A collection of SERJ papers by new researchers

Presented at: 13 November 2023; 20:00 UTC

Webinar duration: 90 minutes

Presenter(s): Karin Landtblom, Kelly Findley, Thomas Metzger

Link to video➶

Karin's slides➶

Kelly's slides➶

Karin Landtblom - Stockholm University

Opportunities to Learn Mean, Median, and Mode Afforded by Textbook Tasks Link to paper➶

This research paper examines tasks related to mean, median, and mode in seven Swedish textbook series for students aged between 10–13. The tasks were analysed based on context, mathematical properties, input and output objects, and transformations. These categories allow for a thorough analysis of the opportunities afforded to students to understand these measures. The analysis revealed that most tasks focus on the mean and on procedural transformations with quantitative values. Our findings suggest that the textbooks do not afford enough explicit context for students to develop a deep understanding of the mathematical properties of different measures of central tendency. By analysing various textbooks, we can gain a broader understanding of the learning opportunities afforded to students. We discuss the implications of these results for task design.

Kelly Findley - University of Illinois Urbana-Champaign

Resources and Tensions in Student Thinking about Statistical Design Link to paper➶

Reform efforts in statistics education emphasize the need for students to develop statistical thinking. Critical to this goal is a solid understanding of design in the process of collecting data, evaluating evidence, and drawing conclusions. We collected survey responses from over 700 college students at the start of an introductory statistics course to determine how they evaluated the validity of different designs. Despite preferring different designs, students offered a variety of productive arguments supporting their choices. Our results highlight that instruction should frame design as the balancing of different priorities: namely causality, generalizability, and power.

Thomas Metzger - Ohio State University

The Impact Of A Statistical Collaboration Laboratory On The Statistics Students Working In It Link to paper➶

Graduate level statistics education curricula often emphasize technical instruction in theory and methodology, but can fail to provide adequate practical training in applications and collaboration skills. We argue that a statistical collaboration center (“stat lab”) structured in the style of the University of Colorado Boulder’s Laboratory for Interdisciplinary Statistical Analysis (LISA) is an effective mechanism for providing graduate students with necessary training in technical, nontechnical, and job-related skills. We summarize the operating structure of LISA, and then provide evidence of its positive impact on students via analyses of a survey completed by 123 collaborators who worked in LISA between 2008–15 while it was housed at Virginia Tech. Students described their work in LISA as having had a positive impact on acquiring technical (94%) and non-technical (95%) statistics skills. Five-sixths (83%) of the students reported that these skills will or have helped them advance in their careers. We call for the integration of stat labs into statistics and data science programs as part of a comprehensive and modern statistics education, and for further research on students’ experience in these labs and their impacts on student outcomes.