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

Video to come

Karin Landtblom - Stockholm University

My name is Karin Landtblom and I work as a senior lecturer at the Department of Teaching and Learning at Stockholm University, Sweden. I started my teaching career in 1987 teaching mathematics and science in school years 7–9 (students aged 12–16). Since 2000 I have taught mathematics education at Teacher Education in Stockholm. I took a master's in mathematics education in 2012. The title was: How students act and communicate when using ICT in mathematical activities. A multimodal study performed in school year 9. The mathematical content in this study was functions, in particular the straight line. 2015 my application to study at a PhD level was accepted. I have had the possibility to study 50% of my service, and I graduated 2023. The title of my thesis is: Mean, median, and mode in school years 4–6: A study about aspects of statistical literacy. Link to the kappa of the thesis➶.

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

Kelly is a Teaching Associate Professor at the University of Illinois Urbana-Champaign. With his PhD training in Curriculum and Instruction, Kelly primarily conducts qualitative research that seeks to capture rich examples of student thinking. He aims to create work that builds theory regarding both students' intuition about statistical concepts and student perspectives of the broader discipline.

Resources and Tensions in Student Thinking about Statistical Design

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

Thomas Metzger joined the statistics faculty at Ohio State in 2019 after earning his PhD in statistics from Virginia Tech. He earned bachelor's degrees in mathematics and actuarial science, as well as a master's degree in secondary mathematics education, from The Ohio State University in 2010-11.

The Impact Of A Statistical Collaboration Laboratory On The Statistics Students Working In It

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.