Wednesday, Aug 9: 8:30 AM - 10:20 AM
Invited Paper Session
Metro Toronto Convention Centre
Section on Statistics and Data Science Education
Justice Equity Diversity and Inclusion Outreach Group
In the spirit of Gutiérrez (2002), access represents all of the opportunities available for student learning. Cultivating access is essential for critically examining student achievement, identity, and power. In this talk, I will discuss my approach to creating access to learning opportunities in a systematic way: through resources and practices. Resources include both content and human resources. Practices are habits that both students and instructors can enact to more fully access those content and human resources. High-impact resources and practices have the potential to change the game for both students and instructors.
In the college classroom, grades are the primary avenue by which we quantify and communicate student achievement. In setting up grading systems for our courses, we make countless decisions: Should the project be worth 25 or 30 percent of the final grade? Will I drop the lowest quiz score? What penalty (if any) should I implement for late work? These seemingly small decisions can have a surprisingly large impact on the grades that we assign and the type of learning and understanding that we privilege. In conversation and collaboration with colleagues at Macalester College, I have been drastically and continually rethinking my approach to grading in recent years. In this talk, I will share some of the changes I have made to the way I grade in both introductory and advanced undergraduate statistics courses. I will highlight recent successes in my shift toward ungrading and my efforts to more intentionally and directly involve students in the grading process. I will also discuss changes that have been less successful, sharing lessons learned along the way and areas for continued rethinking.
There has been an increase in the number of students becoming disengaged in the classrooms, particularly in mathematics. This is an important problem as lack of engagement negatively affects students' achievement and performance in mathematics. Student engagement in introductory statistics courses can play a major role in retaining students in STEM fields and thus contributes to the sustainable development of society. Educational researchers are taking note of student engagement and how factors influence student learning and achievement outcomes. In this presentation, I will discuss the perceptions of power and identity within introductory statistics courses with the consideration of student engagement and instructor pedagogical efforts. I will also discuss how power imbalances negatively affect student engagement and can prevent students from reaching their full potential. In addition, I will describe pedagogical strategies I use in my introductory statistics course to provide students with a sense of belonging and balanced power.
For nearly two decades active learning has been elevated by statistics educators as a critical component of the statistics classroom. Yet, these conversations have yet to acknowledge how group collaborations fall prey to issues of status. Fights over who gets to speak and whose words are recognized are indicative of power and status, where students with higher status are positioned as credible sources of information, gain and maintain the conversational floor, and have their ideas attended to. Through the lens of group discourse, researchers have demonstrated how the gendered and racial microaggressions students face oppress their opportunity to learn and squelch the development of their mathematical identity, a substantial determinant of their persistence within the discipline. If we hope for statistics and data science to be diverse, every student needs to feel they belong within the discipline. In this presentation, I will describe pedagogical tools which actively challenge systems of power in the classroom, creating a more equitable environment for collaborative group work, describing my experiences implementing these tools in an introductory data science course.