To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality ...in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool the Common Online Data Analysis Platform (CODAP). Pre- and post-module data show that use of this module was associated with statistically significant growth in students' understanding of fundamental data concepts and individual interests in statistics and data analysis, with small to moderate effect sizes. Student survey responses and interview data from students and teachers suggest that the topic of income inequality, features within CODAP, the use of person-level data, and opportunities to engage in multivariable thinking helped to support critical data literacy and its foundations among participating students. We describe our definitions of data literacy and critical data literacy and discuss curriculum strategies to develop them.
Promoting positive student attitudes towards stochastics has become a core goal of statistics education reform, and we argue that this starts with the teachers during their teacher preparation ...program. Building on previous work assessing teachers’ attitudes, we focus on how pre-service teacher attitudes vary across different dimensions, and how these patterns can inform teacher training. We present results from assessing attitudes towards stochastics and its teaching for a sample of 269 pre-service Chilean mathematics teachers across three topics: descriptive statistics, probability, and statistical inference. Using a quantitative approach, and considering attitudes towards content and pedagogy, we focus on describing the main attitudinal differences among these three areas. In general, we found positive attitude towards the content and its teaching in all three areas, but with differences among them, primarily in the area of statistical inference. We end with some proposals aimed at improving teacher preparation, focusing on helping pre-service teachers understand the utility and overarching process of statistical investigations.
Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing ...to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an introductory or second course in statistics. After adjusting for the change in track length and track condition, winning speed has an interesting nonlinear trend, with one notable outlier. Student investigations can range from validating the phrase "the most exciting two minutes in sports" to predicting the winning speed of next year's race using parallel polynomial models.
Chronic hepatitis B, a condition associated with severe complications, disproportionately affects Asian Americans and Pacific Islanders in the United States. Increasing testing among this population ...is critical for improving health outcomes. This study compares different types of video narratives that use storytelling techniques to an informational video (control), to examine whether narratives are associated with higher hepatitis B beliefs scores and video rating outcomes. A sample of Asian American and Pacific Islander adults (N = 600) completed an online survey where they viewed one of four video conditions, three of which included storytelling techniques and one with informational content. Results indicated that parental stories received significantly higher perceived effectiveness ratings (M = 3.88, SD = 0.61) than the older adult personal stories (M = 3.62, SD = 0.74), F(3, 596) = 3.795, p = .010. Parental stories also had significantly higher perceived severity scores (M = 3.83, SD = 0.69) compared to the young adult stories (M = 3.73, SD = 0.74) and the informational videos (M = 3.83, SD = 0.69), F(3, 596) = 7.72, p < .001. The informational videos (M = 4.10, SD = 0.65) received significantly higher message credibility ratings than the older adult personal stories (M = 3.84, SD = 0.70), F(3, 596) = 4.71, p = .003. Follow-up tests using Bonferroni correction revealed that parental stories (M = 3.98, SD = 0.64) and young adult personal stories (M = 3.934, SD = 0.76) scored significantly higher on speaker ratings than the older adult personal stories (M = 3.698, SD = 0.77). Results suggest that storytelling has the potential for connecting with a specific audience in an emotional way that is perceived well overall. Future research should examine the long-term impact of hepatitis B personal story videos and whether the addition of facts or statistics to videos would improve outcomes.
Recent curriculum development projects emphasize teaching simulation and randomization‐based statistical inference as a prominent feature in introductory statistics courses. We describe the goals, ...distinctive features, and examples from some of these projects. Technology is a key component of these courses, so we mention desirable features of the various technology products used with this approach. We also discuss how student learning is being assessed in such courses, along with how the curriculum effort itself is being evaluated. We also touch on some challenges that we have encountered with teaching these courses, both from a student and a faculty viewpoint. WIREs Comput Stat 2014, 6:211–221. doi: 10.1002/wics.1302
This article is categorized under:
Statistical and Graphical Methods of Data Analysis > Bootstrap and Resampling
Statistical Models > Simulation Models
The recent simulation-based inference (SBI) movement in algebra-based introductory statistics courses (Stat 101) has provided preliminary evidence of improved student conceptual understanding and ...retention. However, little is known about whether these positive effects are preferentially distributed across types of students entering the course. We consider how two metrics of Stat 101 student preparation (precourse performance on concept inventory and math ACT score) may or may not be associated with end of course student performance on conceptual inventories. Students across all preparation levels tended to show improvement in Stat 101, but more improvement was observed across all student preparation levels in early versions of a SBI course. Furthermore, students' gains tended to be similar regardless of whether students entered the course with more preparation or less. Recent data on a sample of students using a current version of an SBI course showed similar results, though direct comparison with non-SBI students was not possible. Overall, our analysis provides additional evidence that SBI curricula are effective at improving students' conceptual understanding of statistical ideas postcourse regardless student preparation. Further work is needed to better understand nuances of student improvement based on other student demographics, prior coursework as well as instructor and institutional variables.
Although much attention has been paid to issues around student assessment, for most introductory statistics courses few changes have taken place in the ways students are assessed. The assessment ...literature describes three foundational elements-cognition, observation, and interpretation-that comprise an "assessment triangle" underlying all assessments. However, most instructors focus primarily on the second component: tasks that are used to produce grades. This article focuses on three sections written by leading statistics educators who describe some innovative and even provocative approaches to rethinking student assessment in statistics classes.
The 2000 ASA Guidelines for Undergraduate Statistics majors aimed to provide guidance to programs with undergraduate degrees in statistics as to the content and skills that statistics majors should ...be learning. The 2014 Guidelines revise the earlier guidelines to reflect changes in the discipline. As programs strive to adjust their curricula to align with the 2014 Guidelines, it is appropriate to also think about developing an assessment cycle of evaluation. This will enable programs to determine whether students are learning what we want them to learn and to work on continuously improving the program over time. The first step is to translate the broader Guidelines into institution-specific measurable learning outcomes. This article focuses on providing examples of learning outcomes developed by different institutions based on the 2000 Guidelines. The companion article by Moore and Kaplan (this issue) focuses on choosing appropriate assessment methods and rubrics and creating an assessment plan. We hope the examples provided are illustrative and that they will assist programs as they implement the 2014 Guidelines.
Received November 2014. Revised July 2015.
Formal inference, which makes theoretical assumptions about distributions and applies hypothesis testing procedures with null and alternative hypotheses, is notoriously difficult for tertiary ...students to master. The debate about whether this content should appear in Years 11 and 12 of the Australian Curriculum: Mathematics has gone on for several years. If formal inference is not included in Years 11 and 12, what statistical content, if any, should there be? Should students continue learning more data handling skills, which are a feature of the F-10 curriculum (Australian Curriculum, Assessment and Reporting Authority ACARA, 2011)? Perhaps the focus should be on procedural aspects, such as correlation and lines of best fit, employing principles from calculus. Or perhaps the curriculum should drop statistics and focus on the more complex theoretical aspects of probability.
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Introductory biology courses typically incorporate data collection and analysis into laboratory assignments, and therefore teach some basic statistics. However, these curricula are ...generally developed without input from statisticians and are taught by instructors, often graduate students, with a range of experience using statistics but no training in statistics education. Given the importance of statistical thinking and other quantitative approaches in biology, the Statistical Thinking in Undergraduate Biology (STUB) network was established to facilitate coordination of best practices and assessment of statistical thinking in introductory biology students. As an example of the network’s benefits, we present a case study involving redesign of laboratory activities in an Introduction to Organismal Form and Function course. Changes include alignment of language with that used in statistics courses, more thorough instruction about experimental design and statistical tests, and use of an online simulation tool shown to improve understanding of statistical inference in statistics courses. These changes are supported by statistics faculty participating in the training of biology teaching assistants. An assessment in progress compares conceptual understanding and attitudes toward statistics before and after the redesigned lab activity; the assessment tool is being used throughout the STUB network, providing comparable data across institutions. This kind of cross‐disciplinary collaboration has the potential to improve statistical thinking in undergraduate biology students, better preparing them for advanced coursework and for careers in modern biology.
Support or Funding Information
NSF Research Coordination Network‐Undergraduate Biology Education (RCN‐UBE)
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