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Lagrangian relaxation for constrained curve-fitting with binary variables: Applications in educational testing.

机译:拉格朗日松弛法用于带有二元变量的约束曲线拟合:在教育测试中的应用。

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摘要

This dissertation offers a mathematical programming approach to curve fitting with binary variables. Various Lagrangian Relaxation (LR) techniques are applied to constrained curve fitting. Applications in educational testing with respect to test assembly are utilized. In particular, techniques are applied to both static exams (i.e. conventional paper-and-pencil (P&P)) and adaptive exams (i.e. a hybrid computerized adaptive test (CAT) called a multiple-forms structure (MFS)). This dissertation focuses on the development of mathematical models to represent these test assembly problems as constrained curve-fitting problems with binary variables and solution techniques for the test development.; Mathematical programming techniques are used to generate parallel test forms with item characteristics based on item response theory. A binary variable is used to represent whether or not an item is present on a form. The problem of creating a test form is modeled as a network flow problem with additional constraints. In order to meet the target information and the test characteristic curves, a Lagrangian relaxation heuristic is applied to the problem. The Lagrangian approach works by multiplying the constraint by a "Lagrange multiplier" and adding it to the objective. By systematically varying the multiplier, the test form curves approach the targets.; This dissertation explores modifications to Lagrangian Relaxation as it is applied to the classical paper-and-pencil exams. For the P&P exams, LR techniques are also utilized to include additional practical constraints to the network problem, which limit the item selection.; An MFS is a type of a computerized adaptive test. It is a hybrid of a standard CAT and a P&P exam. The concept of an MFS will be introduced in this dissertation, as well as, the application of LR as it is applied to constructing parallel MFSs.; The approach is applied to the Law School Admission Test for the assembly of the conventional P&P test as well as an experimental computerized test using MFSs.
机译:为二进制变量的曲线拟合提供了一种数学编程方法。各种拉格朗日松弛(LR)技术应用于约束曲线拟合。利用了关于测试组装的教育测试中的应用。尤其是,技术既适用于静态考试(即常规的纸笔(P&P))又适用于适应性考试(即称为多形式结构(MFS)的混合计算机适应性考试(CAT))。本文着重于数学模型的开发,以将这些测试装配问题表示为带有二进制变量的约束曲线拟合问题和用于测试开发的求解技术。基于项目响应理论,使用数学编程技术来生成具有项目特征的并行测试表格。二进制变量用于表示表单上是否存在项目。创建测试表单的问题被建模为具有附加约束的网络流问题。为了满足目标信息和测试特性曲线,将拉格朗日松弛试探法应用于该问题。拉格朗日方法是通过将约束乘以“拉格朗日乘数”并将其添加到目标来工作的。通过系统地改变乘数,测试表格曲线接近目标。本文探讨了拉格朗日松弛法在经典纸笔考试中的改进。对于P&P考试,LR技术还被用来包括对网络问题的其他实际限制,这限制了项目的选择。 MFS是计算机自适应测试的一种。它是标准CAT和P&P考试的混合体。本文将介绍MFS的概念,以及LR在构建并行MFS中的应用。该方法适用于法学院入学考试,以组装常规P&P考试以及使用MFS进行的实验性计算机化考试。

著录项

  • 作者

    Berliner Koppel, Nicole.;

  • 作者单位

    Rutgers The State University of New Jersey - Newark.;

  • 授予单位 Rutgers The State University of New Jersey - Newark.;
  • 学科 Business Administration Management.; Operations Research.; Education Tests and Measurements.; Psychology Psychometrics.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 124 p.
  • 总页数 124
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 贸易经济;运筹学;教育;心理学研究方法;
  • 关键词

  • 入库时间 2022-08-17 11:47:46

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