Scaffolding online argumentation during problem solving

Abstract  In this study, constraint-based argumentation scaffolding was proposed to facilitate online argumentation performance and ill-structured problem solving during online discussions. In addition, epistemological beliefs were presumed to play a role in solving ill-structured diagnosis–solution problems. Constraint-based discussion boards were implemented to scaffold pre-service teachers' online discussions about behaviour management (diagnosis–solution) problems. The scaffolded discussion group generated more evidence notes and also generated more hypothesis messages and hypothesis testing messages as well as problem space construction messages. There was a relationship between epistemological beliefs and ill-structured problem solving. Simple knowledge, omniscient authority, and fixed ability significantly predicted problem-solving performance. A significant negative relationship between simple knowledge and individual problem-solving performance was found. This implies that individuals who believe in simple knowledge may be less inclined to explore more solution alternatives. However, contrary to prediction, omniscient authority and fixed ability beliefs were positively associated with problem-solving processes.     

Transfer of problem-solving strategy using Covlan

Psychological evidence suggests that humans use visual knowledge and reasoning in solving complex problems. We present Covlan, a visual knowledge representation language for representing visual knowledge and supporting visual reasoning. We describe Galatea, a computer program that uses Covlan for analogical transfer of problem-solving procedures from known analogs to new problems. We present the use of Galatea to model analogical visual problem solving by four human experimental participants, and describe one of the four cases in detail. The Galatea model of human problem solving suggests that problem-solving procedures can be effectively represented with Covlan.     

认知演化算法

受人类创造性思维过程的启发,借鉴认知心理学和创新计算的研究成果,提出了一种模拟人类基于创造性思维的问题求解过程和行为的智能算法--认知演化算法.该算法以知识为核心,将问题求解看成一个基于知识的创造性思维过程,对发散思维、收敛思维、记忆、执行、学习和价值体系6个关键模块进行了建模,充分发挥了知识演化和基于知识的创造性思维技巧在问题求解中的作用.通过数值实验分析了CEA各参数对算法性能的影响,并以一个扩展的路径优化问题将CEA与经典的智能算法进行了比较.实验结果表明,针对知识密集型优化问题,该算法能够以较少的目标评价次数得到问题的较优解.     

The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning

In this paper we compared the efficacy of face-to-face and computer supported collaborative learning (CSCL) in increasing academic knowledge and professional competences. We also explored how students’ personality characteristics and learning strategies and teachers’ characteristics were associated with better learning outcomes in online or face-to-face contexts. One hundred and seventy students participated in 10 community psychology seminars, five online and five face-to-face. Academic and professional learning increased for participants in both settings. Tutors’ characteristics did not influence students’ learning. Students who performed better in online and in face-to-face contexts differed in some psychological variables and in their learning strategies. Overall results show that asynchronous collaborative learning online can increase professional competences normally learnt only in small face-to-face educational settings, and that CSCL can be used to provide innovative educational opportunities that fit particular needs of students with low anxiety, high problem solving efficacy, who have time management problems in their learning strategies.     

A CONSTRAINED ARCHITECTURE FOR LEARNING AND PROBLEM SOLVING

This paper describes Eureka , a problem-solving architecture that operates under strong constraints on its memory and processes. Most significantly, Eureka does not assume free access to its entire long-term memory. That is, failures in problem solving may arise not only from missing knowledge, but from the (possibly temporary) inability to retrieve appropriate existing knowledge from memory. Additionally, the architecture does not include systematic backtracking to recover from fruitless search paths. These constraints significantly impact Eureka 's design. Humans are also subject to such constraints, but are able to overcome them to solve problems effectively. In Eureka 's design, we have attempted to minimize the number of additional architectural commitments, while staying faithful to the memory constraints. Even under such minimal commitments, Eureka provides a qualitative account of the primary types of learning reported in the literature on human problem solving. Further commitments to the architecture would refine the details in the model, but the approach we have taken de-emphasizes highly detailed modeling to get at general root causes of the observed regularities. Making minimal additional commitments to Eureka 's design strengthens the case that many regularities in human learning and problem solving are entailments of the need to handle imperfect memory.     

基于图划分抽样算法的图表示学习

在基于神经网络的图表示算法中,当节点属性维度过高、图的规模过大时,从内存到显存的数据传输会成为训练性能的瓶颈。针对这类问题,该方法将图划分算法应用于图表示学习中,降低了内存访问的I/O开销。该方法根据图节点的度数,将图划分成若干个块,使用显存缓存池存储若干个特征矩阵块。每一轮训练,使用缓存池中的特征矩阵块,以此来减少内存到显存的数据拷贝。针对这一思想,该方法使用基于图划分的抽样算法,设计显存的缓存池来降低内存的访问,运用多级负采样算法,降低训练中负样本采样的时间复杂度。在多个数据集上,与现有方法对比发现,该方法的下游机器学习准确率与原算法基本一致,训练效率可以提高2~ 7倍。实验结果表明,基于图划分的图表示学习能高效训练模型,同时保证节点表示向量的测试效果。今后的课题可以使用严谨的理论证明,阐明图划分模型与原模型的理论误差。     

ON KNOWLEDGE REPRESENTATION FOR INFERENCES IN CAST METHOD BANKS

This article explains why aspects of knowledge representation must be considered in the context of computer aided systems theory (CAST). CAST method banks support human experts during the process of problem solving. They should be understood as decision support systems, as assistants of their human expert users. One key to making this approach work is the communication between the expert and the system. The assistant should provide systematical and goal-directive information about the current problem state for the human expert. Another, even more important requirement is the assistant's knowledge about all available methods at a certain problem-solving state and their expected impact on the further problem-solving process. Knowledge representation denotes how the problem domain is represented within the support system and how it is used. We investigate different forms of knowledge representations and summarize criteria for the applicability of different forms of knowledge representations in CAST systems.     

A computational model for distributed knowledge systems with learning mechanisms

This paper addresses the issues of machine learning in distributed knowledge systems, which will consist of distributed software agents with problem solving, communication and learning functions. To develop such systems, we must analyze the roles of problem-solving and communication capabilities among knowledge systems. To facilitate the analyses, we propose a computational model: LPC. The model consists of a set of agents with (a) a knowledge base for learned concepts, (b) a knowledge base for problem solving, (c) prolog-based inference mechanisms and (d) a set of beliefs on the reliability of the other agents. Each agent can improve its own problem-solving capabilities by deductive learning from the given problems, by memory-based learning from communications between the agents and by reinforcement learning from the reliability of communications between the other agents. An experimental system of the model has been implemented in Prolog language on a Window-based personal computer. Intensive experiments have been carried out to examine the feasibility of the machine learning mechanisms of agents for problem-solving and communication capabilities. The experimental results have shown that the multiagent system improves the performance of the whole system in problem solving, when each agent has a higher learning ability or when an agent with a very high ability for problem solving joins the organization to cooperate with the other agents in problem solving. These results suggest that the proposed model is useful in analyzing the learning mechanisms applicable to distributed knowledge systems.     

Knowledge representation in human problem solving: implications for expert system design

The study focuses on the identification of the underlying representational properties of human problem solving and their application to expert systems. In this study the interaction between problem representation (procedural, conceptual, unstructured) and problem type (transformation, arrangement, inducing structure) was observed. The results of this study indicate partly that quantitative and qualitative differences in problem-solving performance can be attributed to the form of knowledge representation employed by the problem solver. It is suggested that modularized expert systems could be designed with different problem-solving modules organized by problem characteristics or type, exploiting the representational differences identified in this study.     

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.     

Advancing young adolescents’ hypothesis-development performance in a computer-supported and problem-based learning environment

Hypothesis development is a complex cognitive activity, but one that is critical as a means of reducing uncertainty during ill-structured problem solving. In this study, we examined the effect of metacognitive scaffolds in strengthening hypothesis development. We also examined the influence of hypothesis development on young adolescents’ problem-solving performance. Data was collected from sixth-grade students (N = 172) using a computer-supported problem-based learning environment, Animal Investigator. The findings of the study indicated that participants using metacognitive scaffolds developed significantly better hypotheses and that hypothesis-development performance was predictive of solution-development performance. This article discusses further educational implications of the findings and future research.     

Using animation to help students learn computer algorithms

This paper compares the effects of graphical study aids and animation on the problem-solving performance of students learning computer algorithms. Prior research has found inconsistent effects of animation on learning, and we believe this is partly attributable to animations not being designed to convey key information to learners. We performed an instructional analysis of the to-be-learned algorithms and designed the teaching materials based on that analysis. Participants studied stronger or weaker text-based information about the algorithm, and then some participants additionally studied still frames or an animation. Across 2 studies, learners who studied materials based on the instructional analysis tended to outperform other participants on both near and far transfer tasks. Animation also aided performance, particularly for participants who initially read the weaker text. These results suggest that animation might be added to curricula as a way of improving learning without needing revisions of existing texts and materials. Actual or potential applications of this research include the development of animations for learning complex systems as well as guidelines for determining when animations can aid learning.     

Planning in a hierarchy of abstraction spaces

A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem solver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representation of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further implications of the hierarchical planning approach are explored.     

Framework of knowledge-based systems : Multiple meta-level architecture for representing problems and problem-solving processes

Currently available expert systems have a performance limit because of the lack of capability to describe problems and problem-solving methods. It is closely related with knowledge representation language, but this is not the only concern with this issue. Real world problems and problem-solving methods are not so simple as to be represented always in the same way by the same language. Their representations must be different depending on various factors involved in the problems themselves and the situations these problems are surrounded with. In this paper, the author discusses first the intrinsic nature of problem representation and problem-solving process representation. The requirements for and the conceptual framework of a knowledge-based system that is suited for dealing with various problems then become apparent quite naturally. The author asserts that a multiple meta-level architecture is necessary as well as a knowledge-representation language that can describe complex data structures as the basic framework of knowledge-based systems.     

An approach to expert control of interactive software systems

Expert problem-solving strategies in many domains require the use of detailed mathematical techniques coupled with experiential knowledge about how and when to use the appropriate techniques. In many of these domains, such techniques are made available to experts in large software packages. In attempting to build expert systems for these domains, we wish to make use of these packages, and are therefore faced with an important problem: how to integrate the existing software, and knowledge about its use, into a practical expert system. The expert knowledge is used, in dynamic selection and interpretation of appropriate programs and parameters, to reach a successful goal in the problem solving. We describe the framework of a hybrid expert system for representing problem-solving knowledge in these domains. This hybrid system may be characterized as consisting of a production system and mathematical methods. The software package is reorganized as necessary to map it into the mathematical-method representation of a hybrid system. This approach has evolved out of an effort to build an expert system for performing well-log analysis, ELAS (expert log analysis system).     

记忆神经网络在机器人导航领域的应用与研究进展

记忆神经网络非常适合解决时间序列决策问题,将其用于机器人导航领域是非常有前景的新兴研究领域。本文主要讨论记忆神经网络在机器人导航领域的研究进展。给出几种基本记忆神经网络结合导航任务的工作机理,总结了不同模型的优缺点;对记忆神经网络在导航领域的研究进展进行简要综述;进一步介绍导航验证环境的发展;最后梳理了记忆神经网络在导航问题所面临的复杂性挑战,并预测了记忆神经网络在导航领域未来的发展方向。     

Noise and verbal or spatial solutions of Rubik's cube

Subjects were required to solve simple problems using a Rubik's cube, posed either spatially (a pictorial representation of the problem) or verbally (written instructions). Results showed that subjects classified on a pretest as spatially able performed better on the Rubik's cube than those classified as verbally able. In addition, verbal performance was better under 95 dBA than 70 dBA and spatial performance better under 70 dB A than under 95 dB A white noise. The results are considered in the light of the contradictory literature on the effects of noise on human memory, with particular reference to the adoption of problem-solving strategies in noise.     

The effects of text structure and prior knowledge of the learner on computer-based learning

This study is an attempt to investigate the effects of document structure and knowledge level of the reader on reading comprehension, browsing, and perceived control. Four types of texts are distinguished, differing in structure (linear text, hierarchical hypertext, mixed hypertext, and generative text). All the materials were on a PC. In all conditions, participants were allowed 1 h to read through the document. After completing the reading part of the experiment, they were asked to fill out the perceived control questionnaire followed by the reading comprehension test. As far as reading comprehension was concerned, knowledgeable participants had higher reading comprehension scores than non-knowledgeable participants only in the linear text. In addition, there were no significant differences in terms of the reading comprehension scores of the knowledgeable participants among the four topologies. However, the performance of non-knowledgeable participants differed with respect to the type of the topology. In particular, non-knowledgeable participants in the hierarchical and generative conditions performed better than those in the other two conditions. With respect to perceived control, the performance of knowledgeable and non-knowledgeable participants was equivalent in all four conditions. The results are discussed in terms of their implications for the computer-based learning.     

Effect of high-level content organizers on hypertext learning

This study investigates the cognitive abilities involved in hypertext learning and design approaches that can help users. We examined the effects of two types of high-level content organizers - a graphic spatial map and an alphabetical list - on readers’ memory for hypertext structure. In the control condition, a simple “home” page with no navigational aid was offered. Subjects were asked to read the hypertext with the purpose of learning the content, but in the post test phase they also had to recall the layout of nodes and links. Memory for links and page places varied as a function of condition. When a spatial map was available participants reconstructed more accurate formal structure then in the two other conditions. Participants’ memory about page places was the least accurate in the list condition. Results also indicate that participants use the content organizer when it is available in order to orientate during learning from hypertext documents.Our results prove that a content organizer showing the formal structure can facilitate the spatial mapping process. However, an organizer exposing a different structure than the real one would generate a conflict.     

Showing a model's eye movements in examples does not improve learning of problem-solving tasks

Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an open question whether EMME would also improve learning of procedural problem-solving tasks. We investigated this question in two experiments. In Experiment 1 (72 university students, M_age = 19.94), the effectiveness of EMME for learning simple geometry problems was addressed, in which the eye movements cued the underlying principle for calculating an angle. The only significant difference between the EMME and a no eye movement control condition was that participants in the EMME condition required less time for solving the transfer test problems. In Experiment 2 (68 university students, M_age = 21.12), we investigated the effectiveness of EMME for more complex geometry problems. Again, we found no significant effects on performance except for time spent on transfer test problems, although it was now in the opposite direction: participants who had studied EMME took longer to solve those items. These findings suggest that EMME may not be more effective than regular video examples for teaching procedural problem-solving skills.