Abstract
Content experts conduct traditional approaches to developing rubrics for constructed response (CR) items, which is a labor-intensive process. We aim to illustrate the potential benefits of utilizing topic modeling techniques to improve the efficiency of the rubric workload. The results of this study reveal that over half of the keywords in the rubric corresponded with the top 20 words extracted from the supervised latent Dirichlet allocation (sLDA) model. The rubric-based scores assigned by human raters showed medium-to-high associations with the sLDA-predicted scores. The sLDA approach offers supporting evidence for refining the rubric concerning criteria for word usage and score assignments. It accomplishes this by concurrently incorporating the predetermined rubric of content experts and the actual responses provided by students.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
