Description

This session aims to promote shifting the focus of statistics instruction for non-majors towards more wholistic data literacy outcomes. Case studies, lessons learned, and measurement tools that can serve the field will be presented. Technological developments have led to the creation of unprecedented amounts of data, and a cultural shift whereby decisions are almost exclusively data driven. Whether conducting interviews, working with organizational performance indicators, tracking social media behaviour, or reading charts and dashboards, individuals working in fields such as marketing, politics, healthcare, and program evaluation are interacting with exponentially more data and data-based products than ever before. Data literacy is the ability to interpret, critically evaluate, and communicate the statistical-, text-, and probability-based information that emerges in a data-driven society. It requires knowledge of statistics, but also elements of data science, information science, and beyond. Currently, formal training in statistics courses focuses primarily on the mathematical processes and underlying statistical analyses, leaving many students – especially non-majors – overwhelmed and without the understanding of core concepts necessary for identifying good and bad representations of data, converting this knowledge into action, and succeeding in their future careers. To better prepare the current generation of workers for the job market and to help promote productive citizenship, higher education statistics instruction should shift focus towards data literacy outcomes.

The speakers will present research results examining the current prevalence of data literacy in higher education, as well as collaborative projects and design principles for revised courses aiming to promote data literacy outcomes. Perspectives from multiple stakeholders including students, professors, and employers obtained using a variety of cross-sectional and longitudinal research designs will be shared. These results will highlight the low rates of data literacy outcomes for students, lack of preparedness for the job market, challenges in current statistics courses, and opportunities for improvements moving forward. The session will conclude with a critical overview of implications for curriculum and training development, as well as key challenges and future directions, and a general discussion.

Justification

Much of the current statistical education focuses on providing students with the skills to collect and analyze data while emphasizing rote memorization and mathematical computations instead of interpreting and applying results in the real world. As a result, these courses have a bad reputation among non-majors (i.e., students who are not specializing in statistics or math-focused courses) as they are perceived as irrelevant, anxiety-inducing, unnecessarily challenging, and too focused on performing statistical analyses. Instead, the session will present innovative ideas and findings from formative research about revised courses that adopt a data literacy approach. Such courses emphasize acquiring the skills and tools necessary to become expert consumers and users of data, but not producers and analysts, thus better providing them with the tools to succeed on the job market. 

The proposed session is designed to attract attendees from several Associations, including the International Association for Statistical Education (IASE) and the International Society for Business and Industrial Statistics (ISBIS) who are interested in modernizing and adapting a data literacy perspective to statistical education in order to provide non-specialists, and business-oriented students, with the skills required to consume and communicate data in their careers. The proposed presentations will be of particular interest to individuals involved in statistics and data science education, as well as policymakers, public servants, and media who are interested in improving the data literacy skills of citizens-at-large and workers. It may also attract those involved in developing data literacy within the Canadian Federal Government.

The speakers were chosen to represent diverse disciplinary perspectives and nationalities in order to approach the topic from multiple perspectives and have actively collaborated on a number of projects since 2020. They are experts in statistics education; measurement; and motivation. The discussants will offer a critical commentary on the described directions, with a focus on the importance of developing skills for consumers of data. 

This session aims to promote shifting the focus of statistics instruction for non-majors towards more wholistic data literacy outcomes. Case studies, lessons learned, and measurement tools that can serve the field will be presented. Technological developments have led to the creation of unprecedented amounts of data, and a cultural shift whereby decisions are almost exclusively data driven. Whether conducting interviews, working with organizational performance indicators, tracking social media behaviour, or reading charts and dashboards, individuals working in fields such as marketing, politics, healthcare, and program evaluation are interacting with exponentially more data and data-based products than ever before. Data literacy is the ability to interpret, critically evaluate, and communicate the statistical-, text-, and probability-based information that emerges in a data-driven society. It requires knowledge of statistics, but also elements of data science, information science, and beyond. Currently, formal training in statistics courses focuses primarily on the mathematical processes and underlying statistical analyses, leaving many students – especially non-majors – overwhelmed and without the understanding of core concepts necessary for identifying good and bad representations of data, converting this knowledge into action, and succeeding in their future careers. To better prepare the current generation of workers for the job market and to help promote productive citizenship, higher education statistics instruction should shift focus towards data literacy outcomes.

The speakers will present research results examining the current prevalence of data literacy in higher education, as well as collaborative projects and design principles for revised courses aiming to promote data literacy outcomes. Perspectives from multiple stakeholders including students, professors, and employers obtained using a variety of cross-sectional and longitudinal research designs will be shared. These results will highlight the low rates of data literacy outcomes for students, lack of preparedness for the job market, challenges in current statistics courses, and opportunities for improvements moving forward. The session will conclude with a critical overview of implications for curriculum and training development, as well as key challenges and future directions, and a general discussion.

Organiser: Prof. Meredith Rocchi 

Chair: Prof. Christopher Gravel 

Speaker: Prof. Simon Beaudry 

Speaker: Prof. Meredith Rocchi 
Discussant: Prof. Dani Ben Zvi

Discussant: Prof. Emilie Gravel