New Views of Student Learning: Implications for Educational Measurement

Abstract

Recent research in cognitive psychology has drawn attention to the important role that students' personal understandings and representations of subject matter play in the learning process. This paper briefly reviews some of this research, and contrasts the kind of learning that results in an individual's changed conception or view of a phenomenon with the more passive, additive kind of learning assessed by most traditional achievement tests. To be consistent with a view of learning as an active, constructive process, educational tests are required which focus on key concepts in an area of learning, and which take into account the variety of types and levels of understanding that students have of those concepts. In these tests, scoring responses right and wrong is likely to be less appropriate than using students' answers to infer their levels of understanding. This will require not only imaginative new types of test items, but statistical models that permit inferences about students' understandings once their responses have been observed. Psychometric approaches are sketched to construct measures of achievement from such tests.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1991
Accession Number
ADA235285

Entities

People

  • Geofferey N. Masters
  • Robert J. Mislevy

Organizations

  • Educational Testing Service

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Achievement Tests
  • Classification
  • Cognition
  • Cognitive Science
  • Demographic Cohorts
  • Education
  • Educational Psychology
  • Learning
  • Measurement
  • Military Research
  • New York
  • Probability
  • Psychology
  • Reasoning
  • Social Sciences
  • Students
  • Thinking

Fields of Study

  • Education

Readers

  • Artificial Intelligence
  • Psychometric Testing or Psychological Assessment.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks