Information problem solving by experts and novices: analysis of a complex cognitive skill

In (higher) education students are often faced with information problems: tasks or assignments that require them to identify information needs, locate corresponding information sources, extract and organize relevant information from each source, and synthesize information from a variety of sources. It is often assumed that students master this complex cognitive skill of information problem solving all by themselves. In our point of view, however, explicit and intensive instruction is necessary. A skill decomposition is needed in order to design instruction that fosters the development of information problem solving. This research analyzes the information problem solving process of novices and experts in order to reach a detailed skill decomposition. Results reveal that experts spend more time on the main skill ‘define problem’ and more often activate their prior knowledge, elaborate on the content, and regulate their process. Furthermore, experts and novices show little differences in the way they search the Internet. These findings formed the basis for formulating instructional guidelines.     

The effect of the timing of instructional support in a computer-supported problem-solving program for students in secondary physics education

Many students experience difficulties in solving applied physics problems. Researchers claim that the development of strategic knowledge (analyze, explore, plan, implement, verify) is just as necessary for solving problems as the development of content knowledge. In order to improve these problem-solving skills, it might be profitable to know at what time during problem solving is the use of instructional support most effective: before, during or after problem solving.     

A conceptual framework mapping the application of information search strategies to well and ill-structured problem solving

Problem-based learning (PBL) is an instructional approach that is organized around the investigation and resolution of problems. Problems are neither uniform nor similar. 11 and 12 in his design theory of problem solving has categorized problems into two broad types – well-structured and ill-structured. He has also described a host of mediating skills that impact problem solving outcomes. The thrust of this paper rests in the argument that the basis of these mediating skills is information search literacy and particularly, in view of the utility of the Internet as an informational repository, effective information searching skills. This study was an investigation of how different Internet information seeking strategies can be used to engage in problem solving. A conceptual framework that explains how different Internet information searching strategies can be employed in successfully solving well and ill-structured problems was devised and empirically tested. The research site was a newly established polytechnic in Singapore that employs problem-based learning to support its curricular implementation. The sample population of students came from a class of 25 first-year students. The research findings of this study inform that information searching skills indeed play an important role in problem solving. The findings affirm the need for students to be systematically instructed in the skills of information searching to be able to accomplish problem solving. The information searching necessary for solving well-structured problems is constrained and readily manageable. Thus, students only have to be acquainted with fundamental information searching skills to solve well-structured problems. On the other hand, the information needs of ill-structured problems are usually complex, multi-disciplinary and expansive. Hence, students have to be trained to apply a more advanced set of information searching skills in resolving ill-structured problems.     

PBL教学法在课程教学中的教学研究——以《信息系统项目管理》课程教学为例

PBL教学法是一种以学生为主体、问题为导向的教学方法。该文意在《信息系统项目管理》课程教学中引入该教学法,从而提高该课程的授课质量,提升教学水平,使学生更加积极、主动地参与学习,提高学生的动手能力、理论与实践的综合应用能力、分析解决问题的能力,使学生能提高利用计算机来解决现实生活中遇到实际问题的能力,即使走上工作岗位以后也可以利用所学的计算机知识解决专业领域遇到的问题,也能使该课程更好地围绕计算机信息管理专业。     

Development and evaluation of multimedia whiteboard system for improving mathematical problem solving

This study developed a web-based multimedia whiteboard system to help students learning with mathematical problem solving. The purpose is to promote a new online mathematical learning model that students not only use electronic whiteboard to write down their mathematical problem solving solutions but also use voice recording tool to give oral explanations about their thinking behind the solutions. To cultivate students’ critical thinking capability and encourage collaborative peer learning, the new learning model also requests students to criticize others’ solutions and reply to others’ arguments. With the multimedia supporting tools, students can communicate easily with each other about what they think and how they solve mathematical problems. We have conducted an experiment with sixth grade primary school students for evaluation. After the experiment, a questionnaire about students’ attitude toward the multimedia whiteboard system for math learning was then held. The results show that students were satisfied with the use of the multimedia whiteboard system for helping them with learning fractional division. Most students were interested in studying mathematics with the multimedia whiteboard system and thought this tool is particularly useful for doing collaborative learning. After analyzing the recorded solving processes and discussions content of students, we found that the performance of female students was superior to male students in communications and mathematical problem solving. Additionally, students with higher final exam grades had better mathematical abilities for doing critiques, arguments and communications.     

Fostering transfer of websearchers’ evaluation skills: A field test of two transfer theories

Transfer of complex cognitive skills is important when stimulating students to become life long learners. An example of a complex cognitive skill is the skill of evaluating results, information and source while solving information problems using the WWW. Especially the knowledge and use of evaluation criteria is crucial in this matter. Two educational programs to foster this skill in students were designed and evaluated. The programs were based on two different transfer theories. The first program was based on the theory that transfer of complex cognitive skills is fostered through the development of a rich knowledge structure. The second program is based on the theory that transfer is fostered by paying explicit attention to the various steps that have to be taken in a problem solving process. Effects of the two programs on increase in knowledge and use of criteria and degree of transfer were determined. Results show that both programs enable transfer.     

Problem solving techniques in cognitive science

For many years, researchers have tried to discover how humans solve problems. This research has answered many questions, but still many of them remain unanswered. However, knowledge gained in this field has greatly enhanced our understanding and has enabled us to design human-like intelligent systems. In the 1920s the Gestalt psychologists introduced a new field to cognitive science. They discovered that when presented with certain problems we use insight to reach a solution. In the 1950s Newell & Simon then brought the field of problem solving into the information age. They experimented with the idea of problem solving as a search for a solution in a state space. This technique is today utilized in the field of computing and Artificial Intelligence. This paper reviews techniques and looks at how we use previous experience gained by solving problems to solve new similar problems, making analogies between them. Experts’ performance during problem solving is compared to that of novices.     

Learning with intelligent tutors and worked examples: selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum

The main learning activity provided by intelligent tutoring systems is problem solving, although several recent projects investigated the effectiveness of combining problem solving with worked examples. Previous research has shown that learning from examples is an effective learning strategy, especially for novice learners. A worked example provides step-by-step explanations of how a problem is solved. Many studies have compared learning from examples to unsupported problem solving, and suggested presenting worked examples to students in the initial stages of learning, followed by problem solving once students have acquired enough knowledge. This paper presents a study in which we compare a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adapts learning tasks to students’ needs. The adaptive strategy determines the type of the task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received on the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problem solving. Novices from the adaptive condition learnt faster than novices from the control group, while the advanced students from the adaptive condition learnt more than their peers from the control group.     

What's going on behind the screens?

Since their introduction in 2010, much has been said and written about Apple's iPad (Apple Inc., Cupertino, CA, USA) and its potential to transform when and how students learn. Much of this hype has focused on attributes of the device such as its touch screen interface, light and portable form factor, easy‐to‐use operating system, and large array of apps. Emerging studies mainly report positive outcomes for students from using iPads in specific learning situations. These studies have cited enhanced motivation and learner engagement, often linking this with improved learning outcomes. However, there is a dearth of research exploring how students interact with these devices, and the factors that affect the quality and learning value from that interaction. This article reports on the use of an innovative recording solution to capture video and audio data of young students using iPads to develop literacy, numeracy and problem‐solving/decision‐making skills. The study offers insights into how students go about solving problems within apps, and highlights the importance of knowledge, affective and dispositional elements, and good app design to profitable interaction. It also flags considerations for teachers embarking on initiatives involving iPads, and suggests app developers need to work more closely with teachers to improve the learning value of their products.     

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.     

An autonomous multistrategy theorem proving system using knowledge-based techniques

Most general-purpose theorem-proving systems have weak search control. There is no alternative to the use of a large number of heuristics or strategies for search guidance. Choosing appropriate strategies for solving a given problem may require the knowledge of different strategies and may involve a lot of painstaking trial-and-error. To encourage the widespread use of computer reasoning systems, it is important that a theorem prover be usable by those with little knowledge of problem-solving strategies, and that a theorem prover be able to select good strategies for the user. An autonomous multistrategy theorem-proving system is developed, using knowledge-based techniques, to entirely free the user from the necessity of understanding the system or the merits of different strategies. All the user has to do is input his or her problem in first-order logic, and the system solves the problem efficiently for him or her without any manual intervention. The system embodies much of expert knowledge about how to solve problems. The knowledge is represented as metarules in knowledge base which guide a hyperlinking theorem prover to solve problems automatically and efficiently.     

How students evaluate information and sources when searching the World Wide Web for information

The World Wide Web (WWW) has become the biggest information source for students while solving information problems for school projects. Since anyone can post anything on the WWW, information is often unreliable or incomplete, and it is important to evaluate sources and information before using them. Earlier research has shown that students have difficulties with evaluating sources and information. This study investigates the criteria secondary educational students use while searching the Web for information. 23 students solved two information problems while thinking aloud. After completing the tasks they were interviewed in groups on their use of criteria. Results show that students do not evaluate results, source and information very often. The criteria students mention when asked which criteria are important for evaluating information are not always the same criteria they mention while solving the information problems. They mentioned more criteria but also admitted not always using these criteria while searching the Web.     

面向业务问题求解的知识资源特定领域建模方法

针对企业中解决业务问题时知识资源利用不合理的问题,提出一种面向业务问题求解的知识资源特定领域模型。首先从领域纲要出发,将业务问题求解领域的基本元素描述出来,并详细分析其中对象之间的关系;然后通过在领域模板中建立的问题-知识事件驱动过程链(PK-EPC)模型对问题求解的流程进行格式化,并匹配到相应的知识单元;其次是通过对应的应用模型对业务问题进行分解,并将前面的知识单元与知识载体匹配。根据前面的模型可得出可供企业选择的集成业务活动、知识单元和知识载体的多层次求解方案模型,为企业业务问题求解提供了快捷准确的方法。最后基于Java设计了面向业务问题求解的企业求解方案建模系统,通过实例验证了该模型的可行性。     

Knowledge sources for an intelligent algebra tutor

The focus of this paper is on the underlying knowledge base for an intelligent tutorial system for high-school algebra problems. We present a model of problem solving flexible enough to account for a variety of problem-solving behaviors and general enough to allow new problem domains to be defined easily. The model is based upon the analysis of protocols between students and expert tutors. We show how student errors can be monitored and remediated using the model, and we provide an approach to understanding problem difficulty that can be used to generate challenging problems and also provides a mechanism for planning their solution.     

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.In order to assess the constructs, we developed a virtual environment which allows students to solve interactive and static problems. 162 students of grade 10 and the upper secondary level completed the tests within a cross-sectional survey. In order to investigate the structure of problem-solving competency, we established measurement models representing different theoretical assumptions, and evaluated model fit statistics by using confirmatory factor analyses.Results show that problem-solving competency in virtual environments comprises to three correlated abilities: achieving a goal state, systematical handling of variables, and solving analytical tasks. Furthermore, our study provides empirical evidence on the distinction between analytical and complex problem solving.Additionally, we found significant differences between students of grades 10 and 12 within the problem-solving subscales, which could be explained by gaming experience and prior knowledge. These findings are discussed from a measurement perspective. Implications for assessing complex problem solving are given.     

Software maintenance seen as a knowledge management issue

Creating and maintaining software systems is a knowledge intensive task. One needs to have a good understanding of the application domain, the problem to solve and all its requirements, the software process used, technical details of the programming language(s), the system’s architecture and how the different parts fit together, how the system interacts with its environment, etc. All this knowledge is difficult and costly to gather. It is also difficult to store and usually lives only in the mind of the software engineers who worked on a particular project.If this is a problem for development of new software, it is even more for maintenance, when one must rediscover lost information of an abstract nature from legacy source code among a swarm of unrelated details.In this paper, we submit that this lack of knowledge is one of the prominent problems in software maintenance. To try to solve this problem, we adapted a knowledge extraction technique to the knowledge needs specific to software maintenance. We explain how we explicit the knowledge discovered on a legacy software during maintenance so that it may be recorded for future use. Some applications on industry maintenance projects are reported.     

A heuristic problem solver: George

Developing problem solving skills is an important educational objective in science teaching. In conventional applications of Computer-Assisted Learning, students are trained through tutorials, drill and practice, guided problems and simulation. One obstacle in the way of achieving a high degree of individualization is that the computer has been unable to answer questions for which answers have not been encoded. The use of artificial intelligence techniques and in particular of expert systems, may remove this obstacle. The program described here constitutes one step in this direction. The program is called GEORGE and has some of the essential characteristics of expert systems. It has been designed to find the solution to most problems of elementary chemistry dealing with mass, volume and number of moles. Contrary to conventional algorithmic programs which deal repeatedly with similar data and always process them in the same way, GEORGE uses heuristic rules to discover a solution to a problem. The program has no questions to offer students but shows users how to solve problems of their own. The heuristic rules are very simple and can be understood by students. In order to use this program, a student must be able to define precisely the quantity which is to be found and to identify the available data. If the available data are insufficient to support a solution, the program tells the user and asks for data relating to the missing information. If the data are sufficient, the program supplies the answer, but more important is that it explains how the answer was reached. Diagrams are used to show the network relating the available items of information to the solution. If the student finds some problems especially interesting. these problems can be saved to be used again or even modified for later use. Thus the student can create a personal collection of problems.This approach is different from traditional categories of Computer-Assisted Learning. The possible impact of creating and using problem solvers in science education is discussed.     

A GEP-based spatial decision support system for multisite land use allocation

Multisite Land Use Allocation Problem (MLUA) refers to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete (combinatorial optimization) problems. To cope with this type of problems, intelligent techniques such as genetic algorithms, and simulated annealing, have been used. Research in the area of Spatial Decision Support Systems (SDSS) for resource allocation issues, a new scientific area of information system applications developed to support semi-structured or unstructured spatial decisions, has recently generated attention for integrating Artificial Intelligence (AI) techniques with Geographic Information Systems (GIS). In this paper we demonstrate how GIS can be integrated with Gene Expression Programming (GEP), a recently developed AI approach, for solving MLUA problems. The feasibility of the proposed approach in solving MLUA problems was checked using a fictive case study. The results indicated that the proposed approach gives good and satisfactory results.     

Schoenfeld’s problem solving theory in a student controlled learning environment

This paper evaluates the effectiveness of a student controlled computer program for high school mathematics based on instruction principles derived from Schoenfeld’s theory of problem solving. The computer program allows students to choose problems and to make use of hints during different episodes of solving problems. Crucial episodes are: analyzing the problem, selecting appropriate mathematical knowledge, making a plan, carrying it out, and checking the answer against the question asked.     

Procedural versus content‐related hints for word problem solving: an exploratory study

For primary school students, mathematical word problems are often more difficult to solve than straightforward number problems. Word problems require reading and analysis skills, and in order to explain their situational contexts, the proper mathematical knowledge and number operations have to be selected. To improve students' ability in solving word problems, the problem solving process could be supported by procedural and content specific guidance or with only procedural support.. This paper evaluates the effect of two types of hints, procedural only and content‐procedural, provided by a computer programme presented in two versions. Students of grade 6 were randomly assigned to these two versions, which offered five lesson units consisting of eight word problems each. The results indicate that on average the students in the procedural‐content hints group (n = 54) finished about just as many problems in the programme as their counterparts in the procedural‐only condition (n = 51). However, the participants in the first group solved more problems correctly and improved their problem‐solving skills more as indicated by the scores on the problem‐solving post‐test. Apart from presenting our analysis of the findings of this study, also its limitations and its possible implications for future research are discussed in this paper.