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Haeryun Kim – Policy Futures in Education, 2025
This study explores how high school computer science (CS) course enrollment differs by student background through an intersectional lens. I use statewide data from North Carolina that contains longitudinal student-level background and course-taking information from 2005-2006 to the 2018-2019 school year and estimate linear probability models…
Descriptors: Computer Science Education, Course Selection (Students), High School Students, Intersectionality
Ying Chen; Stephanie M. Werner – Illinois Workforce and Education Research Collaborative, Discovery Partners Institute, 2024
The purpose of "The State of Computer Science in Illinois High Schools Series" is to analyze the landscape, structures, and pathways to and through computer science (CS) education in Illinois and to create a baseline by which to measure the expansion of CS education in the coming years. The series will include five reports, each…
Descriptors: Computer Science Education, High School Students, Enrollment Trends, Grade 9
Sturludóttir, Erla Guðrún; Arnardóttir, Eydís; Hjálmtýsson, Gísli; Óskarsdóttir, María – International Educational Data Mining Society, 2021
Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network…
Descriptors: Course Selection (Students), Undergraduate Students, Engineering Education, Business Administration Education
Bruno, Paul; Lewis, Colleen M. – Educational Administration Quarterly, 2022
Purpose: We aim to better understand the curricular, staffing, and achievement trade-offs entailed by expansions of high-school computer science (CS) for students, schools, and school leaders. Methods: We use descriptive, correlational, and quasi-experimental methods to analyze statewide longitudinal course-, school-, and staff-level data from…
Descriptors: High School Students, Computer Science Education, Educational Trends, Course Selection (Students)
Jing Liu; Cameron Conrad; David Blazar – Annenberg Institute for School Reform at Brown University, 2024
This study provides the first causal analysis of the impact of expanding Computer Science (CS) education in U.S. K-12 schools on students' choice of college major and early career outcomes. Utilizing rich longitudinal data from Maryland, we exploit variation from the staggered rollout of CS course offerings across high schools. Our findings…
Descriptors: Computer Science Education, Equal Education, Access to Education, Majors (Students)
Education Week, 2023
Schools experienced a massive expansion of technology over the past few years. They put in place 1-to-1 computing programs, are now using a record number of digital learning tools, and improved the availability of home-internet access for students. It was essentially a "fast-forward" for the evolution of digital learning in schools. But…
Descriptors: Educational Technology, Technology Uses in Education, Computer Science Education, High School Students
Katharine Childs; Sue Sentance – International Journal of Computer Science Education in Schools, 2024
Gender balance in computing education is a decades-old issue that has been the focus of much previous research. In K-12, the introduction of mandatory computing education goes some way to giving all learners the opportunity to engage with computing throughout school, but a gender imbalance still persists when computer science becomes an elective…
Descriptors: Computer Science Education, Females, Student Attitudes, Elementary School Students
Jonas Tillmann; Claas Wegner – International Journal of Research in Education and Science, 2024
Satellite laboratories, designed to ignite interest in technical and computer science topics, employ cross-age peer tutoring and physical computing platforms. Catering to students from 5th grade onwards, these laboratories are led by tutors from 9th grade onwards. Employing a design-based research approach, the project aims to comprehensively…
Descriptors: Computer Science Education, Tutoring, STEM Education, Secondary School Students
Stephanie M. Werner; Ying Chen – Illinois Workforce and Education Research Collaborative, Discovery Partners Institute, 2024
The purpose of "The State of Computer Science in Illinois High Schools Series" is to analyze the landscape, structures, and pathways of computer science (CS) education in Illinois and to create a baseline by which to measure the expansion of CS education in the coming years. Beginning in the 2023-2024 school year, all districts in the…
Descriptors: Computer Science Education, High School Students, Student Characteristics, Secondary School Curriculum
Ran Wei – Cogent Education, 2024
The utilisation of the RIASEC Theory, based on the Holland Code, has gained substantial popularity in the realm of career planning. Nevertheless, only a limited number of studies have explored the potential influence of the six personality types identified in the Realistic-Investigative-Artistic-Social-Enterprising-Conventional Theory (RIASEC…
Descriptors: Foreign Countries, Undergraduate Students, Educational Theories, Career Choice
Kristina Kramarczuk – ProQuest LLC, 2024
As technology continues to permeate all aspects of modern society, it is critical for PK-12 students to participate in computer science (CS) learning opportunities that prepare them to navigate and leverage technology in their future careers. However, research consistently shows that Black, Hispanic/Latino/a/x, and Native American students,…
Descriptors: Computer Science Education, Principals, Equal Education, Administrator Attitudes
Gulzar, Zameer; Raj, L. Arun; Leema, A. Anny – International Journal of Information and Communication Technology Education, 2019
Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests.…
Descriptors: Electronic Learning, Course Selection (Students), Educational Technology, Information Retrieval
Sax, Linda J.; Newhouse, Kaitlin N. S.; Goode, Joanna; Nakajima, Tomoko M.; Skorodinsky, Max; Sendowski, Michelle – ACM Transactions on Computing Education, 2022
The Advanced Placement Computer Science Principles (APCSP) course was introduced in 2016 to address long-standing gender and racial/ethnic disparities in the United States among students taking Advanced Placement Computer Science (APCS) in high school, as well as among those who pursued computing majors in college. Although APCSP has drawn a more…
Descriptors: Advanced Placement Programs, Computer Science Education, Equal Education, High School Students
Pinson, Halleli; Feniger, Yariv; Barak, Yael – Journal of Research in Science Teaching, 2020
In the past three decades in high-income countries, female students have outperformed male students in most indicators of educational attainment. However, the underrepresentation of girls and women in science courses and careers, especially in physics, computer sciences, and engineering, remains persistent. What is often neglected by the vast…
Descriptors: Foreign Countries, High School Students, Semitic Languages, Gender Differences
Tatel, Corey E.; Lyndgaard, Sibley F.; Kanfer, Ruth; Melkers, Julia E. – Journal of Learning Analytics, 2022
As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics…
Descriptors: Course Selection (Students), Enrollment Trends, Academic Achievement, Learning Analytics

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