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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
Melissa T. A. Simarmata; Gwo-Guang Lee; Hoky Ajicahyadi; Kung-Jeng Wang – Education and Information Technologies, 2024
Teaching computer programming language remotely presents particular difficulties due to its requirement for abstract and logical thinking. There is a dearth of research specifically examining the potential factors that determine student performance when distance self-learning is conducted for programming language education. This study aims to…
Descriptors: Distance Education, Independent Study, Computer Science Education, Programming
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
Kaitlyn Nicole Stormes – ProQuest LLC, 2024
Despite efforts to increase representation among those enrolled, earning degrees, and working in the computing and technology industry, women across races/ethnicities and People of Color more broadly remain underrepresented in the field. Fortunately, extant literature has found that psychosocial factors like computing identity can help broaden…
Descriptors: Self Efficacy, Sense of Community, Predictor Variables, Self Concept
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation
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
Lihui Sun; Danhua Zhou – Journal of Computer Assisted Learning, 2024
Background: Integrating programming in K-12 curriculum has become a global consensus. Teachers are central figures in programming instruction. But the majority of current research focuses on teachers' external teaching behaviours and less on teachers' attitudes towards programming. Objectives: The purpose of this study is to validate the K-12…
Descriptors: Foreign Countries, Elementary School Teachers, Secondary School Teachers, Teacher Attitudes
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
Umar Bin Qushem; Solomon Sunday Oyelere; Gökhan Akçapinar; Rogers Kaliisa; Mikko-Jussi Laakso – Technology, Knowledge and Learning, 2024
Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine…
Descriptors: Predictor Variables, Learning Analytics, At Risk Students, Computer Science
Qian Fu; Wenjing Tang; Yafeng Zheng; Haotian Ma; Tianlong Zhong – Interactive Learning Environments, 2024
In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…
Descriptors: Predictor Variables, Psychological Patterns, Programming, Self Management
Gülay Öztüre Yavuz; Gökhan Akçapinar; Hatice Çirali Sarica; Yasemin Koçak Usluel – Education and Information Technologies, 2024
This study aims to develop a predictive model for predicting gifted students' engagement levels and to investigate the features that are important in such predictions. Features reflecting students' emotions, social-emotional learning skills, learning approaches and video-watching behaviours were used in the prediction models. The study group…
Descriptors: Secondary School Students, Academically Gifted, Gifted Education, Learner Engagement
Silvia Wen-Yu Lee; Jyh-Chong Liang; Chung-Yuan Hsu; Meng-Jung Tsai – Interactive Learning Environments, 2024
While research has shown that students' epistemic beliefs can be a strong predictor of their academic performance, cognitive abilities, or self-efficacy, studies of this topic in computer education are rare. The purpose of this study was twofold. First, it aimed to validate a newly developed questionnaire for measuring students' epistemic beliefs…
Descriptors: Student Attitudes, Beliefs, Computer Science Education, Programming
Ndudi O. Ezeamuzie; Jessica S. C. Leung; Dennis C. L. Fung; Mercy N. Ezeamuzie – Journal of Computer Assisted Learning, 2024
Background: Computational thinking is derived from arguments that the underlying practices in computer science augment problem-solving. Most studies investigated computational thinking development as a function of learners' factors, instructional strategies and learning environment. However, the influence of the wider community such as educational…
Descriptors: Educational Policy, Predictor Variables, Computation, Thinking Skills

Sarah K. Mason; Matt Hancock; Izzy Thornton – Grantee Submission, 2024
Rural Mississippi schools often face challenges in providing equitable access to Advanced Placement (AP) courses, particularly in STEM fields. This lack of access limits opportunities for high-achieving students to pursue rigorous STEM coursework and related careers. The AP STEM program aims to improve access to AP STEM courses for high-achieving…
Descriptors: Rural Schools, STEM Education, Advanced Placement Programs, Access to Education