Data Science Education

Speakers and abstracts for the Session #2 talks: Data Science Education


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Sung-Hee Nam  (Sung) 

Speaker Affiliation: Senior Instructor, Computer Science and Engineering, CU Denver

Title: ChatGPT's Influence in Undergraduate Computer Science Foundation and Core Class Education

Abstract: The widespread use of ChatGPT in computer science undergraduate core classes has had a significant impact on the assessment of students' learning experiences.  A preliminary assessment was conducted through a non-scientific informal survey involving various foundational course instructors.  The finding suggests that the quality of ChatGPT-generated responses has been generally received, with instructors awarding grades in the A and B range.

These high-quality responses might require more comprehensive evaluations of students' understanding of fundamental computer science concepts. Nonetheless, it also highlights the need for careful monitoring and academic integrity to ensure the authenticity of student work.

As ChatGPT continues to be integrated into computer science (or any field of) education, ongoing research, and refinement of assessment practices will be vital to leverage its potential while maintaining the rigor and fairness of evaluations in undergraduate core and foundation classes.

Perhaps, now is the moment to pause and closely observe its technological evolutionary trajectory at this point.

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Joshua French

Speaker Affiliation: Associate Professor, Department of Mathematical and Statistical Sciences, CU Denver

Title: Quarto, Jupyter, and interactive data analysis for students

Abstract: We will discuss a workflow that allows faculty to easily create reproducible materials that allows students to interactively reproduce and easily create new analysis. The workflow allows version control, easier accessibility for students, and creates richer learning experiences in the classroom.

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Dawn Gregg

Speaker Affiliation: Associate Dean, Professor, CU Denver Business School

Title: Enhancing Education with ChatGPT

Abstract: One of the biggest challenges in academia is that education isn’t individualized despite the differing needs of a large, diverse body of students. In my coding classes I have students with strong coding backgrounds and other students with no coding experience at all. This makes it difficult to create a class where every student can learn and enhance their skills.

The adoption of generative AI tools as a learning tutor as well as a tool to improve code quality has transformed my class.  Each student has the opportunity to receive customized support. I feel using Chat GPT in this way gives students the chance to easily grasp concepts but also prepares them for a competitive workplace, which is increasingly turning to tools like ChatGPT to automate many of their coding process as well.
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Cameron Blevins

(Has had to cancel)

Speaker Affiliation: Associate Professor, Interim Director of Digital Initiatives, Department of History, CU Denver

Title: The Data Advocacy For All Project

Abstract: Data Advocacy For All is an open-access curriculum project geared towards helping students learn how to inquire with data, communicate with data, and deploy data with a goal of creating more just futures. Funded through a CU Next Award for intercampus pedagogical innovation, the project is being piloted at both CU Boulder and CU Denver. This presentation will offer an overview of the project’s pedagogical principles and how it fits within the larger landscape of data science curriculum.

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Steve Delcastillo

Speaker Affiliation: New Directions in Politics and Public Policy Program Director, CU Denver

Title: Developing a Rural Talent Economy Through Rural High-Tech Hubs

Abstract: The purpose of the session is to present the model of a Rural High Tech Hub and how such Hubs can facilitate talent and economic development in rural communities. The Hub is comprised of three components: 1) Data Science Certificate; 2) sponsoring Internships/Apprenticeships by local businesses to assist in local technological development; and 3) Innovation Labs for promoting economic development.

The Data Science component provides the academic basis for preparing local Data Science Technicians while the business partnerships present the chance for experiential learning. The Innovation Labs promote community involvement and encourage youth to remain in their respective rural communities.

The Rural High-Tech Hub strategy can contribute to the stabilization of rural communities by leveraging the technological skills of the local rural youth. The results will be a flourishing intellectual infrastructure that can benefit the region, the state of Colorado, and the nation
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