What should be in the College Geometry class taken to prepare preservice high school math teachers to teach geometry? To work towards agreement, an online community of faculty who teach or do research on college geometry courses taken by secondary teachers (GeT courses) was formed in Summer 2018 by Pat Herbst and Amanda Brown from the University of Michigan GRIP lab (https://www.gripumich.org/). A subgroup of GeT instructors in the project worked on articulating a set of essential student learning outcomes (SLOs) where essential means the identification of content knowledge that all prospective secondary geometry teachers should have the opportunity to learn. In this breakout session, we will report on the first public draft of the set of learning outcomes, available at https://getapencil.org/student-learning-objectives/ and ongoing efforts to disseminate the SLOs in order to gather feedback and wider perspectives on the SLOs. Then we will consider different activities or tasks that could be done to meet these objectives as well as discuss whether the list should be expanded, reduced or modified.

The SENCER approach provides a framework for students to contextualize their education through civic and community engagement with STEM concepts where they can see the impact of their learning and make connections beyond the classroom. In this breakout session, we’ll (re)introduce you to Science Education for New Civic Engagements and Responsibilities (SENCER), provide examples of how we’ve infused civic engagement and social justice into our courses, and walk you through “SENCERizing” your own materials. You could start small with a single activity or go big by designing a course. You’ll also have a chance to connect with others with shared interests for potential collaborations.

In this panel session, participants will have an opportunity to debrief about the keynote presentations and the breakout sessions, discuss take-aways, and brainstorm on challenges and opportunities for implementing strategies discussed in the symposium. Participants will consider next steps for actions they can take, as well as actions for departments/institutions and for the fields of science and mathematics.

Organizers: Andrea Van Duzor and Mel Sabella

Moderator: Adaeze Olikagu (Chicago State University)

LA Panelists: Miroslava Chavolla Avina (University of Illinois at Chicago), Odalis Curzio (Northeastern Illinois University), Vivian Cox (Chicago State University), Varun Maheshwari (University of Illinois at Chicago), D'Kaila Price (Chicago State University), Korvell Russell (Chicago State University)

Peer instructor programs such as the Learning Assistant (LA) Model, developed at the University of Colorado-Boulder, have provided essential support to students enrolled in science and mathematics, in a variety of institutional settings. In this panel, LAs will discuss their involvement in engaging and supporting STEM students in a variety of STEM classes. The panel will be an opportunity to learn from our student experts.

In this breakout section, a graphical causal model for none-experimental data analysis will be introduced. In most education studies, it is usually not feasible to carry out random controlled experiment due to factors of enrollment policy, enrollment numbers, budget, time span, ...etc. and we must either use observational or quasi-experimental data to study educational topics. However, it is very difficult or even impossible to extract out the causal relationship embedded in a correlational network embedded in data.

Simpson paradox is the most prominent example to illustrate the degree of difficulty in applying statistical tools only. Couple Simpson Paradox case studies will be presented and discussed especially on the aspect of drawing causal conclusion from analyzed results. These paradoxical situations would be resolved clearly if the data-generating mechanism is known.

The Graphical Causal Model (GCM) proposed by Dr. Judea Pearl is a very appealing approach to do causal study for none-experimental data once the data-generating mechanism described by Direct Acyclic Diagram (DAG) is given. Two advantages of this approach are: (1) major causal assumptions are explicitly coded in the graph for examining analyses and communications (2) it is clear for researchers to condition on the appropriate variables for identifying causal relationship and eventually to draw causal statements from the data if it is identified. We will use GCM to resolve previous Simpson paradox examples.