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We have created, piloted and are growing the Align program, a Master of Science in Computer Science (MS in CS) for post-secondary graduates who did not major in CS. Our goal is to create a pathway to CS for all students, with particular attention to women and underrepresented minorities. Indeed, women represent 57% and underrepresented minorities represent 25% of all bachelor's recipients in the U.S., but only 19.5% and 12.6% of CS graduates, respectively. If we can fill this opportunity gap, we will satisfy a major economic need and address an issue of social equity and inclusion. In this paper, we present our "Bridge'' curriculum, which is a two-semester preparation for students to then join the traditional MS in CS students in master's-level classes. We describe co-curricular activities designed to help students succeed in the program. We present our empirical findings around enrollment, demographics, retention and job outcomes. Among our findings is that Align students outperform our traditional MS in CS students in grade point average. To date we have graduated 137 students and 827 are enrolled.
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Evaluating Private Artificial Intelligence (AI) Curriculum in Computer Science (CS) Education: Insights for Advancing Student-Centered CS Learning
This study was undertaken to pilot a Private AI curriculum designed with a problemcentered instruction (PCI) approach for post-secondary Computer Science (CS) education. To this end, a condensed version of one of the ten curricular modules was implemented in a twohour workshop. The mixed-method data analysis revealed participants' positive motivation and interest in the curriculum, while also pinpointing opportunities to further improve the design strategies of the curriculum.
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- Award ID(s):
- 2413856
- PAR ID:
- 10582482
- Publisher / Repository:
- International Society of the Learning Sciences
- Date Published:
- Page Range / eLocation ID:
- 2271 to 2272
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.22318/icls2024.141269
