Because digital technologies and collection of data about our lives are omnipresent in our society, statistics has become an increasingly important school subject. In this article, we investigate the development of statistical literacy in a class of middle school students over the course of a one-year case study. Previously, the students tended to work procedurally with statistics, but during the study, they increasingly engaged in questioning statistical information, justifying statistical interpretations, and communicating data-based claims about issues within and beyond their school context. We take a social constructivist perspective and analyse which sociomathematical norms related to statistical literacy emerged during the classroom interactions. We identified two such norms—what counts as a valuable description of a data set and what counts as a valuable answer to a statistical investigation—that connected classroom interactions with the aim of supporting students’ development of statistical literacy, especially its dispositional elements. The study contributes a rare empirical exploration of how to support students’ development of statistical literacy at the classroom level.
As statistics content standards are increasingly embedded in U.S. high school mathematics curricula, Intermediate Algebra has become a key course for students’ statistical learning. This multiple case study used survey and interview data to examine how 10 high school mathematics teachers described their inclusion of statistics within Intermediate Algebra, as well as the rationales and curricular decisions guiding that work. Across cases, statistical literacy emerged as a shared instructional goal for including statistics in the course; teachers emphasized that learning statistics prepares students for the workplace and future professions, informed citizenship and personal choices, or both. Although teachers described teaching similar statistics content––primarily descriptive statistics, probability, and statistical association––statistical inference received comparatively limited attention. These findings highlight both the opportunities and constraints of including statistics content within Intermediate Algebra and offer implications for curriculum design, teacher preparation, and professional learning.
We investigated how high school students formulate and refine investigative questions when conducting a statistical investigation with secondary data. The data consisted of students’ written activity reports and email-based, post-interview responses collected after a seven-session instructional sequence in which CODAP served as the primary tool for multivariate data analysis. We distinguished initiating investigative questions (IIQs) from analysis-phase investigative questions (AIQs) implied in students’ analysis plans and characterized both sets across five analytical components: variables, clarity of population, intent, feasibility of drawing conclusions from the data, and global view of data. We then used thematic analysis to examine how data visualization appeared to be involved at points where IIQ-to-AIQ refinement was evident. Compared with IIQs, AIQs more often included a greater number of clearer variables and took forms that were more feasible for drawing conclusions from the given dataset. Across the three episodes analyzed, the representational and exploratory functions of data visualizations appeared to support refinement by helping students operationalize everyday terms, narrow populations, anticipate interpretable relationships, and set aside uninformative variables. This study offers classroom-based empirical insights into investigative questions in the context of secondary data and into the potential role of data visualization in their refinement.
This systematic review investigates measures of statistical ability in published literature to understand how statistical ability has been conceptualised and assessed. The review examines the components, reliability, validity, and correlations of these measures with cognitive (e.g., intelligence) and non-cognitive (e.g., attitude towards statistics) factors. From 51 papers, 25 unique measures were identified, with 60% assessing knowledge-based competencies. The validity evidence suggests that these measures assess their intended learning outcomes. Correlations between the measures and cognitive factors were stronger when closely aligned with the assessed ability. Research reporting correlations between statistical ability measures and non-cognitive factors is relatively limited. The review aims to inform educators and provide direction for future measurement development to address the identified gaps in the literature.
Using the commognitive construct of routine—repetitive rules or patterns observed in statistical discourse—we aimed to investigate how students use inconsistent routines when engaging in statistical reasoning about mode in the context of comparing modes across several data groups. The study data was collected by distributing mode-related questions to students through a Google Form, followed by interviews. Four mode-related questions were given to 43 undergraduate students participating in the study. The results showed that routine plays a significant role in statistical reasoning. The study identified two factors that contributed to the occurrence of inconsistent routines among students: (a) the way students described the data display and (b) the disconnection between routine and endorsed narrative. The results of this research highlight the importance of providing students with opportunities to work with diverse forms and conditions of data associated with mode.
