Dissertation and Abstract
Development and Multi-Tiered Analysis of a Socio-scientific Reasoning Assessment: Application of Computer Automated Scoring and Rasch Analysis
Scientific literacy (SL) as an idea is considered by many to be one of the main goals of science education. This study will focus on Roberts’ (2007) second vision of SL, the ability of students to competently navigate “situations with a scientific component…” (p. 730). Assessments centered on Vision-II-SL could be refined to better capture student’s competency. To this end, this study focuses on the development of an open-ended Vision-II aligned assessment (QuASSR-oe2). To make this assessment usable in large populations this study utilizes automated scoring, which is the synergy of natural-language-processing and supervised-machine-learning.
This study hopes to establish the validity and reliability of the QuASSR-oe2 using psychometric techniques from classical-test theory and Rasch analysis. The study has the potential to show that accurate automated-scoring models can be generated, allowing for efficient scoring of open-ended student responses. Ultimately, this allows for the QuASSR-oe2 to be easily utilized in large populations.
Advisor: Troy Sadler
Expected graduation date: May 2019