Visualizing the sequential process of knowledge elaboration in computer-supported collaborative problem solving

This case study illustrates the sequential process of the joint and individual knowledge elaboration in a computer-supported collaborative learning (CSCL) environment. The case comprised an Internet-based physics problem-solving platform. Six Dutch secondary school students (three males, three females) participated in the three-week experiment. They were paired based on self-selection. Each dyad was asked to collaborate on eight moderately structured problems concerning Newtonian mechanics. Their online interactions, including their textual and pictorial messages, were categorized and sequentially plotted. The three dyads showed three different collaboration patterns in terms of joint and individual knowledge elaboration.     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Learning teamwork skills in university programming courses

University courses about computer programming usually seek to provide students not only with technical knowledge, but also with the skills required to work in real-life software projects. Nowadays, the development of software applications requires the coordinated efforts of the members of one or more teams. Therefore, it is important for software professionals to master the sort of skills that assure the success of teamwork, such as communication, leadership, negotiation, or team management. However, these abilities are difficult to teach, one of the reasons being that they require true commitment from the students. However, today students are taking a more and more passive role in their own education, two of the more evident consequences being the increase in dropout rates and the decrease in marks obtained in exams. The NUCLEO e-learning framework has been designed to promote the effective acquisition of teamwork skills and, at the same time, to promote the more active participation of the students in their own learning process. NUCLEO adopts a socio-constructivist pedagogical approach that pursues the development of communities of practice for Problem Based Learning. Our research has rooted the design decisions of NUCLEO in the analysis of its socio-cultural environment with Activity Theory, which considers conflicts within groups as the impetus of their evolution and the forges of their environments. This paper presents the analysis of the main features of NUCLEO according to Activity Theory, as well as the experimental results obtained with the framework in three different case studies in university courses.     

Measuring individual learning performance in group work from a knowledge integration perspective

Evaluation is an important part of the teaching-learning process, and it becomes more difficult when individuals are developing a joint project and individual marks have to be assigned to the group members. Different strategies can be used to perform this task. In this work, an approach that combines the global group results and the individual performance is presented. This approach makes use of a semantic framework to rank the individual participation of each group member and to compare their results with those they should have obtained to achieve the final mark. An experiment performed in real settings is also reported in this paper.     

复杂适应系统的多层级建模研究

针对复杂系统仿真普遍缺乏层级思想,局限于广度模拟的情况,对复杂适应系统（CAS）层级建模进行研究,协同进化理论,从主体平均适应度的角度,分析中间子系统的生成、过渡和演化,得到层级结构产生的内在动因,提出层级CAS演化的数学模型和层级框架。该模型应用于多人重复囚徒博弈,结果证明其能在高N值时形成稳定高合作率。     

Cooperative optimal preview tracking control of continuous-time multi-agent systems

This paper presents the cooperative preview control method for continuous-time multi-agent systems with a time-invariant directed communication topology. First, the cooperative tracking preview control problem is transformed into the optimal regulation problem of an augmented system. Next, by applying the results of the standard optimal preview control of continuous-time linear systems, a controller of the augmented system is obtained. Furthermore, the stabilisability and detectability of the augmented system are studied under the fixed digraph and the prescribed leader. Then, we derive the controller of multi-agent systems with error integral and preview action that can guarantee the achievement of cooperative optimal preview tracking. Finally, the effectiveness of the controller is shown by numerical simulations.     

Cooperative group optimization with ants (CGO-AS): Leverage optimization with mixed individual and social learning

We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The optimization performance is tested with instances of the traveling salesman problem (TSP). The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning. The best performance is achieved under the condition when agents use individual memory as their primary information source, and simultaneously use social memory as their searching guidance. In comparison with existing AS systems, CGO-AS retains a faster learning speed toward those higher-quality solutions, especially in the later learning cycles. The leverage in optimization by CGO-AS is highly possible due to its inherent feature of adaptively maintaining the population diversity in the individual memory of agents, and of accelerating the learning process with accumulated knowledge in the social memory.     

A new paradigm for urban surveillance with vehicular sensor networks

We consider a new application paradigm of vehicular sensor networks (VSN). Currently, vehicles are equipped with forward facing cameras to assist forensic investigations of events by proactive image-capturing from streets and roads. Due to content redundancy and storage imbalance in this in-network distributed storage system, how to maximize its storage capacity becomes a nontrivial challenge. In other words, how to maximize the average lifetime of sensory data (i.e., images generated by cameras) in the network is a fundamental problem to be solved. This paper presents, VStore, a cooperative storage solution in vehicular sensor networks for mobile surveillance, which has been designed to support redundancy elimination and storage balancing throughout the network. Compared with existing works, we propose a novel storage architecture for urban surveillance and deal with challenges in a mobile scenario. Field testing was carried out with a trace-driven simulator, which utilized about 500 taxis in Shanghai. The testing results showed that VStore can largely prolong the average lifetime of sensory data by cooperative storage.     

无线传感网协作能量感知路由方法*

将多跳分集引入无线传感网中以节能,提出协作能量感知路由方法（CEAR：Cooperative Energy Aware Routing）。协作能量感知路由方法利用多跳分集方法对无线传感网经典的能量感知路由（EAR: Energy Aware Routing）方法加以改进。分析了多跳分集方法中各跳的总能耗,并基于能耗分析结果设计协作能量感知路由方法。仿真结果表明,相比于能量感知路由方法,协作能量感知路由方法有更好的节能性能。     

一种新型的认知无线电合作频谱感知算法*

为了提高认知无线电频谱感知性能,同时考虑到不同认知用户SU(Secondary User)具有不同的感知贡献和谈判力量,该文利用合作博弈理论提出了一种新的基于认知无线电的合作频谱感知非对称纳什谈判算法ANBS(Asymmetric Nash Bargaining Solution),该算法充分考虑到了每一个认知用户的感知可信度不尽相同的情况。仿真结果表明,与NBS(Nash Bargaining Solution)等算法相比,该算法不仅具有更强的合理性和可靠性,而且使系统整体感知性能得到了较大提高。