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Validation of automated scoring of science assessments
Author(s) -
Liu Ou Lydia,
Rios Joseph A.,
Heilman Michael,
Gerard Libby,
Linn Marcia C.
Publication year - 2016
Publication title -
journal of research in science teaching
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.067
H-Index - 131
eISSN - 1098-2736
pISSN - 0022-4308
DOI - 10.1002/tea.21299
Subject(s) - scoring system , computer science , science education , measure (data warehouse) , test (biology) , mathematics education , coherence (philosophical gambling strategy) , educational testing , artificial intelligence , machine learning , psychology , data mining , standardized test , statistics , mathematics , medicine , paleontology , surgery , biology
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. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 215–233, 2016.