A Kaleidoscope of Understanding: Comparing Real with Random Data, Using Binary-Choice Items, to Study Preservice Elementary Teachers' Knowledge of Climate Change

Douglas Hayhoe, Shawn Bullock, Katharine Hayhoe

Research output: Contribution to journalArticle

Abstract

The authors used a 59-item survey to probe the understanding of climate change by 89 Ontario preservice teachers. The study investigated the usefulness of comparing real survey data from closed, binary choice items, with randomly generated data. Climate change was chosen to be the topic because it is a new emphasis in K–12 science curricula. If teachers had answered the survey randomly, according to Monte Carlo simulations, a normal distribution would result, with 56 of the 59 items answered correctly by 40%–60% of the respondents. A bimodal distribution resulted, however, with 34 items answered correctly by more than 60% and 18 items by less than 40%. Apparently, the teachers knew a lot about climate change, but also had many misconceptions, some identified here for the first time. Item discrimination indices and correlation coefficients, however, were the same for the real versus Monte Carlo data, suggesting that preservice teachers’ knowledge was a “kaleidoscope of understanding,”
Original languageEnglish
JournalWeather, Climate, and Society
DOIs
StatePublished - Oct 9 2011

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