technology is a fundamental change, while the learning model is reinforced. From the dynamic capability perspectives, a set of core capabilities needed for successfully exploiting E-learning is identified. These results provide insight for learners, instructors and educational institutions for enhancing their understanding of E-learning innovation, and provide guidelines to help E-learning stakeholders adapt from the traditional classroom to E-learning environments.     

一种基于本体的具有语义搜索功能的E-learning模型

针对符合SCORM规范的教学资源，提出一个具有效语义搜索功能的E-learning模型。通过SCORM本身对LOM学习对象已规定好丰富的Meta-data，并引进本体工程理论（Ontological Engineering），对不同的符合SCORM规范的教学资源（教材）加以分析、对比、归纳、关联、评估、推荐，使得不同的E-leaming中的教材得以串联，以辅助网络学习平台对SCORM教材能进行更精确的分类管理工作。而学习者通过网络学习教材中找到所需要的资料，改善以往查询准确度不够高的问题。     

Learning styles assessment and theoretical origin in an E-learning scenario: a survey

The performance of the learners in E-learning environments is greatly influenced by the nature of the posted E-learning contents. In such a scenario, the performance of the learners can be enhanced by posting the suitable E-learning contents to the learners based on their learning styles. Hence, it is very essential to have a clear knowledge about various learning styles in order to predict the learning styles of different learners in E-learning environments. However, predicting the learning styles needs complete knowledge about the learners past and present characteristics. Since the knowledge available about learners is uncertain, it can be resolved through the use of Fuzzy rules which can handle uncertainty effectively. The core objective of this survey paper is to outline the working of the existing learning style models and the metrics used to evaluate them. Based on the available models, this paper identifies Felder–Silverman learning style model as the suitable model for E-learning and suggests the use of Fuzzy rules to handle uncertainty in learning style prediction so that it can enhance the performance of the E-learning system.