Validation of automated scoring of science assessments

Ou Lydia Liu, Joseph Rios, Michael Heilman, Libby Gerard, Marcia C. Linn

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Constructed response items can both measure the coherence of student ideas and serve as reflective experiences to strengthen instruction. We report on new automated scoring technologies that can reduce the cost and complexity of scoring constructed-response items. This study explored the accuracy of c-rater-ML, an automated scoring engine developed by Educational Testing Service, for scoring eight science inquiry items that require students to use evidence to explain complex phenomena. Automated scoring showed satisfactory agreement with human scoring for all test takers as well as specific subgroups. These findings suggest that c-rater-ML offers a promising solution to scoring constructed-response science items and has the potential to increase the use of these items in both instruction and assessment.

Original languageEnglish (US)
Pages (from-to)215-233
Number of pages19
JournalJournal of Research in Science Teaching
Volume53
Issue number2
DOIs
StatePublished - Feb 1 2016
Externally publishedYes

Keywords

  • automated scoring
  • c-rater-ML
  • science assessment

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