No abstract

Graphs are ubiquitous. Many graphs, including histograms, bar charts, and stacked dotplots, have proven tricky to interpret. Students’ gaze data can indicate students’ interpretation strategies on these graphs. We therefore explore the question: In what way can machine learning quantify differences in students’ gaze data when interpreting two near-identical histograms with graph tasks in between? Our work provides evidence that using machine learning in conjunction with gaze data can provide insight into how students analyze and interpret graphs. This approach also sheds light on the ways in which students may better understand a graph after first being presented with other graph types, including dotplots. We conclude with a model that can accurately differentiate between the first and second time a student solved near-identical histogram tasks.

No abstract

Students are typically introduced to probability through calculating simple events like flipping a coin. While these calculations can be done by hand, more complex probabilistic events, both in class and in the real world, require the use of computers. In this paper, we introduce a new tool—an R shiny web app and associated CRAN package based on the board game “CamelUp”—to help students explore these probability calculations through simulation in a fun context. Through this app and in conjunction with the board game Camel Up, we present some sample activities for helping students better understand and make in‐game probabilistic decisions.

As COVID-19 spread throughout the United States, governors and health experts (HEs) received a surge in followers on Twitter. This paper seeks to investigate how HEs, Democratic governors, and Republican governors discuss COVID-19 on Twitter. Tweets dating from January 1st, 2020 to October 18th, 2020 from official accounts of all fifty governors and 46 prominent U.S.-based HEs were scraped using python package Twint (N = 192,403) and analyzed using a custom-built wordcount program (Twintproject, 2020). The most significant finding is that in 2020, Democratic governors mentioned death at 4.03 times the rate of Republican governors in their COVID-19 tweets. In 2019, Democratic governors still mentioned death at twice the rate of Republicans. We believe we have substantial evidence that Republican governors are less comfortable talking about death than their Democratic counterparts. We also found that Democratic governors tweet about masks, stay-at-home measures, and solutions more often than Republicans. After controlling for state-level variations in COVID-19 data, our regression model confirms that party affiliation is still correlated with the prevalence of tweets in these three categories. However, there isn’t a large difference between the proportion of COVID-19 tweets, tweets about the economy, tweets about vaccines, and tweets containing “science-like” words between governors of the two parties. HEs tweeted about death and vaccines more than the governors. They also tweeted about solutions and testing at a similar rate compared to governors and mentioned lockdowns, the economy, and masks less frequently.