  Research experience and mentoring has been identified as an effective
intervention for increasing student engagement and retention in the STEM
fields, with high impact on students from undeserved populations. However,
one-on-one mentoring is limited by the number of available faculty, and in
certain cases also by the availability of funding for stipend. One-on-one
mentoring is further limited by the selection and self-selection of students.
Since research positions are often competitive, they are often taken by the
best-performing students. More importantly, many students who do not see
themselves as the top students of their class, or do not identify themselves as
researchers might not apply, and that self selection can have the highest
impact on non-traditional students. To address the obstacles of scalability,
selection, and self-selection, we designed a data science research experience
for undergraduates as part of an introductory computer science course. Through
the intervention, the students are exposed to authentic research as early as
their first semester. The intervention is inclusive in the sense that all
students registered to the course participate in the research, with no process
of selection or self-selection. The research is focused on analytics of large
text databases. Using discovery-enabling software tools, the students analyze a
corpus of congressional speeches, and identify patterns of differences between
democratic speeches and republican speeches, differences between speeches for
and against certain bills, and differences between speeches about bills that
passed and bills that did not pass. In the beginning of the research experience
all student follow the same protocol and use the same data, and then each group
of students work on their own research project as part of their final project
of the course.
