NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 93 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Williamson, Joanna; Vidal Rodeiro, Carmen – Cambridge University Press & Assessment, 2022
The transition from Key Stage 4 (KS4) to post-16 education marks a shift from a broad programme of study (around 8-10 subjects) to a narrower programme (usually 1-4 subjects). Within the constraints of course availability and entry criteria, students must make important choices. Students' post-16 pathways matter because different subjects as well…
Descriptors: Class Rank, Exit Examinations, Postsecondary Education, Foreign Countries
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
Direct linkDirect link
Ko, Chia-Yin; Leu, Fang-Yie – IEEE Transactions on Education, 2021
Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how…
Descriptors: Undergraduate Students, Student Characteristics, Academic Achievement, Predictor Variables
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4  |  5  |  6  |  7