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To increase the number of minorities that enter highly demanding computing field, a conversational agent is being developed to serve as a supplemental virtual mentor for minority students pursuing the professoriate. As minority engineering and computing professoriate interest is limited in the literature, it is important to identify the level of interest and concerns minority students have in pursuing the professoriate. A survey was administered to undergraduate students at an HBCU enrolled in one of three computer science courses. The results identify likelihood of pursuing the professoriate, persuasive and discouragement themes for pursuing the professoriate, as well as frequently asked questions over several variables of the minority STEM professoriate experience. Questions and data on question type, grammar, syntax and word choice will be used to improve the knowledgebase of the virtual mentor.
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This content will become publicly available on April 24, 2026
Exploring the Evolution of High School Students’ Questions in an Interest-Driven Data Science Curriculum
This study investigates the data science inquiry process of high school students from populations historically excluded in computing-related fields. We analyzed 213 student-generated questions from the final project of a newly implemented interest-driven data science curriculum. We used a qualitative analytic approach to identify dominant themes of interest and assess question complexity and scope through four stages of data collection. Findings reveal a shift from descriptive to more complex, evaluative, and exploratory questions. Students asked questions from diverse themes, with music and animals being the most common. These insights highlight the importance of scaffolding, culturally relevant content, and adaptive instructional strategies in data science education to empower students from marginalized backgrounds and foster their engagement and success in the field.
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- Award ID(s):
- 2141655
- PAR ID:
- 10653764
- Publisher / Repository:
- American Educational Research Association 2025 Annual Meeting
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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To increase the number of minorities that enter highly demanding computing field, a conversational agent is being developed to serve as a supplemental virtual mentor for minority students pursuing the professoriate. As minority engineering and computing professoriate interest is limited in the literature, it is important to identify the level of interest and concerns minority students have in pursuing the professoriate. A survey was administered to undergraduate students at an HBCU enrolled in one of three computer science courses. The results identify likelihood of pursuing the professoriate, persuasive and discouragement themes for pursuing the professoriate, as well as frequently asked questions over several variables of the minority STEM professoriate experience. Questions and data on question type, grammar, syntax and word choice will be used to improve the knowledgebase of the virtual mentor.more » « less
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Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students’ learning. Supplementary materials for this article are available online.more » « less
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In today's data-driven world, students must be able to explore and analyze the data surrounding them. A crucial aspect of this process is formulating meaningful research questions that can be addressed with the available data. This study investigates the data science inquiry process of high school students. We analyzed 213 student-generated questions from the final project of an innovative interest-driven data science curriculum. Through a qualitative analytic approach, we examined changes in question types, complexity, and scope across four stages of data collection. The findings shed light on a shift from descriptive to more complex, evaluative, and exploratory questions. It also highlights the importance of providing scaffolding, culturally relevant content, and adaptive instructional strategies in data science education. These elements are essential for empowering students from marginalized backgrounds and fostering their engagement and success in the field.more » « less
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