tandfonline.com har udgivet en rapport under søgningen “Teacher Education Mathematics”: ABSTRACT ABSTRACT The purpose of this research is to investigate prospective teachers’ learning in the statistical modeling process. To reach this goal, we position the study in a socio-critical perspective of modeling that informed the design of a statistical investigation. Participants were 10 prospective teachers from a state university in northwestern Colombia who were taking a methods course in statistics. They worked out a statistical investigation inspired by a news article on global warming. The main source of data was fragments from prospective teachers’ speech while working out the statistical investigation. The information was complemented with prospective teachers’ narratives in which they reflected on their experience with statistics. The results reveal that the prospective teachers’ discourse offers indications to suggest… Continue Reading →
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tandfonline.com har udgivet en rapport under søgningen “Teacher Education Mathematics”: Abstract Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Statistical modeling continues to gain prominence in the secondary curriculum, and recent recommendations to emphasize data science and computational thinking may soon position algorithmic models into the school curriculum. Many teachers’ preparation for and experiences teaching statistical modeling have focused on probabilistic models. Subsequently, much of the research literature related to the teachers’ understanding has focused on probabilistic models. This study explores the extent to which secondary statistics teachers appear to understand ideas of statistical modeling, specifically the… Continue Reading →
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tandfonline.com har udgivet en rapport under søgningen “Teacher Education Mathematics”: Abstract Abstract Kaggle is a data modeling competition service, where participants compete to build a model with lower predictive error than other participants. Several years ago they released a simplified service that is ideal for instructors to run competitions in a classroom setting. This article describes the results of an experiment to determine if participating in a predictive modeling competition enhances learning. The evidence suggests it does. In addition, students were surveyed to examine if the competition improved engagement and interest in the class. Supplementary materials for this article are available online. Link til kilde
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