Developing a Framework to Understand Teachers\u27 Self-efficacy in the Teaching of Writing

The purpose of this study was to explore the ways in which the domain specific self-efficacies manifest themselves in the teaching of writing. A teachers\u27 sense of efficacy is a multidimensional construct and is a significant construct in how teachers\u27 view themselves as writers and teachers of writing. This study explored how five elementary classroom teachers viewed these domain specific self-efficacies and the ways in which the various sources of efficacy influenced their self-efficacy as writers and teachers of writing. The researcher collected and analyzed domain specific self-efficacy scales and individual interviews to capture the experiences of these teachers. There was variability across and within the domain specific self-efficacy scales. The most salient source of efficacy as a writer and teacher of writing that emerged was physiological arousals

Pulsar Algorithms: A Class of Coarse-Grain Parallel Nonlinear Optimization Algorithms

Parallel architectures of modern computers formed of processors with high computing power motivate the search for new approaches to basic computational algorithms. Another motivating force for parallelization of algorithms has been the need to solve very large scale or complex problems. However, the complexity of a mathematical programming problem is not necessarily due to its scale or dimension; thus, we should search also for new parallel computation approaches to problems that might have a moderate size but are difficult for other reasons. One of such approaches might be coarse-grained parallelization based on a parametric imbedding of an algorithm and on an allocation of resulting algorithmic phases and variants to many processors with suitable coordination of data obtained that way. Each processor performs then a phase of the algorithm -- a substantial computational task which mitigates the problems related to data transmission and coordination. The paper presents a class of such coarse-grained parallel algorithms for unconstrained nonlinear optimization, called pulsar algorithms since the approximations of an optimal solution alternatively increase and reduce their spread in subsequent iterations. The main algorithmic phase of an algorithm of this class might be either a directional search or a restricted step determination in a trust region method. This class is exemplified by a modified, parallel Newton-type algorithm and a parallel rank-one variable metric algorithm. In the latter case, a consistent approximation of the inverse of the hessian matrix based on parallel produced data is available at each iteration, while the known deficiencies of a rank-one variable metric are suppressed by a parallel implementation. Additionally, pulsar algorithms might use a parametric imbedding into a family of regularized problems in order to counteract possible effects of ill-conditioning. Such parallel algorithms result not only in an increased speed of solving a problem but also in an increased robustness with respect to various sources of complexity of the problem. Necessary theoretical foundations, outlines of various variants of parallel algorithms and the results of preliminary tests are presented

IAC-DIDAS-N - A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models, v.4.0

This paper presents introductive and user documentation -- including extended summary, theoretical manual, short user manual and description of illustrative examples -- for a version of decision analysis and support systems of DIDAS family that is designed for multicriteria analysis of nonlinear models on professional microcomputers. This version has been developed in the years 1986-1990 in the Institute of Automatic Control, Warsaw University of Technology, under a joint research program with the Systems and Decision Sciences Program of IIASA. It can be run on professional microcomputers compatible with IBM-PC-XT or AT (with Hercules Graphics Card, Color Graphics Adapter or Enhanced Graphics Adapter and, preferably, with a numeric coprocessor and a hard disk) and supports graphical representation of results of interactive multicriteria analysis. Moreover, this version called IAC-DIDAS-N is provided with a new nonlinear model generator and editor that support, in an easy standard of a spreadsheet, the definition, edition and symbolic differentiation of nonlinear substantive models for multiobjective decision analysis. A specially introduced standard of defining nonlinear programming models for multiobjective optimization helps to connect the model generator with other parts of the system. Optimization runs involved in interactive, multiobjective decision analysis are performed by a solver, that is, a version of nonlinear programming algorithm specially adapted for multiobjective problems. This algorithm is based on shifted penalty functions and projected conjugate directions techniques similarly as in former nonlinear versions of DIDAS, but it was further developed and several improvements were added. The system is permanently updated and developed. Currently (starting from October 1990) the version 4.0 of the system is released. Most of enhancements added in this version are not directly visible to the user. They influence the efficiency of the system

The Use of CPTU and DMT Methods to Determine Soil Deformation Moduli - Perspectives and Limitations

publishedVersio

IAC-DIDAS-L Dynamic Interactive Decision Analysis and Support System Linear Version

This paper is one of the series of 11 Working Papers presenting the software for interactive decision support and software tools for developing decision support systems. These products constitute the outcome of the contracted study agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions. The theoretical part of these results is presented in the IIASA Working Paper WP-88-071 entitled "Theory, Software and Testing Examples in Decision Support Systems". This volume contains the theoretical and methodological backgrounds of the software systems developed within the project. This paper presents user documentation for two versions of decision analysis and support systems of DIDAS family: IAC-DIDAS-L1 (pilot version) and IAC-DIDAS-L2. These programs can be used for supporting decision problems when the model of the decision situation can be described using the linear programming framework

Radiographic characterization and energy spectrum of the IoGold 125I source Model 3631‐A

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135057/1/mp8543.pd

IAC-DIDAS-N: A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models with Nonlinear Model Generator Supporting Model Analysis

This paper is one of the series of 11 Working Papers presenting the software for interactive decision support and software tools for developing decision support systems. These products constitute the outcome of the contracted study agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions. The theoretical part of these results is presented in the IIASA Working Paper WP-88-071 entitled "Theory, Software and Testing Examples in Decision Support Systems". This volume contains the theoretical and methodological backgrounds of the software systems developed within the project. This paper presents the user documentation for decision analysis and support systems of DIDAS family designed for supporting decision problems when the model of the system under study can be formulated in terms of set of nonlinear equations. The program presented in the paper, called IAC-DIDAS-N is provided with a nonlinear model generator and editor that support definition, edition and symbolic differentiation of nonlinear models for multiobjective decision analysis. A specially introduced standard of defining nonlinear programming models for multiobjective optimization helps to connect the model generator with other parts of the system. Optimization runs involved in interactive, multiobjective decision analysis are performed by a new version of nonlinear programming algorithm specially adapted for multiobjective problems. This algorithm is based on shifted penalty functions and projected conjugate directions techniques. An attachment to this paper presents user documentation for a pilot version of a nonlinear model generator with facilities for symbolic differentiation and other means of fundamental model analysis