Journal
JOURNAL OF RESEARCH IN SCIENCE TEACHING
Volume 53, Issue 2, Pages 215-233Publisher
WILEY
DOI: 10.1002/tea.21299
Keywords
automated scoring; c-rater-ML; science assessment
Categories
Funding
- National Science Foundation [1119670]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1451604] Funding Source: National Science Foundation
- Division Of Research On Learning
- Direct For Education and Human Resources [1418423, 1119670] Funding Source: National Science Foundation
Ask authors/readers for more resources
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. (c) 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 215-233, 2016.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available