Assessing Teachers’ Discourse About the Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education (Gaise)

Abstract


 
 
 This paper starts from the premise that teachers’ discourse communities influence how ideas for reform are implemented. In order to understand some of the discourse surrounding the reforms proposed by GAISE, an online focus group activity was conducted. The focus group consisted of pre-service and practicing teachers responsible for teaching statistics at various grade levels. Focus group discourse was used to formulate a set of working hypotheses about actions that need to be taken to facilitate the implementation of GAISE. Working hypotheses emphasized that statistics educators need to play roles in developing teachers’ content knowledge, helping teachers understand the differences between mathematics and statistics, deepening teachers’ pedagogical knowledge, building teachers’ curricular knowledge, and influencing the writing of state-level standards.
 First published May 2008 at Statistics Education Research Journal Archives
 
 

An Investigation of Pre-service Middle School Mathematics Teachers’ Discussion Skills in the Context of Lesson Study

“…One of the learning areas where mathematical discussion environments are important is statistics (Ben-Zvi, 2011;Cobb & McClain, 2004). In statistical learning environments, students are expected to experience the entire statistical process from primary education to the end of secondary education, namely, posing problems, collecting, organizing, analyzing, and interpreting data (Bargagliotti et al, 2020;Franklin et al, 2005;MONE, 2018;NCTM, 2000). The process of posing questions, which is the first component of doing statistics, includes defining the problem clearly and creating the question(s) that can be answered with the help of data (Franklin et al, 2005).…”

“…Deciding on suitable data collection methods and collecting data according to these methods is one of the essential skills in this process (Franklin et al, 2005). In the data analysis, suitable methods are selected, and the data are analyzed (Franklin et al, 2005;Bargagliotti et al, 2020). Classification, one of the salient elements of data analysis, involves using tools to represent the data to be analyzed, such as graphs and central tendency and dispersion measures (Bargagliotti et al, 2020).…”

“…In the data analysis, suitable methods are selected, and the data are analyzed (Franklin et al, 2005;Bargagliotti et al, 2020). Classification, one of the salient elements of data analysis, involves using tools to represent the data to be analyzed, such as graphs and central tendency and dispersion measures (Bargagliotti et al, 2020). In the stage of interpreting the results, which is the last stage of the process of doing statistics, starting from the analysis made in the third stage, and then they are associated with the initial research question (Franklin et al, 2005;Bargagliotti et al, 2020).…”

Designing a sequence of activities to build reasoning about data and visualization

“…Giving students experiences with data, especially multivariate data, helps emphasize the multivariate essence inherent in the discipline of statistics as a tool with which to reason about the world [3]. Working with multivariate data can develop multivariate reasoning, a focus the GAISE College Report [10] and the GAISE Pre‐K–12 Report [1] recommended for all introductory level statistics courses across all educational levels. However, multivariate reasoning does not develop without careful pedagogical consideration.…”

A comparison of the effects of different methodologies on the statistics learning profiles of prospective primary education teachers from a gender perspective

“…As argued in the Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) (Bargagliotti et al, 2020), the demands for statistical literacy have never been greater. For example, students who finish the secondary school should develop the ability to interpret, critically evaluate, and communicate about statistical information (Gal, 2002), with the ultimate goal of understanding decision making under uncertainty (Shaughnessy, 2019).…”