MetaSeer.STEM: Towards Automating Meta-Analyses

Abstract

Meta-analysis is a principled statistical approach for summarizing quantitative information reported across studies within a research domain of interest. Although the results of metaanalyses can be highly informative, the process of collecting and coding the data for a meta-analysis is often a laborintensive effort fraught with the potential for human error and idiosyncrasy. This is due to the fact that researchers typically spend weeks poring over published journal articles, technical reports, book chapters and other materials in order to retrieve key data elements that are then manually coded for subsequent analyses (e.g., descriptive statistics, effect sizes, reliability estimates, demographics, and study conditions). In this paper, we propose a machine learning based system developed to support automated extraction of data pertinent to STEM education meta-analyses, including educational and human resource initiatives aimed at improving achievement, literacy and interest in the fields of science, technology, engineering, and mathematics.