Announcement of Ph.D. in Education Dissertation Proposal Defense Jeremy Lee: “Using Student Log Files to Examine the Efficacy of Scafolds for Multilingual Learners’ Science Inquiry”

10:30 am - 12:30 pm

ABSTRACT: An increasing amount of research, coupled with policies such as the Next Generation Science Standards (NGSS) and WIDA Framework advocate for the integration of language and STEM learning. That is, multilingual learners (MLs) should be exposed to the authentic language used in the STEM areas while they are engaging in meaningful tasks, rather than being taught vocabulary and grammar that is removed from the context entirely. To meet these rigorous standards, researchers have attempted to provide accommodations such as glossaries and visual stimuli, and reduce any unnecessary linguistic complexity of materials. However, much of the extant research relies on coarse measures in the form of traditional multiple-choice tests to evaluate the efficacy of these types of accommodations. In recent years, students have been learning science in immersive and interactive computer-based learning environments, such as Inq-ITS, which generates assessments and log files of students’ actions as they conduct inquiry. Log files are data of students’ interactions (e.g., variables manipulated in simulations, changes to widgets, and time on tasks) while conducting inquiry. The availability of these log files permits researchers with rigorous and fine-grained data on students’ learning processes, including patterns in their inquiry behaviors and strategies, rather than the relatively static data provided in M/C and final answer choices. Researchers have used log files to examine the learning processes and behaviors of students during science inquiry in Inq-ITS but have yet to examine the log files of MLs in the same ways. This study aims to leverage Inq-ITS by analyzing the assessments and log files of middle school MLs’ as they complete virtual labs that target the NGSS science inquiry practices; scaffolds will be designed to support MLs during science inquiry based on the log file analysis. The goal of this research is to examine how (1) log files can be analyzed to better understand the needs of MLs in science inquiry, and (2) how log files can be used to investigate the efficacy of the scaffolds for MLs (compared to controls) as determined by the level of competencies at inquiry practices and transfer of these over science topics and time.

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