  Supporting equitable instruction is an important issue for teachers attending
diverse STEM classrooms. Visual learning analytics along with effective student
survey measures can support providing on time feedback to teachers in making
instruction more culturally relevant to all students. We adopted a
user-centered approach, where we engaged seven middle school science teachers
in iterative testing of thirty data visualizations disaggregated over markers
such as gender and race for implementation of selected displays in a visual
learning analytics tool- Student Electronic Exit Ticket (SEET). This process
helped us gather insights into teachers' sensemaking in identifying patterns of
student data related to gender and race, selecting and improving the design of
the feedback displays for the SEET [10].
