WIP: Visualizing Success: An AI-Enhanced Interactive Conceptual Timeline for First-Generation Computer Science Students
This work-in-progress innovative practice paper presents an interactive visual map tool called 1 GenSuccess, designed for first-generation computer science students. First-generation college students are students whose parents or guardians have not attended college or earned a college degree. In the United States, first-generation students account for over 40% of first-year college students, making them a significant demo-graphic in the engineering and computing educational field. How-ever, due to a lack of parental familiarity with higher education, limited understanding of the college admissions process, finan-cial constraints, internships, and the challenges associated with engineering and computing coursework, first-generation students in the field of engineering and computing frequently encounter distinctive obstacles across academic, finance, career, cultural, and social dimensions. Recognizing the difficulties these students face, this paper introduces 1 GenSuccess, a novel roadmap web application developed specifically for first-generation college stu-dents in the engineering and computing fields. While numerous web sites currently offer a range of valuable resources to support first-generation college students, a significant gap persists-a lack of a personalized roadmap explicitly crafted to meet the distinct needs and challenges they experience on their computer science educational journey. The primary goal of 1 GenSucccess is to empower engineering students and facilitate their navigation through academia through a visual roadmap. In addition, by presenting a comprehensive suite of resources, support materials, and a personalized roadmap, 1 GenSuccess aims to bridge the gaps in access and information that often impede the progress of first-generation college students in the computer science field.
Authors
- Anna Baynes
- Huiheng Liao
Institutions
- California State University, Sacramento (US)
Publication Details
- Published
- 2024-10-13
- DOI
- https://doi.org/10.1109/fie61694.2024.10892963
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00