Problem-solving Environment Research Articles

CONVERGENCE OF DATA SOURCES IN THE ANALYSIS OF COMPLEX LEARNING ENVIRONMENTS

Learning in technology mediated learning environments is a complex process that varies across individual and group contexts. Complex learning environments that are mediated by technology require distinct concurrent methodologies that reveal when and where learning may occur. This paper describes the analysis of two technology-mediated problem-solving environments, one that uses concurrent methods to identify expertise, and the other that examines the influence of technology in a collaborative learning situation. The first example examines individual problem solving in the context of a stand-alone environment, BioWorld, whereas the second example examines the joint production of medical decisions with traditional and interactive whiteboard technology in a medical classroom. These examples demonstrate how concurrent methods add to our understanding of individual learning as well as the co-construction of knowledge in the context of clinical reasoning using technology.

Environ: integrating VR and CAD in engineering projects.

Environ plays an important part in a collaborative problem-solving environment tailored to assist the control and execution of large, complex petroleum engineering projects. To aid collaboration, Environ provides a scripting layer for executing commands (locally and remotely) and resources such as annotation of engineering artifacts and synchronized navigation control. Although Environ's performance is satisfactory for numerous CAD models, its performance deserves more attention, especially because we designed it to run on desktop computers and to visualize CAD models of increasing complexity. As we mentioned before, no commercial or academic solution has completely solved the problems of CAD-VR integration. This fact restricts, but hasn't stopped, the industry's increasing use of VR systems. Meanwhile, our research related to Environ, as well as other researchers' work, is providing valuable contributions to the search for effective solutions.

A problem-solving environment for data assimilation in air quality modelling

A generic toolbox for data assimilation called COSTA (COmmon Set of Tools for the Assimilation of data) makes it possible to simplify the application of data assimilation to models and to try out various methods for a particular model. Concepts of object oriented programming are used to define building blocks for data assimilation systems that can be exchanged and reused. The main building blocks in COSTA are the model component and the stochastic observer component. These components can be created by wrapping existing code. The LOTOS–EUROS air quality model will be used for operational smog and aerosol forecasts in the Netherlands in the near future. The COSTA framework will be used in this operational environment to implement the data assimilation techniques. As a first steps towards this operational system the model component of LOTOS–EUROS is created and the performance of various Kalman filter based data assimilation techniques are compared for a real live case study. The ensemble Kalman filter and the ensemble square-root filters converged faster than the other tested techniques for the selected case study setup.

Uniqueness and Stability of Action Potential Models during Rest, Pacing, and Conduction Using Problem-Solving Environment

Development and application of physiologically detailed dynamic models of the action potential (AP) and Ca 2+ cycling in cardiac cells is a rapidly growing aspect of computational cardiac electrophysiology. Given the large scale of the nonlinear system involved, questions were recently raised regarding reproducibility, numerical stability, and uniqueness of model solutions, as well as ability of the model to simulate AP propagation in multicellular configurations. To address these issues, we reexamined ventricular models of myocyte AP developed in our laboratory with the following results. 1), Recognizing that the model involves a system of differential-algebraic equations, a procedure is developed for estimating consistent initial conditions that insure uniqueness and stability of the solution. 2), Model parameters that can be used to modify these initial conditions according to experimental values are identified. 3), A convergence criterion for steady-state solution is defined based on tracking the incremental contribution of each ion species to the membrane voltage. 4), Singularities in state variable formulations are removed analytically. 5), A biphasic current stimulus is implemented to completely eliminate stimulus artifact during long-term pacing over a broad range of frequencies. 6), Using the AP computed based on 1–5 above, an efficient scheme is developed for computing propagation in multicellular models.

The expertise reversal effect and worked examples in tutored problem solving

Prior research has shown that tutored problem solving with intelligent software tutors is an effective instructional method, and that worked examples are an effective complement to this kind of tutored problem solving. The work on the expertise reversal effect suggests that it is desirable to tailor the fading of worked examples to individual students’ growing expertise levels. One lab and one classroom experiment were conducted to investigate whether adaptively fading worked examples in a tutored problem-solving environment can lead to higher learning gains. Both studies compared a standard Cognitive Tutor with two example-enhanced versions, in which the fading of worked examples occurred either in a fixed manner or in a manner adaptive to individual students’ understanding of the examples. Both experiments provide evidence of improved learning results from adaptive fading over fixed fading over problem solving. We discuss how to further optimize the fading procedure matching each individual student’s changing knowledge level.

A drawing and multi-representational computer environment for beginners’ learning of programming using C: Design and pilot formative evaluation

This paper presents both the design and the pilot formative evaluation study of a computer-based problem-solving environment (named LECGO: Learning Environment for programming using C using Geometrical Objects) for the learning of computer programming using C by beginners. In its design, constructivist and social learning theories were taken into account. The general design has taken into consideration models of the learning process and subject matter as well as potential learner behaviour in dealing with fundamental tasks. The main emphasis has been placed on the role of: (a) multiple external representations in student learning, (b) motivation, through performing problem-solving activities taken from the familiar and meaningful context of drawing, using simple geometrical objects, (c) the active participation of students in their own learning by using hands-on experience, (d) appropriate feedback on the actions taken by students, to aid their self-correction, and (e) holistic, activity-based, multi-media, multi-representational and multi-layered content for the learning of basic concepts of programming using C. LECGO was pilot evaluated in the field through a qualitative and comparative study where nine 12th grade (18-year-old) students participated. In fact, students faced three similar yet not identical sets of four tasks across three learning environments, namely; paper and pencil (p–p), Turbo C and LECGO. The data emerging from this field evaluation study indicates that students gain better results within LECGO than in both the p–p environment and the typical programming environment of Turbo C, while performing similar activities.

Business-oriented service modeling: A case study

Based on our hand-on experiences in developing a service-based problem-solving environment for bioinformatics research, we present in this paper a particular approach called VINCA4Science to business-oriented modeling of service functionalities, to Web services virtualization and to user-centric utilization of modeled artifacts. Some practical effects regarding the improvement of domain stability, efficiency and scalability are illustrated.

Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples

Many simulation studies in biomedicine are based on a similar sequence of processing steps, starting from images and running through geometric model generation, assignment of tissue properties, numerical simulation and visualization of the results—a process known as image-based geometric modelling and simulation. We present an overview of software systems for implementing such a sequence both within highly integrated problem-solving environments and in the form of loosely integrated pipelines. Loose integration in this case indicates that individual programs function largely independently but communicate through files of a common format and support simple scripting, so as to automate multiple executions wherever possible. We then describe three specific applications of such pipelines to translational biomedical research in electrophysiology.

Towards a web-based progressive handwriting recognition environment for mathematical problem solving

The emergence of pen-based mobile devices such as PDAs and tablet PCs provides a new way to input mathematical expressions to computer by using handwriting which is much more natural and efficient for entering mathematics. This paper proposes a web-based handwriting mathematics system, called WebMath, for supporting mathematical problem solving. The proposed WebMath system is based on client–server architecture. It comprises four major components: a standard web server, handwriting mathematical expression editor, computation engine and web browser with Ajax-based communicator. The handwriting mathematical expression editor adopts a progressive recognition approach for dynamic recognition of handwritten mathematical expressions. The computation engine supports mathematical functions such as algebraic simplification and factorization, and integration and differentiation. The web browser provides a user-friendly interface for accessing the system using advanced Ajax-based communication. In this paper, we describe the different components of the WebMath system and its performance analysis.

Fraction Photo Frenzy: A New Exploration

Using technology to expand opportunities to share their thinking in a problem-solving environment, these preservice teachers discover exciting possibilities for sharing mathematical reasoning in elementary school classrooms.

Building problem-solving environments with the Arches framework

The computational problems that scientists face are rapidly escalating in size and scope. Moreover, the computer systems used to solve these problems are becoming significantly more complex than the familiar, well-understood sequential model on their desktops. While it is possible to re-train scientists to use emerging high-performance computing (HPC) models, it is much more effective to provide them with a higher-level programming environment that has been specialized to their particular domain. By fostering interaction between HPC specialists and the domain scientists, problem-solving environments (PSEs) provide a collaborative environment. A PSE environment allows scientists to focus on expressing their computational problem while the PSE and associated tools support mapping that domain-specific problem to a high-performance computing system. This article describes Arches, an object-oriented framework for building domain-specific PSEs. The framework was designed to support a wide range of problem domains and to be extensible to support very different high-performance computing targets. To demonstrate this flexibility, two PSEs have been developed from the Arches framework to solve problem in two different domains and target very different computing platforms. The Coven PSE supports parallel applications that require large-scale parallelism found in cost-effective Beowulf clusters. In contrast, RCADE targets FPGA-based reconfigurable computing and was originally designed to aid NASA Earth scientists studying satellite instrument data.

Breaking with Tradition: Preparing Faculty to Teach in a Student-Centered or Problem-Solving Environment

This article views all educational approaches as either traditional (teacher-centered) or non-traditional (learner-centered), with not much emphasis placed on differentiating between the non-traditional models. The purpose of this paper is to describe the impediments to changing one's approach to teaching and some strategies to overcome those impediments. We will also focus our attention on those educators who seek to change from a traditional approach to a more non-traditional approach to teaching. The three main sources of resistance described are the students, the administration, and the individual attempting the change. The rest of the paper describes strategies and actions to deal with the resistances encountered.

A blended E-learning experience in a course of object oriented programming fundamentals

In this paper, we present a blended e-learning experience consisting of supplying an undergraduate student population (in addition to traditional on-site classes) with a learning tool called OOPS (Object Oriented Programming System) and a testing system called SIETTE. OOPS is a problem-solving environment in which students can resolve Object Oriented Programming exercises. The system applies an assessment for learning strategy where students are formatively assessed, i.e. OOPS diagnoses their knowledge level but also generates feedback and hints to help students to understand and overcome their misconceptions and to reinforce correctly learnt concepts. In conjunction with OOPS, we have used SIETTE, a web-based assessment system in which students can take tests and teachers can construct them Subsequently, we have explored whether or not the use of OOPS contributes to improve the students’ knowledge about Object Oriented Programming.

A Problem Solving Environment Portal for Multidisciplinary Design Optimization

Multidisciplinary Design Optimization (MDO) is a design methodology that derives optimal design solutions by concurrently considering various mutually dependent design elements from an assortment of disciplines. As such, it is applicable to the designing of ships and automobiles, as well as to aero vehicles. However, applying MDO methodologies in the real world would require a designer to spend an enormous amount of time arranging and integrating resources used in the process. This paper proposes a Problem Solving Environment (PSE) Portal for MDO methodologies, providing an environment that enables designers to utilize design resources conveniently even without working knowledge of the systems. Furthermore, the PSE portal yields an optimal MDO environment by allowing for global collaborative sites, which securely share design resources, and by offering users an efficient interface.

MEDITOMO: a high performance software package for 3D SPECT imaging

We describe a parallel software library, named MEDITOMO, designed for processing MEDIcal images obtained by SPECT (single photon emission computed tomography) TOMOgraphic systems. MEDITOMO is the core library of the PSE (problem solving environment) MEDIGRID, oriented to medical imaging analysis, which the authors are currently developing. MEDIGRID is employed in a Grid-computing infrastructure involving clinical departments and research institutes. The algorithms of MEDITOMO are the standard ones that are usually applied in the SPECT analysis, i.e. the conjugate gradient and the expectation maximization. The main contribution of this work concerns the introduction of the total variation seminorm as the edge-preserving regularization in both algorithms and the development of the parallel software library. Experiments carried out on synthetic and clinical data are shown.

Special Issue Devoted to Professor Ian Gladwell

Special Issue Devoted to Professor Ian Gladwell

The Use of Problem-Solving Therapy and Restraint-Free Environment for the Management of Depression and Agitation in Long-Term Care

We demonstrate the use of behavioral techniques with older adults in long-term care settings, using two case examples. Ms. N is an 81-year-old woman with mild cognitive impairment who attended 12 sessions of problem-solving therapy for ongoing depression. Her Montgomery Asberg Depression Rating Scale score of 30 (moderate levels of depression) decreased to 6 (not depressed) following treatment. Mr. K is a 77-year-old male with moderate to severe Alzheimer's disease who was referred for treatment of behavioral problems. Following 10 weeks of restraint free environment, his Cohen Mansfield Agitation Inventory score declined from 60 (moderate behavioral problems) to 38 (minimal problem behaviors), indicating a significant reduction in agitation. Through these two cases, we provide a description of behavioral interventions for the treatment of depression and behavioral problems in long-term care settings.

Hexahedral mesh generation for biomedical models in SCIRun

Biomedical simulations are often dependent on numerical approximation methods, including finite element, finite difference, and finite volume methods, to model the varied phenomena of interest. An important requirement of the numerical approximation methods above is the need to create a discrete decomposition of the model geometry into a ‘mesh’. Historically, the generation of these meshes has been a critical bottleneck in efforts to efficiently generate biomedical simulations which can be utilized in understanding, planning, and diagnosing biomedical conditions. In this paper we discuss a methodology for generating hexahedral meshes for biomedical models using an algorithm implemented in the SCIRun Problem Solving Environment. The method is flexible and can be utilized to build up conformal hexahedral meshes ranging from models defined by single isosurfaces to more complex geometries with multi-surface boundaries.

Performance Study of GasTurbnLab, an Agent-Based Multi-Physics Problem Solving Environment for the Gas Turbine Engine Simulation

Multiphysics applications are real world problems with a large number of different shape components that obey different physical laws and manufacturing constraints and interact with each other through geometric and physical interfaces. They demand accurate and efficient solutions and a modern type of computational modeling, which designs the whole physical system with as much detail as possible. The simulation of gas turbine engine is such a multiphysics application and is realized with GasTurbnLab, an agent-based Multiphysics Problem Solving Environment (MPSE). Its performance and evaluation study is presented in this paper. For this, a short description of the software components and hardware infrastructure is given. The performance and the scalability of the parallelism are depicted, and the communication overhead between agents is studied with respect to the number of agents and their location in the “computational grid.” The execution time is recorded, and its analysis verifies the complexity of the solvers in use and the performance of the available hardware. Three different clusters of INTEL Pentium processors were used for experimentation to study how the communication time was affected by processor’s homogeneity/heterogeneity and the different connections between the processors. The study of the numerical experiments shows that the domain decomposition and interface relaxation methodology, along with the usage of agent platforms, does not increase the complexity of the simulation problem, and the communication cost is too low, compared with the computations, to reflect on the total simulation time. Therefore, GasTurbnLab is an efficient example of a complex physical phenomena simulation.

Extending Grid-Based Workflow Tools With Patterns/Operators

Many grid applications involve combining computational and data access components into complex workflows. A distinction is generally made between mechanisms to compose components (referred to as build-time functions) and subsequent mechanisms to execute these components on distributed resources (referred to as run-time functions). An approach to supporting such build- and run-time functions using specialist patterns and operators is presented. “Structural” patterns may be treated as meta-components within a workflow system, and used within the composition process. Subsequently, such components may be scheduled for execution using “behavioral” patterns via the enactment process. Application examples are presented to demonstrate how such patterns — and subsequently operators — may be used. Their implementation within the Triana Problem Solving Environment is also described.