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 language | English (US) |
|---|---|
| Pages (from-to) | 215-233 |
| Number of pages | 19 |
| Journal | Journal of Research in Science Teaching |
| Volume | 53 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2016 |
| Externally published | Yes |
Bibliographical note
Funding Information:National Science Foundation; Contract grant number: 1119670. This material is based upon work supported by the National Science Foundation under Grant No. 1119670. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2016 Wiley Periodicals, Inc.
Keywords
- automated scoring
- c-rater-ML
- science assessment