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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
Shah, Zohal; Chen, Chen; Sonnert, Gerhard; Sadler, Philip M. – AERA Online Paper Repository, 2023
Computer gameplay and social media are the two most common forms of entertainment in the digital age. Many scholars share the assumption that leisure-time digital consumption is associated with CS affinity, but there is a dearth of research evidence for this relationship. Female students generally spend less time on gaming and more time on social…
Descriptors: Computer Science, Vocational Interests, Computer Use, Gender Differences
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
Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
Karaoglan Yilmaz, Fatma Gizem – Technology, Pedagogy and Education, 2022
The purpose of this study is to examine the effect of student satisfaction on students' engagement and motivation in a mobile-based flipped classroom. A total of 222 university students taking the Computing I course designed as a mobile-based flipped classroom were recruited. Data were collected through three self-report instruments: the…
Descriptors: Flipped Classroom, Learner Engagement, Student Motivation, Student Satisfaction
Chen, Chen; Jeckel, Stuart; Sonnert, Gerhard; Sadler, Philip M. – International Journal of Computer Science Education in Schools, 2019
This study examines the relationship between students' pre-college experience with computers and their later success in introductory computer science classes in college. Data were drawn from a nationally representative sample of 10,197 students enrolled in computer science at 118 colleges and universities in the United States. We found that…
Descriptors: Computer Science Education, Programming, Academic Achievement, College Students
Smith, Julie M. – Journal of Computers in Mathematics and Science Teaching, 2020
In contrast to the experience of other professional fields, the percentage of women in computer science has decreased substantially in recent decades. This phenomenon is a significant and growing problem in a society where new technologies impact nearly every facet of life, including criminal justice and health care. This study examines whether…
Descriptors: Females, Computer Science Education, College Students, Predictor Variables
Gurung, Regan A. R.; Mai, Theresa; Nelson, Matthew; Pruitt, Sydney – Teaching of Psychology, 2022
Background: Instructors and students are on a continuing quest to identify predictors of learning. Objective: This study examines the associations between self-reported exam score and study techniques among students in two courses, Introductory Psychology and Computer Science. Method: We used an online survey to measure the extent students (N =…
Descriptors: Predictor Variables, Study Skills, Thinking Skills, Metacognition
Chen, Chen; Haduong, Paulina; Brennan, Karen; Sonnert, Gerhard; Sadler, Philip – Computer Science Education, 2019
Background and Context: The relationship between novices' first programming language and their future achievement has drawn increasing interest owing to recent efforts to expand K-12 computing education. This article contributes to this topic by analyzing data from a retrospective study of more than 10,000 undergraduates enrolled in introductory…
Descriptors: Computer Science Education, Programming Languages, College Students, Computer Attitudes
Adkins, Joni K.; Linville, Diana R.; Badami, Charles – Information Systems Education Journal, 2020
Online textbooks allow instructors to provide interactive and engaging activities for students. In this paper, we look at how providing an interactive online textbook is utilized and valued in a beginning computer programming course. In addition, we compare the utilization of the online textbook to the student final course grade. Our findings…
Descriptors: Instructional Effectiveness, Introductory Courses, Programming, Computer Science Education
Alvarez, Niurys Lázaro; Callejas, Zoraida; Griol, David – Journal of Technology and Science Education, 2020
We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not…
Descriptors: Foreign Countries, College Students, Computer Science Education, Engineering Education
Ruiz, Samara; Urretavizcaya, Maite; Rodríguez, Clemente; Fernández-Castro, Isabel – Interactive Learning Environments, 2020
A positive emotional state of students has proved to be essential for favouring student learning, so this paper explores the possibility of obtaining student feedback about the emotions they feel in class in order to discover emotion patterns that anticipate learning failures. From previous studies about emotions relating to learning processes, we…
Descriptors: College Students, Computer Science Education, Emotional Response, Student Reaction
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
Sheshadri, Adithya; Gitinabard, Niki; Lynch, Collin F.; Barnes, Tiffany; Heckman, Sarah – International Educational Data Mining Society, 2018
Online tools provide unique access to research students' study habits and problem-solving behavior. In MOOCs [Massive Open Online Courses], this online data can be used to inform instructors and to provide automatic guidance to students. However, these techniques may not apply in blended courses with face to face and online components. We report…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
Kong, Siu-Cheung; Wang, Yi-Qing – Computer Science Education, 2019
Background and Context: Positive youth programming development (PYPD) was conceptualized to measure various positive qualities of students in programming education. Objective: This study aimed to develop a valid PYPD instrument in the pilot before exploring students' positive qualities in two follow-up studies. Method: A multi-study design was…
Descriptors: Computer Science Education, Programming, College Students, Test Validity