Artificial Intelligence and the Future of Testing / Edition 1

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Overview

This volume consists of a series of essays written by experts, most of whom participated in a conference conducted by the Educational Testing Service to explore how current fields of artificial intelligence might contribute to ETS's plans to automate one or more of its testing activities.

The papers presented in Artificial Intelligence and the Future of Testing touch on a variety of topics including mathematics tutors, graph comprehension and computer vision, student reasoning and human accessing, modeling software design within a general problem-space architecture, memory organization and retrieval, and natural language systems. Also included: speculation on possible uses each AI specialty might have for a wide number of testing activities, and selective critical commentaries by two eminent AI researchers.

As Roy Freedle notes in his introduction, "We are at an exciting juncture in applying AI to testing activities." The essays presented in this collection convey some of that excitement, and represent an important step toward the merging of AI and testing — a powerful combination that has the potential to instruct and inspire.

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Product Details

  • ISBN-13: 9780805801170
  • Publisher: Taylor & Francis
  • Publication date: 7/1/1990
  • Edition number: 1
  • Pages: 344
  • Lexile: 1440L (what's this?)
  • Product dimensions: 6.00 (w) x 9.00 (h) x 1.00 (d)

Table of Contents

Contents: R. Freedle, Introduction: Artificial Intelligence and Its Implications for the Future of ETS's Tests. Part I:Assessment of Quantitative Skills. T. Ager, From Interactive Instruction to Interactive Testing. R. Milson, M. Lewis, J.R. Anderson, The Teacher's Apprentice Project: Building an Algebra Tutor. Part II:Graphs and Computer Vision: Selected Applications. S. Pinker, A Theory of Graph Comprehension. L. Kitchen, A Sketch of Accomplishments in Computer Vision with Speculations on Its Use in Educational Testing Part III:Learning, Memory, Reasoning and Language Issues. M. Burstein, B. Adelson, Issues for a Theory of Analogical Learning. B. Ross, The Access and Use of Relevant Information: A Specific Case and General Issues. B. Adelson, Modeling Software Design Within a Problem-Space Architecture. L. Rau, Memory Organization and Retrieval. P. Jacobs, Two Hurdles for Natural Language Systems. Part IV:Invited Critique. S. Amarel. A. Joshi. Part V:Toward the Future of Testing at ETS. R. Bennett, B. Gong, R. Kershaw, D. Rock, E. Soloway, A. Macalalad, Assessment of an Expert System's Ability to Automatically Grade and Diagnose Student's Constructed Responses to Computer Science Problems.

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