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Anjing Dai; Li Tan – Journal of Engineering Education, 2025
Background: Despite the vital function of engineering and computer science (Eng & CS) to innovation and economic development, retention within Eng & CS programs remains a major challenge in the U.S. educational system. Despite extensive research on influential factors of retention, there is a gap in our understanding of how these factors…
Descriptors: Undergraduate Students, Engineering Education, Computer Science Education, School Holding Power
Jennifer M. Blaney; David F. Feldon; Annie M. Wofford; Kaylee Litson – Research in Higher Education, 2025
Community college transfer students represent a diverse and talented group to recruit to PhD and other graduate programs. Yet, little is known about practical strategies to support community college transfer students' access to graduate training. Focusing specifically on transfer students in computer science and guided by social cognitive career…
Descriptors: College Transfer Students, Community College Students, Graduate Study, Access to Education
Hüseyin Gokal; Cem Ufuk Baytar – Turkish Online Journal of Educational Technology - TOJET, 2025
This study aims to examine university students' intentions to use artificial intelligence (AI) applications in their educational processes within the context of job characteristics (JC), technology characteristics (TC), task-technology fit (TTF), and self-efficacy (SE). The research was conducted with 965 students enrolled in Information…
Descriptors: College Students, Intention, Technology Uses in Education, Artificial Intelligence
Abraham E. Flanigan; Markeya S. Peteranetz; Duane F. Shell; Leen-Kiat Soh – ACM Transactions on Computing Education, 2023
Objectives: Although prior research has uncovered shifts in computer science (CS) students' implicit beliefs about the nature of their intelligence across time, little research has investigated the factors contributing to these changes. To address this gap, two studies were conducted in which the relationship between ineffective self-regulation of…
Descriptors: Computer Science Education, Self Concept, Intelligence, Self Management
Ella Christiaans; So Yeon Lee; Kristy A. Robinson – Educational Psychology, 2024
Students want to learn computer science due to its usefulness for future careers, however they often meet challenges in introductory courses. In the increasingly digital world, it is important to understand some important psychological consequences of such challenges: perceived costs of pursuing computer science. This study thus investigated…
Descriptors: Undergraduate Students, Computer Science Education, Psychological Patterns, Student Attitudes
Obeng, Asare Yaw – Cogent Education, 2023
The learning processes have been significantly impacted by technology. Numerous learners have adopted technology-based learning systems as the preferred form of learning. It is then necessary to identify the learning styles of learners to deliver appropriate resources, engage them, increase their motivation, and enhance their satisfaction and…
Descriptors: Predictor Variables, Cognitive Style, Electronic Learning, College Freshmen
Jiali Zheng; Melissa Duffy; Ge Zhu – Discover Education, 2024
Students in technology majors such as Computer Science and Information Technology need to take a series of computer programming courses to graduate. Yet, not all students will persist in taking programming courses as required, and little is known about the factors influencing their enrollment intentions. Research is needed to better understand…
Descriptors: Computer Science Education, Programming, Predictor Variables, Enrollment
Niklas Humble; Jonas Boustedt; Hanna Holmgren; Goran Milutinovic; Stefan Seipel; Ann-Sofie Östberg – Electronic Journal of e-Learning, 2024
Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant…
Descriptors: Cheating, Artificial Intelligence, Technology Uses in Education, Computer Science Education
Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Brett D. Jones; Xiao Zhu; Margaret Ellis; Zeynep Ambarkutuk; Hande Fenerci – European Journal of Engineering Education, 2025
To address the demand for engineers and computer scientists in the workforce, and the fact that some engineering students dropout or change majors, we explored how the motivational climate in an undergraduate computer science (CS) course was related to students' motivational beliefs and academic and career goals. Participants included 310 students…
Descriptors: Student Attitudes, Student Motivation, Beliefs, Occupational Aspiration
George, Kari L.; Sax, Linda J.; Wofford, Annie M.; Sundar, Sarayu – Research in Higher Education, 2022
Computing career opportunities are increasing across all sectors of the U.S. economy, yet there remains a serious shortage of college graduates to fill these jobs. This problem has fueled a nationwide effort to expand and diversify the computing career pipeline. Guided by social cognitive career theory (SCCT), this study used logistic regression…
Descriptors: College Environment, Career Choice, College Students, School Role
Belland, Brian R.; Kim, Chanmin; Zhang, Anna Y.; Lee, Eunseo – ACM Transactions on Computing Education, 2023
This article reports the analysis of data from five different studies to identify predictors of preservice, early childhood teachers' views of (a) the nature of coding, (b) integration of coding into preschool classrooms, and (c) relation of coding to fields other than computer science (CS). Significant changes in views of coding were predicted by…
Descriptors: Predictor Variables, Preservice Teachers, Student Attitudes, Programming
Khanittha Sittitiamjan; Pongpisit Wuttidittachotti – Educational Process: International Journal, 2025
Background/purpose: This study investigates how knowledge, attitudes, and practices (KAP) influence cybersecurity awareness (CSA) among students in Thai educational institutions. The research addresses regional disparities in cybersecurity readiness by incorporating a culturally responsive adaptation of the KAP model. Materials/methods: A…
Descriptors: Computer Security, Computer Science Education, Foreign Countries, College Students
Belland, Brian R.; Kim, ChanMin; Zhang, Anna Y.; Baabdullah, Afaf A.; Lee, Eunseo – IEEE Transactions on Education, 2021
Contribution: This study indicates that supporting debugging processes is a strong method to improve debugging outcome quality among preservice, early childhood education (ECE) teachers. Background: Central to preparing ECE teachers to teach computer science is helping them learn to debug. Little is known about how ECE teachers' motivation and…
Descriptors: Student Motivation, Predictor Variables, Preservice Teachers, Early Childhood Teachers
Blaney, Jennifer M.; Barrett, Julia – Community College Journal of Research and Practice, 2022
Supporting upward transfer students is critical to diversifying STEM. This study provides insight into how we can best support upward transfer students in computing, one of the least diverse STEM disciplines. Specifically, we expand upon recent research on sense of belonging to examine how the predictors of belonging might be unique for upward…
Descriptors: Gender Differences, Equal Education, Sense of Community, Computer Science Education

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