Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 5 |
| Since 2017 (last 10 years) | 5 |
| Since 2007 (last 20 years) | 9 |
Descriptor
| Computer Science Education | 10 |
| Course Selection (Students) | 10 |
| Student Characteristics | 10 |
| Gender Differences | 5 |
| Majors (Students) | 5 |
| Foreign Countries | 4 |
| Engineering Education | 3 |
| High School Students | 3 |
| Longitudinal Studies | 3 |
| Academic Achievement | 2 |
| Career Choice | 2 |
| More ▼ | |
Source
Author
| Stephanie M. Werner | 2 |
| Ying Chen | 2 |
| Arjona-Villicaña, P. David | 1 |
| Berglund, Anders | 1 |
| Castillo-Barrera, F. Edgar | 1 |
| Chen, Xianglei | 1 |
| Haeryun Kim | 1 |
| Ho, Phoebe | 1 |
| Kanfer, Ruth | 1 |
| Kordaki, Maria | 1 |
| Lyndgaard, Sibley F. | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 6 |
| Reports - Research | 6 |
| Reports - Evaluative | 3 |
| Numerical/Quantitative Data | 2 |
| Reports - Descriptive | 1 |
| Tests/Questionnaires | 1 |
Education Level
| Higher Education | 7 |
| Postsecondary Education | 6 |
| High Schools | 3 |
| Secondary Education | 3 |
| Grade 10 | 1 |
| Grade 11 | 1 |
| Grade 12 | 1 |
| Grade 9 | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Two Year Colleges | 1 |
| More ▼ | |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
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
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
Thota, Neena; Berglund, Anders – ACM Transactions on Computing Education, 2016
We know from research that there is an intimate relationship between student learning and the context of learning. What is not known or understood well enough is the relationship of the students' background and previous studies to the understanding and learning of the subject area--here, computer science (CS). To show the contextual influences on…
Descriptors: Computer Science Education, Asians, Foreign Countries, Graduate Students
Silva-Maceda, Gabriela; Arjona-Villicaña, P. David; Castillo-Barrera, F. Edgar – IEEE Transactions on Education, 2016
Learning to program is a complex task, and the impact of different pedagogical approaches to teach this skill has been hard to measure. This study examined the performance data of seven cohorts of students (N = 1168) learning programming under three different pedagogical approaches. These pedagogical approaches varied either in the length of the…
Descriptors: Programming, Teaching Methods, Intermode Differences, Cohort Analysis
Chen, Xianglei; Ho, Phoebe – National Center for Education Statistics, 2012
Science, technology, engineering, and mathematics (STEM) fields are widely regarded as critical to the national economy. To provide a nationally representative portrait of undergraduate students' experiences in STEM education, these Web Tables summarize longitudinal data from a cohort of first-time, beginning students who started postsecondary…
Descriptors: STEM Education, Postsecondary Education, Undergraduate Students, Student Attrition
Tsagala, Evrikleia; Kordaki, Maria – Themes in Science and Technology Education, 2008
This study focuses on how Computer Science and Engineering Students (CSESs) of both genders address certain critical issues for gender differences in the field of Computer Science and Engineering (CSE). This case study is based on research conducted on a sample of 99 Greek CSESs, 43 of which were women. More specifically, these students were asked…
Descriptors: Computer Science Education, Engineering Education, College Students, Gender Differences
Micceri, Theodore – Online Submission, 2005
The purpose of this exercise was to determine whether any of the available demographic or academic variables show distinct trends in three specific discipline areas that differ from those of other areas: (1) Engineering, (2) Computer Sciences, and (3) Biological Sciences. Using data from 39,087 SUS graduates in 2002-03 and of 324,164 science…
Descriptors: Physics, Ethnic Groups, Biology, Transfer Students

Peer reviewed
Direct link
