Study Description

During the 2023-2024 school year, the Utah State Board of Education began a data science pilot during which 15 high school teachers throughout the state volunteered to teach a full year data science class for the first time. This pilot presented a valuable opportunity to ask consequential questions about data science education in practice. With funding from the Valhalla Foundation and in close collaboration with the Utah State Board of Education, researchers from Outlier Research & Evaluation at the Data Science Institute at the University of Chicago conducted a study that aimed to contribute to understanding of:

1. four high school data science curricula,
2. teachers’ experiences selecting and using the curricula for the first time,
3. key supports and barriers affecting the implementation of the curricula, and
4. students’ experiences with data science.

Participants

Data science teachers from 12 different public high schools in Utah participated. The high schools resided in the following school districts: Alpine, Davis, Granite, Jordan, Nebo, Provo, and Tooele.

Teachers Background/Experience: Twelve of the fifteen data science pilot teachers volunteered to participate in the research. Though all of the teachers were teaching data science for the first time, their overall teaching experience varied, with three teachers reporting less than five years of teaching experience, four teachers reporting between 6 and 10 years of teaching experience, and four teachers reporting more than 10 years of teaching experience. All of the teachers were math teachers and all but one teacher were teaching other math courses in addition to data science during the academic year when the study took place. Two-thirds of the teachers were women and one-third were men. Of the ten teachers who reported their race/ethnicity, one reported being white and Native American/Alaskan Native, and the rest reported being White.

Curriculum Descriptions: Using a component-based research approach, researchers reviewed four curricula (Bootstrap, Coursekata, Introductory Data Science (IDS) and youcubed) and documented in detail their structural and instructional components. Outlier reviewed the instructional materials, the developers’ websites, and observed professional development. Working with the developers’ input, Outlier summarized that information into the information on this website.

Data Collection

To reach the study goals, Outlier engaged with data science teachers and students throughout the 2023-2024 school year.

Data Science Teacher Interviews and Focus Groups: All participating teachers were interviewed once a quarter for a total of four interviews each. During the interviews, teachers were asked why they chose to teach data science, their curriculum selection process, their experiences teaching data science, their students’ engagement during class, and the supports and barriers they experienced related to teaching data science. Additionally, near the end of the school year, teachers who taught the same curriculum were convened in focus groups to discuss their experiences.

Classroom Observations and Student Focus Groups: Outlier visited a subset of data science classrooms during the second and fourth quarters of the academic year. During the visit, they observed data science classrooms and conducted focus groups with students to hear about their experiences in the class and their reasons for enrolling.