Productive failure in CSCL groups

This study was designed as a confirmatory study of work on productive failure (Kapur, Cognition and Instruction, 26(3), 379–424, 2008). N = 177, 11th-grade science students were randomly assigned to solve either well- or ill-structured problems in a computer-supported collaborative learning (CSCL) environment without the provision of any external support structures or scaffolds. After group problem solving, all students individually solved well-structured problems followed by ill-structured problems. Compared to groups who solved well-structured problems, groups who solved ill-structured problems expectedly struggled with defining, analyzing, and solving the problems. However, despite failing in their collaborative problem-solving efforts, these students outperformed their counterparts from the well-structured condition on the individual near and far transfer measures subsequently, thereby confirming the productive failure hypothesis. Building on the previous study, additional analyses revealed that neither preexisting differences in prior knowledge nor the variation in group outcomes (quality of solutions produced) seemed to have had any significant effect on individual near and far transfer measures, lending support to the idea that it was the nature of the collaborative process that explained productive failure.

高效帧内预测模式选择算法

帧内预测技术是H.264不同于以往标准的特点之一.在深入分析帧内预测原理以及预测模式选择过程的基础上,提出了一种新的帧内预测模式选择算法.算法通过相邻宏块预测模式相关性原则、预测模式尺寸预先选择原则、提前中止原则来加快编码速度.实验结果表明,与全搜索算法相比,在编码质量相近的情况下,能够节省51.01%～72.91%的编码时间,与FIMDA算法相比,能够节省15.63%～27.31%的编码时间.     

WebQuests as perceived by teachers: implications for online teaching and learning

Abstract   The WebQuest as an instructional tool has recently been widely adopted in K-16 education. However, its underlying principles and functionality are not well understood, which has resulted in an inconsistency in practice. This study identifies the underlying constructs of WebQuests as perceived by teachers and variables affecting their perceptions on WebQuests. A survey was conducted on teachers ( n  = 226) recruited from one large research university in the USA and a professional listserv. The findings reveal three constructs perceived by teachers as critical to WebQuests: constructivist problem solving, social interaction and scaffolded learning. Results also show that variables like purpose of WebQuest use, years of teaching, years of WebQuest use and gender predict, at various degrees, teachers' perceptions on WebQuests. Discussions are made on how the constructs identified can be used to improve online teaching and learning. Suggestions for future study are included by examining the influences of social, psychological and affective factors on learners' learning in WebQuests.     

An efficient algorithm for the collapsing knapsack problem

The collapsing knapsack problem (CKP) is a type of nonlinear knapsack problem in which the knapsack size is a non-increasing function of the number of items included. This paper proposes an exact algorithm for CKP by partitioning CKP to some subproblems, then solving them with the improved expanding-core technique. The proposed algorithm solves the subproblems in the special processing order resulting in the reduction of computing time. Experimental results show that the proposed algorithm is an efficient approach for various random instances of size up to 1000.     

Comparative analysis of Palm and wearable computers for Participatory Simulations

Abstract Recent educational computer‐based technologies have offered promising lines of research that promote social constructivist learning goals, develop skills required to operate in a knowledge‐based economy ( Roschelle et al. 2000 ), and enable more authentic science‐like problem‐solving. In our research programme, we have been interested in combining these aims for curricular reform in school science by developing innovative and progressive hand‐held and wearable computational learning tools. This paper reports on one such line of research in which the learning outcomes of two distinct technological platforms (wearable computers and Palm hand‐helds) are compared using the same pedagogical strategy of Participatory Simulations. Participatory Simulations use small wearable or hand‐held computers to engage participants in simulations that enable inquiry and experimentation ( Colella 2000 ) allowing students to act out the simulation themselves. The study showed that the newer and more easily distributable version of Participatory Simulations on Palms was equally as capable as the original Tag‐based simulations in engaging students collaboratively in a complex problem‐solving task. We feel that this robust and inexpensive technology holds great promise for promoting collaborative learning as teachers struggle to find authentic ways to integrate technology into the classroom in addition to engaging and motivating students to learn science.     

An efficient distributed algorithm for generating and updating multicast trees

As group applications are becoming widespread, efficient network utilization becomes a growing concern. Multicast transmission represents a necessary lower network service for the wide diffusion of new multimedia network applications. Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in networks) is prohibitively expensive. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem, allowing the construction of effective distribution trees using a coordination protocol among the network nodes. Furthermore, we propose a novel distributed technique for dynamically updating the multicast tree. The approach proposed has been implemented and extensively tested both in simulation, and on experimental networks. Performance evaluation indicates that the distributed algorithm performs as well as the centralized version, providing good levels of convergence time and communication complexity.     

交互更新模式的量子遗传算法的几何约束求解

目的针对传统量子遗传算法无法充分利用种群中未成熟个体信息的不足,提出了基于交互更新模式的量子遗传算法(IUMQGA)并应用于几何约束求解中。方法几何约束问题的约束方程组可转化为优化模型,因此约束求解问题可以转化为优化问题。采用将遗传算法与量子理论相结合的量子遗传算法,使用双串量子染色体结构,使用交互更新策略将遗传算法中的交叉操作利用量子门变换来实现,根据不同情况采用不同的交互更新策略。这里的交互,指的是两个个体进行信息交换的过程,该过程用以产生新的个体。这不仅增加了个体间信息的交换而且充分利用了种群中未成熟个体的信息,提高了算法的收敛速度。结果通过非线性方程实例和几何约束实例测试并与其他方法比较表明,基于交互更新模式的量子遗传算法求解几何约束问题具有更好的求解精度和求解速率。双圆外公切线问题实例中,IUMQGA算法比QGA算法稳定;单圆填充问题和双圆外公切线问题实例中,通过实验求得各变量的最优值与其相应的精确值的误差在1E-2以下。结论采用交互更新模式的量子遗传算法可以很好地求解几何约束问题。     

Perceptions of learning effectiveness in M‐learning: scale development and student awareness

The purpose of this study is to develop a multi‐dimensional scale to measure students' awareness of key competencies for M‐learning and to test its reliability and validity. The Key Competencies of Mobile Learning Scale (KCMLS) was determined via confirmatory factor analysis to have four dimensions: team collaboration, creative thinking, critical thinking and problem solving, and communication. The research subjects are 815 students from the elementary school that participate in M‐learning programme in Taiwan. The research results show that students have better self‐awareness in team collaboration and creative thinking, but have worse self‐awareness in critical thinking and problem solving. This study also found that there was no significant difference between genders in the KCMLS, but students who study in the schools that committed in M‐learning longer have higher awareness in all dimensions than students who study in the schools that committed to M‐learning in fewer years.     

Adaptive learning algorithm of self-organizing teams

In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.     

An efficient fault-containing self-stabilizing algorithm for the shortest path problem

Shortest path finding has a variety of applications in transportation and communication. In this paper, we propose a fault-containing self-stabilizing algorithm for the shortest path problem in a distributed system. The improvement made by the proposed algorithm in stabilization times for single-fault situations can demonstrate the desirability of an efficient fault-containing self-stabilizing algorithm. For single-fault situations, the worst-case stabilization time of the proposed algorithm is O(Δ), where Δ is the maximum node degree in the system, and the contamination number of the proposed algorithm is 1.     

Factors of problem-solving competency in a virtual chemistry environment: The role of metacognitive knowledge about strategies

The ability to solve complex scientific problems is regarded as one of the key competencies in science education. Until now, research on problem solving focused on the relationship between analytical and complex problem solving, but rarely took into account the structure of problem-solving processes and metacognitive aspects. This paper, therefore, presents a theoretical framework, which describes the relationship between the components of problem solving and strategy knowledge.     

GENERAL SYSTEMS PROBLEM SOLVING: A Basis for Systemic Knowledge Engineering

Abstract

Starting with the systemic models and formalisms of George Klir [1969,1989] a new approach to knowledge engineering has been developed, which is appropriately called Systemic Knowledge Engineering. In the 1970's Robert Orchard supplemented Klir's models with his own Problem Solving Roadmap, a morphological grid used to model human problem solving. This approach has led us to develop models of knowledge on a hierarchy of meta-levels providing a powerful way to represent complex knowledge. In this paper we trace the development of Systemic Knowledge Engineering until the present. We then describe some of the latest research efforts, showing how the approach is being used to model cooperative problem solving. We also describe some seminal research being done in automated knowledge translation     

An efficient mode decision algorithm for H.264/AVC intra prediction

Multimedia Tools and Applications - Rate distortion optimization technique is adopted by H.264/AVC to select the best intra and inter prediction modes. It achieves remarkable improvement in...     

Improving VDT workplaces in offices by use of a physiologically optimized screen with black symbols on a light background: basic considerations

Due to the increased importance and usage of self-managed virtual teams, many recent studies have examined factors that affect their success. One such factor that merits examination is the configuration or composition of virtual teams. This article tackles this point by (1) empirically testing trait-configuration effects on virtual team performance, which are based on supplementary, complementary and interaction person-team fit perspectives and (2) extending the suggested trait-configuration model to include virtual team configuration in terms of the perceived problem-solving demands of the task as a predictor of team performance. To this end, median regression techniques were applied to data from 62 self-managed virtual teams that used an asynchronous bulletin board for working on a case study analysis. The findings suggest a plausible negative main effect of within-team conscientiousness heterogeneity on team performance, operationalised as standardised team grade. This effect depends on the level of the within-team extroversion heterogeneity which helps to mitigate the negative effect of within-team conscientiousness heterogeneity on team performance. Furthermore, within-team heterogeneity of the perceived problem-solving demands of the task reduces team performance. Overall, this study proves that virtual team configuration matters, and demonstrates that the joint utilisation of multiple person-team fit perspectives for improving virtual team performance has merit. Implications for research and practice are further discussed.     

The statistics of state-spaces

The state-space model is a general, powerful, and elegant representation of problem solving. Nevertheless, state-spaces have rarely been used to model realistic environments because conventional state-spaces are inherently deterministic, while the world is not. This paper extends the model toprobabilistic state-spaces (PSS), which elegantly capture many of the world's complexities by regarding state values as generated by random variables. These PSS models, when combined with decision analytic techniques for knowledge elicitation and encoding, should yield realistic representations. The first investigation of states based on random variables derived the expected-outcome model of two-player games, which led to some powerful results about game evaluators. Most of the work done on expected-outcome was quite general, and should extend easily beyond game-trees to arbitrary state-spaces. The second potential application domain, strategic planning for robot controllers in automated assembly plants, is currently under investigation.This research was supported in part by NSF grants IST-8513989 and IRI-8910173 and by the University of Southern California Faculty Research Initiation Fund.     

An efficient top-down search algorithm for learning Boolean networks of gene expression

Boolean networks provide a simple and intuitive model for gene regulatory networks, but a critical defect is the time required to learn the networks. In recent years, efficient network search algorithms have been developed for a noise-free case and for a limited function class. In general, the conventional algorithm has the high time complexity of O(2^{2k mn k+1}) where m is the number of measurements, n is the number of nodes (genes), and k is the number of input parents. Here, we suggest a simple and new approach to Boolean networks, and provide a randomized network search algorithm with average time complexity O ^{(mn k+1}/ (log m)^(k−1)). We show the efficiency of our algorithm via computational experiments, and present optimal parameters. Additionally, we provide tests for yeast expression data. Editor: David Page     

Improving tools to create learning laboratories for complex problem solving

The experimental results for problem-solving behavior in complex, dynamic systems, though extremely rich and varied, give some clear indications as to how learning laboratories should be devised. However, the best learning laboratories tend to be very resourceful and costly. This paper describes an evolving approach for the design of effective learning laboratories at lower cost by using new tools and combining existing tools in innovative ways.     

An efficient point-in-polygon algorithm

An algorithm, and its FORTRAN implementation, are presented which efficiently determine whether a point is interior to a polygonal boundary. As such algorithms form the heart of many geoscience programs concerned with spatial information, it is important that they be efficient in their use of storage and time. A comparison of the algorithm with others obtained from the literature shows that it represents a significant improvement in run-time. An important feature of the algorithm is a user-specifiable function which can be tailored to any particular application so as to maximize run-time efficiency.     

利用不完整和无结构的文本知识的问题求解方法

学习是智能主体获得解决问题能力的重要途径。当前,大多数的研究工作假设主体从完整和结构化的数据中学习,它们难以应对在很多情况下出现的不完整和无结构的信息。本文提出一种基于信息检索技术的解决问题方法,它能够有效地帮助智能主体从不完整和无结构的文本中寻找有用的知识去解决遇到的问题。在141个故障处理事件上的缺一 交叉测试结果显示,它能使主体的处理事件能力迭3．65分（满分为5分）。这表明,将信息检索和主体技术相整合可以有效地提高后者的解决问题能力。     

AN EVALUATION OF THE TEMPORAL COHERENCE HEURISTIC IN PARTIAL-ORDER PLANNING

This paper presents an evaluation of a heuristic for partial-order planning, known as temporal coherence. The temporal coherence heuristic was proposed by Drummond and Currie as a method to improve the efficiency of partial-order planning without losing the ability to find a solution (i.e., completeness). It works by using a set of domain constraints to prune away plans that do not "make sense," or are temporally incoherent. Our analysis shows that, while intuitively appealing, temporal coherence can only be applied to a very specific implementation of a partial-order planner and still maintain completeness. Furthermore, the heuristic does not always improve planning efficiency; in some cases, its application can actually degrade the efficiency of planning dramatically. To understand when the heuristic will work well, we conducted complexity analysis and empirical tests. Our results show that temporal coherence works well when strong domain constraints exist that significantly reduce the search space, when the number of subgoals is small, when the plan size is not too large, and when it is inexpensive to check each domain constraint.