Gist Inference Scores Predict Cloze Comprehension "In Your Own Words" for Native, Not ESL Readers

Understanding the landscape of web-based medical misinformation about vaccination

Given the high rates of vaccine hesitancy, web-based medical misinformation about vaccination is a serious issue. We sought to understand the nature of Google searches leading to medical misinformation about vaccination, and guided by fuzzy-trace theory, the characteristics of misinformation pages related to comprehension, inference-making, and medical decision-making. We collected data from web pages presenting vaccination information. We assessed whether web pages presented medical misinformation, had an overarching gist, used narrative, and employed emotional appeals. We used Search Engine Optimization tools to determine the number of backlinks from other web pages, monthly Google traffic, and Google Keywords. We used Coh-Metrix to measure readability and Gist Inference Scores (GIS). For medical misinformation web pages, Google traffic and backlinks were heavily skewed with means of 138.8 visitors/month and 805 backlinks per page. Medical misinformation pages were significantly more likely than other vaccine pages to have backlinks from other pages, and significantly less likely to receive at least one visitor from Google searches per month. The top Google searches leading to medical misinformation were "the truth about vaccinations," "dangers of vaccination," and "pro con vaccines." Most frequently, pages challenged vaccine safety, with 32.7% having an overarching gist, 7.7% presenting narratives, and 17.3% making emotional appeals. Emotional appeals were significantly more common with medical misinformation than other high-traffic vaccination pages. Misinformation pages had a mean readability grade level of 11.5, and a mean GIS of - 0.234. Low GIS scores are a likely barrier to understanding gist, and are the "Achilles' heel" of misinformation pages.

Correlation analysis using teaching and learning analytics

Data analytics techniques have been gaining more space in the scientific environment with applications in various areas of knowledge, including education. This paper aims to analyse data taken from a questionnaire of the Organization for Economic Development Cooperation (OECD) given to teachers and school managers. In this questionnaire, school environment issues are assessed, specifically: school environment, professional development, school leadership, and efficient management. As a methodology, Teaching and Learning Analytics (TLA) was used, particularly correlation analysis, which enables the extraction of useful information from raw data, relating issues that interfere with the teaching and learning relationship, besides specific analysis of student learning. The results obtained about the school environment are not linear. They do not present moderate or a solid linear correlation, making it impossible to validate and integrate answers related to the statements of the themes and sub-themes chosen for this analysis. In this sense, the research found dichotomous observations that mirrored many controversies and insecurities, enabling considerations about possible school scenarios and their effective practices.