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Lin, Guan-Yu; Liao, Yi-Wen; Su, Zhi-Yuan; Wang, Yu-Min; Wang, Yi-Shun – Education and Information Technologies, 2023
This study attempts to: (a) investigate whether positive and negative emotions mediate the pathways linking self-efficacy for learning programming with effort and persistence in undergraduates' learning Scratch programming combining with a programmable hardware platform (i.e., Arduino), and (b) assess the effect of academic major (i.e.,…
Descriptors: Undergraduate Students, Academic Persistence, Programming, Self Efficacy
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
Patton, Belinda Andromeda – ProQuest LLC, 2020
The rise in demand for computer programming jobs has created a significant need for computer programming training. Online learning can be an effective tool for meeting the needs of these job demands. The challenge for universities is that computer programming is perceived as a difficult course by many students (Askar & Davenport, 2009; Baser,…
Descriptors: Student Attitudes, Undergraduate Students, Programming, Computer Science Education
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
Prasad, Archana; Lal, P.; Wolde, B.; Zhu, M.; Samanthula, B. K.; Panorkou, N. – Journal of STEM Outreach, 2022
Out-of-classroom activities can help cultivate interest and literacy in Science, Technology, Engineering and Mathematics (STEM) subjects. To determine how a week-long out-of-classroom experience might impact STEM interest in adolescents, a free summer camp was offered to students entering grades 6-8. During this time, students participated in…
Descriptors: STEM Education, Camps, Summer Programs, Academic Aspiration
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
Varga, Erika B.; Sátán, Ádám – Hungarian Educational Research Journal, 2021
The purpose of this paper is to investigate the pre-enrollment attributes of first-year students at Computer Science BSc programs of the University of Miskolc, Hungary in order to find those that mostly contribute to failure on the Programming Basics first-semester course and, consequently to dropout. Our aim is to detect at-risk students early,…
Descriptors: Identification, At Risk Students, Computer Science Education, Undergraduate Students
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
Kittur, Javeed – IEEE Transactions on Education, 2020
Contribution: This article has shown that self-efficacy in performing complex computer programming tasks and the self-regulation of electrical and electronics engineering undergraduate students varies with respect to the class standing and prior experience in computer programming. Background: Computer programming is an essential skill that all…
Descriptors: Measures (Individuals), Programming, Self Efficacy, Engineering Education
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
Durak, Hatice Yildiz – Journal of Educational Computing Research, 2020
Learning the basic concepts of programming and its foundations is considered as a challenging task for students to figure out. It is a challenging process for lecturers to learn these concepts, as well. The current literature on programming training abounds with the examples of a wide range of methods employed. Within this context, one of the…
Descriptors: Educational Technology, Technology Uses in Education, Programming, Teaching Methods
Weston, Timothy J.; Dubow, Wendy M.; Kaminsky, Alexis – ACM Transactions on Computing Education, 2020
While demand for computer science and information technology skills grows, the proportion of women entering computer science (CS) fields has declined. One critical juncture is the transition from high school to college. In our study, we examined factors predicting college persistence in computer science- and technology-related majors from data…
Descriptors: Females, Academic Persistence, High School Students, Computer Science 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