Acquisition of troubleshooting skills in a computer simulation: Worked example vs. conventional problem solving instructional strategies

In a computer-based simulation of a chemical processing plant, the differential effects of three instructional strategies for learning how to troubleshoot the plant’s malfunctions were investigated. In an experiment concerning learners’ transfer performance and mental effort, the simulation presented the three strategies to three groups of learners and measured their performance on the transfer tasks. In this experiment, conventional problem solving was contrasted with two worked example strategies. The results indicated a significant difference between practicing problem solving and using worked examples. Learners who practiced problem solving in an interactive simulation outperformed the learners who studied computer-based worked examples. They also invested lower mental effort in transfer tasks. When accounting for the difference in the learners’ domain knowledge, the strategies were not significantly different among the more experienced learners. For the less experienced learners, those who practiced problem solving significantly outperformed their worked example counterparts. Among all participants and also among less experienced learners the problem solving group invested significantly lower mental effort in the performance of transfer tasks. Based on the results of this study, the authors recommend the use of the conventional problem solving strategy with or without worked examples for learning complex skills.     

Utility and accuracy of smell-driven performance analysis for end-user programmers

This paper proposes a technique, called smell-driven performance analysis (SDPA), which automatically provides situated explanations within a visual dataflow language IDE to help end-user programmers to overcome performance problems without leaving the visual dataflow paradigm. An experiment showed SDPA increased end-user programmers’ success rates at finding performance problems and decreased the time required for finding solutions. Another study, based on using SDPA to analyze a corpus of example end-user programs, revealed that it is usually accurate at identifying performance problems. Based on these results, we conclude that SDPA provides a reliable basis for helping end-user programmers to troubleshoot performance problems, as well as a potential foundation for future work aimed at training users and at aiding code reuse.     

Artificial Neural Networks as Adjuncts for Assessing Medical Students' Problem Solving Performances on Computer-Based Simulations

Artificial neural networks were trained by supervised learning to recognize the test selection patterns associated with students' successful solutions to seven immunology computer-based simulations. New test selection patterns evaluated by the trained neural network were correctly classified as successful or unsuccessful solutions to the problem >90% of the time. The examination of the neural networks output weights after each test selection revealed a progressive and selective increase for the relevant problem suggesting that a successful solution is represented by the neural network as the accumulation of relevant tests. Unsuccessful problem solutions were classified by the neural network software into two patterns of students performance. The first pattern was characterized by low neural network output weights for all seven problems reflecting extensive searching and lack of recognition of relevant information. In the second pattern, the output weights from the neural network were biased toward one of the remaining six incorrect problems suggesting that the student misrepresented the current problem as an instance of a previous problem. Finally, neural network analysis could detect cases where the students switched hypotheses during the problem solving exercises.     

Dynamic problem selection in air traffic control training: a comparison between performance,mental effort and mental efficiency

The differential effects on training and training outcome of four methods of problem selection were investigated in a computer-based training for air traffic control. In one method, training problems were given to the participants in a fixed predetermined sequence, from simple to complex. In the other methods, problems were selected dynamically, based on three different learner variables. These were mental effort, performance and mental efficiency, which is a variable that combines mental effort and performance measures to determine problem efficiency. After the training, transfer was measured. The hypothesis that dynamic problem selection would lead to more efficient training than non-dynamic problem selection was confirmed. The second hypothesis, that dynamic problem selection based on mental efficiency would lead to more efficient training and better transfer than dynamic problem selection based on performance or mental effort alone was not supported. However, the efficiency measures of the three variables were distorted by the differential effects of these variables on the acquisition phase. A possible explanation for the results is that selection based on performance stimulates rule automation, whereas selection based on mental effort or mental efficiency leads to schema acquisition.     

Single and parallel machine capacitated lotsizing and scheduling: New iterative MIP-based neighborhood search heuristics

We propose a general-purpose heuristic approach combining metaheuristics and mixed integer programming to find high quality solutions to the challenging single- and parallel-machine capacitated lotsizing and scheduling problem with sequence-dependent setup times and costs. Commercial solvers fail to solve even medium-sized instances of this NP-hard problem; therefore, heuristics are required to find competitive solutions. We develop construction, improvement and search heuristics all based on MIP formulations. We then compare the performance of these heuristics with those of two metaheuristics and other MIP-based heuristics that have been proposed in the literature, and to a state-of-the-art commercial solver. A comprehensive set of computational experiments shows the effectiveness and efficiency of the main approach, a stochastic MIP-based local search heuristic, in solving medium to large size problems. Our solution procedures are quite flexible and may easily be adapted to cope with model extensions or to address different optimization problems that arise in practice.     

如何利用LANMeter网络测试仪进行网络系统分析

在计算机网络系统中，用户经常会遇到网络速度变慢或性能不良，本文介绍了如何用Ｆｌｕｋｅ公司的ＬＡＮＭｅｔｅｒ网络测试仪在硬件和软件两个方面检测故障点。     

关于网络故障诊断和排除方法的讨论

伴随着网络技术的不断发展和网络规模的扩大,用户数目呈现出急剧增加的趋势,这样一来,对网络故障的诊断和排除工作愈来愈面临着巨大的挑战.从一定程度上来说,只有提高网络故障的诊断能力才能够及时排除网络故障,让网络的运行有效躲避风险.本文针对这一问题展开讨论,并结合各种常见的故障及排除方法进行深入探讨,希望能够推动网络技术的发展.     

A study on the performance of local search versus population-based methods for mesh router nodes placement problem

Node placement problems have been long investigated in the optimization field due to numerous applications in facility location, logistics, services, etc. Such problems are attracting again the attention of researchers now from the networking domain, and more especially from Wireless Mesh Networks (WMNs) field. Indeed, the placement of mesh routers nodes appears to be crucial for the performance and operability of WMNs, in terms of network connectivity and stability. However, node placement problems are known for their hardness in solving them to optimality, and therefore heuristics methods are approached to near-optimally solve such problems. In this work we evaluate the performance of different heuristic methods in order to judge on their suitability of solving mesh router nodes problem. We have selected methods from two different families, namely, local search methods (Hill Climbing and Simulated Annealing) and population-based methods (Genetic Algorithms). The former are known for their capability to exploit the solution space by constructing a path of visited solutions, while the later methods use a population of individuals aiming to largely explore the solution space. In both cases, a bi-objective optimization consisting in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage are considered. In the experimental evaluation, we have used a benchmark of instances??varying from small to large size??generated using different distributions of mesh node clients (Uniform, Normal, Exponential and Weibull).     

Factors influencing analysis of complex cognitive tasks: a framework and example from industrial process control

We propose that considering four categories of task factors can facilitate knowledge elicitation efforts in the analysis of complex cognitive tasks: materials, strategies, knowledge characteristics, and goals. A study was conducted to examine the effects of altering aspects of two of these task categories on problem-solving behavior across skill levels: materials and goals. Two versions of an applied engineering problem were presented to expert, intermediate, and novice participants. Participants were to minimize the cost of running a steam generation facility by adjusting steam generation levels and flows. One version was cast in the form of a dynamic, computer-based simulation that provided immediate feedback on flows, costs, and constraint violations, thus incorporating key variable dynamics of the problem context. The other version was cast as a static computer-based model, with no dynamic components, cost feedback, or constraint checking. Experts performed better than the other groups across material conditions, and, when required, the presentation of the goal assisted the experts more than the other groups. The static group generated richer protocols than the dynamic group, but the dynamic group solved the problem in significantly less time. Little effect of feedback was found for intermediates, and none for novices. We conclude that demonstrating differences in performance in this task requires different materials than explicating underlying knowledge that leads to performance. We also conclude that substantial knowledge is required to exploit the information yielded by the dynamic form of the task or the explicit solution goal. This simple model can help to identify the contextual factors that influence elicitation and specification of knowledge, which is essential in the engineering of joint cognitive systems.     

Robustness analysis with full-structured uncertainties

This paper deals with μ problems involving full-structured (rather than block-diagonal) uncertainty, i.e. uncertainty blocks where each sub-block (or element) may be an independent uncertainty. Rearranging this problem into standard (block-diagonal) form results in an explosion in the required computation, and so a number of researchers have proposed more efficient bounds, in particular those based on non-similarity scaling, for such problems. Here we show how to map such problems into reduced size standard μ problems. The standard bounds applied to the reduced size problem are then shown to be at least as accurate, and require the same computational effort, as earlier techniques, with the added bonus that the standard μ upper bound is convex. Moreover this new approach is applicable in a much more general setting, allowing one to efficiently compute both robust stability and robust performance for problems involving multiple real and complex uncertainty blocks, any number of which may be full-structured.     

Large engineering project risk management using a Bayesian belief network

This paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. Differences of project performance risks between large-scale and medium-sized shipbuilding companies were identified. Exceeding time schedule and specification discontent were more important to large-scale shipbuilding companies, while exceeding budget and exceeding time schedule were more important to medium-sized shipbuilding companies. The change of project performance risks was measured by risk reduction activities of quality management, and strikes at headquarters and subcontractors, in both large-scale and medium-sized shipbuilding companies. The research results should be valuable in enabling industrial participants to manage their large engineering project risks and in extending our understanding of Korean shipbuilding risks.     

Pattern recognition of control charts using artificial neural networks—analyzing the effect of the training parameters

In this paper, we have utilized artificial neural networks (ANN) for pattern recognition of the most common patterns which occur in quality control charts. After detecting such patterns, it is possible to relate these patterns to their causes. This could find extreme importance for on-line quality monitoring and on-line trouble shooting. It could be possible to detect problems before they become serious and the operator has to shut the line down or the process may produce defective parts. In this work, we have attempted to explore the effect of the training parameters on the performance of the neural network. The training parameters are important because they emphasis the required performance and the accuracy required from the neural network. A resolution IV fractional factorial experiment is utilized to explore a portion of the range of selected parameters to obtain better performance of the neural network. The results showed that many parameters usually assigned by experience such as minimum shift, shift range, population size and shift percentage, have significant effect on the performance of the ANN, while others such as network size and window size do not have major significance on the performance of the net.     

Optimization via intermittency with a self-organizing neural network

One of the major obstacles in using neural networks to solve combinatorial optimization problems is the convergence toward one of the many local minima instead of the global minima. In this letter, we propose a technique that enables a self-organizing neural network to escape from local minima by virtue of the intermittency phenomenon. It gives rise to novel search dynamics that allow the system to visit multiple global minima as meta-stable states. Numerical experiments performed suggest that the phenomenon is a combined effect of Kohonen-type competitive learning and the iterated softmax function operating near bifurcation. The resultant intermittent search exhibits fractal characteristics when the optimization performance is at its peak in the form of 1/f signals in the time evolution of the cost, as well as power law distributions in the meta-stable solution states. TheN-Queens problem is used as an example to illustrate the meta-stable convergence process that sequentially generates, in a single run, 92 solutions to the 8-Queens problem and 4024 solutions to the 17-Queens problem.     

Progression of mental models throughout the phases of a computer-based instructional simulation: Supportive information,practice, and performance

Students of chemical engineering (n = 26) participated in an experiment using a computer-based simulation of a chemical plant. The progression of participants’ mental models was examined throughout a computer-based instructional experience as they acquired the complex cognitive skills of troubleshooting. Participants’ mental models of the complex learning task were matched against an expert mental model at five observation points through the instruction. Progressions of learners’ mental models were examined before and after three phases of the instructional process: supportive information presentation, problem solving practice, and performance test. The results indicated a significant change in participants’ mental models after receiving the supportive information and little change after practice or performance. This paper presents the results of this investigation and discusses the findings and their implications for computer-based instruction and training.     

The effects of individually personalized computer-based instructional program on solving mathematics problems

This study investigated the effects of an individually personalized computer-based instructional program on the achievement and attitudes regarding mathematics computational problems and word problems of 104 middle school American students. Students were blocked by math entering knowledge based on pre-test scores, then randomly assigned to a personalized or non-personalized version of the computer-based instructional program. A significant two-way interaction (treatment by math entering knowledge) reflected that personalized higher-level math entering knowledge students and non-personalized higher-level math entering knowledge students had similar post-test scores but personalized lower-level math entering knowledge students scored significantly higher on the post-test than non-personalized lower-level math entering knowledge students. Another significant two-way interaction (math entering knowledge by problem type) reflected that students with higher-level math entering knowledge scored considerably higher on the computational problems than on the word problems while students with lower-level math entering knowledge scored significantly higher on the computational problems than on the word problems. Student attitudes were significantly more favorable toward the personalized computer-based instructional program.     

Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization

In this two-paper series, techniques connected with artificial intelligence and genetics are applied to achieve computer-based control of gas pipeline systems. In this, the first paper, genetic algorithms are developed and applied to the solution of two classical pipeline optimization problems, the steady serial line problem, and the single transient line problem. Simply stated, genetic algorithms are canonical search procedures based on the mechanics of natural genetics. They combine a Darwinian survival of the fittest with a structured, yet randomized, information exchange between artificial chromosomes (strings). Despite their reliance on stochastic processes, genetic algorithms are no simple random walk; they carefully and efficiently exploit historic information to guide future trials.In the two pipeline problems, a simple three-operator genetic algorithm consisting of reproduction, crossover, and mutation finds near-optimal performance quickly. In, the steady serial problem, near-optimal performance is found after searching less than 1100 of 1.1(10¹²) alternatives. Similarly, efficient performance is demonstrated in the transient problem.Genetic algorithms are ready for application to more complex engineering optimization problems. They also can serve as a searning mechanism in a larger rule learning procedure. This application is discussed in the sequal.     

Improvement of an asymmetric actuator for optical image stabilization

This paper presents the improvement of an asymmetric actuator for optical image stabilization in a mobile phone camera. The required specification was defined from typical hand vibrations. The actuator was designed to have an asymmetric shape to reduce its size, but this design caused several problems. These problems were analyzed, and a new electromagnetic component design that solved these problems was proposed. Detailed dimensions of proposed design were obtained through the design of experiments procedure. Simulation results show that the final model exhibited sufficient performance to satisfy the required specification.     

Learning to use a word-processing system as a function of training strategy

Three training strategies were evaluated for their effectiveness in teaching naive computer users to use a word-processing system. One hundred and thirty five women ranging in age from 25 years to 70 years participated in the study. Subjects were trained using one of three techniques (instructor, manual, computer( to perform basic word-processing tasks. The effectiveness of the training strategies was assessed by examining performance on basic word-processing tasks such as typing a letter or memo and editing an existing file. Results showed that for all subjects, computer-based training was a less effective teaching method than either instructor- or manual-based training. In general, subjects who were trained using the computer-based method attempted and completed fewer tasks, took longer to perform tasks, and also made more errors. These findings demonstrate the need for directing efforts towards the development of appropriate training methods for computer tasks