…the discipline presents an excellent opportunity to build pathways for students at all levels of academic preparation.
Harry S Truman College (Truman College) is a community college located on the north side of Chicago and is part of a district of seven individually accredited colleges. The institution holds two Minority Status Institution designations, HSI (Hispanic Serving Institution) and AANAPISI (Asian American and Native American Pacific Islander-Serving Institution). As an HSI and AANAPISI, we focus on addressing the underrepresentation of minoritized individuals entering STEM fields. Each college in our District has a “Center of Excellence” focus area, and Truman’s is the Center of Excellence in Education, and Scientific Technology and Innovation. Traditionally, community colleges focus curriculum on one of two paths: career training or transfer preparation. Students in transfer paths can enroll in state-approved degree programs (Associate of Arts and Associate of Science degrees) to award junior status upon transfer to other state schools. The career path also has degrees but includes certificates of varying credit hours that prepare individuals for work in a specific industry. The efforts to bring data science to the college are directly connected to our Center of Excellence designation. However, we have been challenged to understand if transfer or career paths would be best for associate degree students.
The growing data science discipline is a complex industry for community colleges to find its role within, and only a few programs have been developed. The field is emerging, and four-year and graduate programs are still developing. At the same time, the newness of the discipline presents an excellent opportunity to build pathways for students at all levels of academic preparation. A fast-growing field can prove challenging for community colleges to scale. In a partnership with UChicago, Truman and the University launched a preceptorship for data science. Preceptors have a joint placement at UChicago and Truman College. They instruct at both institutions and support Truman’s faculty in developing a curriculum for data science. This support allowed us to think about data science as a course and have the support within our department to begin building.
We focused on building a path and a course that would support employability while keeping transfer opportunities available. Since the curriculum approval process is long, we first developed a three-credit hour elective course for students with credits left in their degree path. In partnership with UChicago, our jointly-appointed preceptors, and academic department leadership, we adjusted UChicago’s Data Science 118 course to launch a new course at Truman that would meet our needs of introducing data science to our students (see course description below). At the same time, we are working towards a more extensive analysis of the “type” of program we should pursue and how to scale for instructional needs and student interest.
Inspiration to start the course
The course curriculum and program development is rooted in the college’s effort to support our students entering STEM fields while building towards innovative opportunities. Through the partnership with UChicago, and the preceptors, we built a course we call “299” that introduces data science concepts to students. This course also was inspired as a way to test our student interest in studying data science and to prepare students to apply for the co-curricular experience at the Data Science for Social Impact Summer program (DSSI). Students that successfully completed the 299 course were eligible to apply to participate in DSSI.
Course Example
Data has become increasingly available in recent years, not just to scientists but to the average citizen as well. With this information at our fingertips, there is increased need for training in how to use and understand this data. In this course, we will grapple with how to use data to study the world around us. We will learn how to collect and store data, use programming languages to explore data, and study methods for making inferences about that data. In addition to learning to manipulate and test data, together we will discuss when and how to use data and whether data is always neutral. How might data exacerbate existing biases? When is the use of data appropriate? How can we be more critical consumers of the data that is constantly being presented to us? We will learn to see data in our everyday lives and work with datasets that are exciting and relevant to ourselves and our community.
Key Supports include the preceptors and department leadership. The preceptors employed at UChicago and Truman College, have experience teaching the UChicago Data Science 118 course and were able to bring their knowledge about the content and instructional strategies to Truman. Our department leadership also did extensive research about the data science content at our main transfer institutions to ensure that students are moving on to their next opportunities well-prepared.
There are two main barriers that need to be addressed which include content pacing and prerequisites. In the first iteration of the course, a lot of content was scheduled and the faculty member teaching had to adjust the curriculum at midterm. In the second, revised, offering of the course faculty opted to focus on depth of knowledge and adjusted the amount of content to be covered. The second barrier for consideration is course prerequisites. In both interactions of the course, we opted to not have prerequisites. This means we may have students who have minimal math and coding skills- if any. While we are finding that the course is mainly taken by CIS students we are concerned that students without strong math or coding skills may struggle if they enroll.
As the college continues to grow and formalize the data science program, we need to consider the current course and additional curriculum development, transfer partnerships, employment opportunities, and recruitment of students. Truman must work with industry partners and four-year institutions in curriculum development to finalize course sequences that meet industry and transfer expectations across multiple universities.
We have been excited about the interest from students as launching new career programs can make it challenging to scale student interest with industry needs. We have been able to run courses for multiple semesters with growing student interest. A consideration for schools working towards this is also around faculty availability. Our UChicago preceptor partnership has been vital for us to have the knowledge and instructional capacity to build a curriculum and have instructors for the courses.
Amount of content in a course: In our first iteration of the 299 course, we found that course was too dense and content needed to be spread out among multiple courses. While we build a full program, we will need to keep an eye on how much content goes into each course and how to properly scaffold learning to meet the needs of transfer and employer partners. When you are building a course, the amount of depth and breadth you decide to do in a course is an important conversation to have within your departments and with partners.
Pre-requisites: This consideration has been one of the more difficult for faculty. Faculty are focused on finding ways to engage more students in data science while also recongizing when preparing students for transfer and employment, math and coding skills will be essential to long-term success. Currently, the faculty are keeping the initial class open to all students that may be interested in learning about data science, but this may change in the future. This is an important conversation for you to explore as it will impact your content and course outcome expectations.
Course Sequencing and institutional knowledge: We are exploring if this course sequence makes sense to be developed within a strictly transfer path focus for students or within a career pathway that would support employment for students while also helping them towards a baccalaureate degree.
For community colleges, partner with universities near you doing this work. The faculty capacity and support they can offer can help you keep pace in fast-changing fields.
For community colleges, partner with universities near you doing this work. The faculty capacity and support they can offer can help you keep pace in fast-changing fields.
A-ha Moment
In new fields, community colleges can be hesitant to jump in due to scaling challenges. Students, faculty, and partners are excited to come together and build new things. Building in exciting opportunities like the DSSI for students was an excellent way to spark their interest in the field, and engage with partners.
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