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.     

Using frameworks to develop a distributed conferencing system: an experience report

Application frameworks are a powerful means to reduce software development costs while improving quality. However, at the same time they are difficult to select and understand, as well as hard to learn, use, and debug effectively and efficiently. In this paper we report the story of eConference, a distributed conferencing system that was developed as part of a broader research effort. Here we discuss the lessons learned from the evolution of our conferencing tool over four generations, which have been necessary to find good frameworks and build a flexible distributed tool. Copyright © 2009 John Wiley & Sons, Ltd.     

基于代理的工作流邦联及子流调用框架

在分析层次工作流建模和工作流执行时互操作研究的基础上,设计了一个基于代理的邦联及子流调用框架,将层次化建模技术、流程互操作技术和异构数据模型映射和转换技术集成在一起,同时提供了异常处理和灾难恢复功能,真正从业务层次上解决了流程协作问题,并在TiPLM2.9上实现了该解决方案.流程实例之间通过代理进行交互,各个流程独立运行在自己的工作流引擎中,实现了松耦合和隔离性,增强了系统健壮性.     

基于多代理的CSCL系统成员感知实现

针对一般基于多代理技术的CSCL系统中没有有效感知机制的问题,提出将具有感知功能的学习者Agent应用于协作学习系统中,描述学习者Agent的内部结构,给出感知功能模块中小组任务的分解和原子任务图的构造算法,定义感知强度的计算函数,对感知信息的呈现过程进行描述,有效解决现有协作学习系统中群体互动效果差、感知信息混乱的问题。     

改进邻居集合的个性化推荐算法

协同过滤算法是目前个性化推荐系统中应用最成功的推荐算法之一,但传统的算法没有考虑在不同时间段内寻找最近邻居问题,导致寻找的邻居集合可能不是最近邻居集合。针对此问题,提出一种改进邻居集合的个性化推荐算法。该算法赋予每项评分一个按时间逐步递减的权重,利用加权后的评分寻找目标用户的最近邻居。实验表明,改进的算法提高了推荐系统的推荐质量。     

多传感器基于动态时空特性软硬协作决策的目标检测研究

针对多传感器系统中目标检测时检测精度与效率的矛盾问题,研究了一种具有协同合作决策功能的目标检测算法及其传感器设计方案。首先将多传感器系统依据时域、空域和频域给出三维定义描述,分析了时空动态特性,其次依据二元检验和目标优化检测分析了软决策和硬决策在目标检测时变化规律,建立了一种基于协同合作的目标检测决策机制,最后给出了基于动态时空特性软硬协作决策的目标检测及其传感器头结构设计方案。实验表明,所提目标检测方案在检测精度、检测效率、资源利用率和覆盖率等方面具有明显优势。     

Active learning through collaborative knowledge building using an automatic free-text scoring system in a b-learning environment

According to active learning, students should be responsible for their own learning. Automatic free-text scoring allows teachers to provide open-ended questions with their correct answers to a computer system, so when students answer the questions, they get immediate feedback (a score, a comment, or both). However, teachers are usually overloaded with many tasks, and they may not have time to create the questions with the correct answers. Therefore, in the 2012/2013 academic year, we asked a group of 124 Pre-Primary and Primary Education students to become the creators of the questions and their correct answers in groups in a free-text scoring system, so the questions use learners’ language, not teachers’ language. From them, 41 students (group of involved students, GIS) fulfilled all the requirements during the course. Our hypothesis was that GIS would be able to increase their academic performance and levels of engagement compared to the rest of the students. The results gathered provide statistic evidence to support that hypothesis. This study pretends to help teachers who want to increase the academic performance and levels of engagement of their students in courses that they may find boring and unrelated to the main topic of their degree, or not directly related with their main academic interests.     

Online collaborative learning in dyads: Effects of knowledge distribution and awareness

Research on collaborative learning traditionally assumes a certain degree of symmetry between the learning partners in terms of both their learning-relevant traits and their individual learning outcomes. However, if one collaborative partner is clearly more able, skilled, or knowledgeable than the other partner, then it remains unclear who profits more from the collaboration. The present study aimed to explore this issue by manipulating symmetry in prior knowledge within small groups of online learners (dyads) and measuring their problem-solving efficiency and incidental learning gain on an individual and dyad level. Awareness of this symmetry/asymmetry was manipulated, too, to discern it as a potential moderator. Dyads with symmetrical and asymmetrical prior knowledge performed equally well on most measures. Moreover, on average, the more and the less knowledgeable partners in the asymmetrical conditions had equal learning gains. However, while in dyads with symmetrical knowledge learning gains were correlated between the partners, in the asymmetrical dyads they were not. Awareness of symmetry/asymmetry did not act as a moderator, but, overall, dyads with awareness of each other's knowledge learned more from each other than dyads without such awareness. The benefit of awareness was, however, specific to the learning content exposed via awareness. We conclude that researchers and practitioners should be careful when choosing or assigning collaborative partners to each other, as only for partners with symmetrical prior knowledge can a symmetrical increase in knowledge be expected. We further discuss the implications of these findings for research on knowledge awareness and collaboration.     

A cross‐cultural study of the effect of a graph‐oriented computer‐assisted project‐based learning environment on middle school students' science knowledge and argumentation skills

The purpose of this mixed‐methods study was to explore how seventh graders in a suburban school in the United States and sixth graders in an urban school in Taiwan developed argumentation skills and science knowledge in a project‐based learning environment that incorporated a graph‐oriented, computer‐assisted application (GOCAA). A total of 42 students comprised the treatment condition and were engaged in a project‐based learning environment that incorporated a GOCAA. Of these 42 students, 21 were located in the United States and 21 were located in Taiwan. A total of 26 students comprised the control condition and were engaged in a project‐based learning environment without the GOCAA. Of these 26 students, 15 were in the United States and 11 were in Taiwan. In each country, verbal collaborative argumentation was recorded and the students' post‐essays were collected. A one‐way analysis of variance (ANOVA) was conducted for each measure of science knowledge about alternative energies. The results showed a significant treatment effect for the outcome of scientific explanation among U.S. students, while among Taiwanese students, a significant treatment effect on scientific facts was observed. A one‐way ANOVA was additionally conducted for each measure of argumentation skills and a significant treatment effect on counterarguments and rebuttals was observed among the U.S. students, while in Taiwan, a significant treatment effect on reasoning and rebuttals was observed. A qualitative analysis was conducted to examine how the GOCAA supported students' development of argumentation skills in different countries. This study found distinct argumentation patterns between the U.S. and Taiwanese intervention teams. Additionally, a distinct gender difference in the use of evidence and division of labour was noted when the Taiwanese teams were compared with the U.S. teams, which may be explained by cultural differences. This study concluded that, in both the United States and Taiwan, a project‐based learning environment incorporating a GOCAA was effective in improving students' science knowledge and developing their scientific argumentation skills.     

Collaborative distribution between two logistics service providers

Mathematical models are described for collaborative distribution between two logistics service providers (LSPs). We find that (a) collaborative distribution is conditionally profitable for LSPs, and the conditions for cooperation are given; (b) LSPs save more costs primarily by shortening the delivery distance and decreasing delivery speed; (c) collaborative distribution makes the delivery time either shorter or longer; (d) if one LSP changes its equilibrium price, the total profit conditionally increases, and this condition is related to demand structure and delivery cost structure; and (e) collaborative distribution can solve the personalization of delivery time and reduce pollution to some extent. For the companies sharing the incremental profit, we characterize a transfer payment contract that includes fixed and variable fees. Our findings suggest that LSPs should adopt collaborative distribution to formulate an optimal distribution plan to save costs and shorten the delivery time, and should select partners with smaller cost parameters and both different and moderate speeds. In addition, warehouse and vehicle sharing are beneficial to the implementation of collaborative distribution.