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Chung-Yuan Hsu; Meng-Jung Tsai – Journal of Educational Computing Research, 2024
This research aimed to investigate the structural relationships among teachers' computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the…
Descriptors: Robotics, Computer Science Education, Factor Analysis, Technological Literacy
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Sanal Kumar T. S.; R. Thandeeswaran – Education and Information Technologies, 2024
The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These platforms never considered the essential attributes of student characteristics and learning preferences while designing such a problematic subject having high dropout and failure rates. The traditional…
Descriptors: Blended Learning, Electronic Learning, Higher Education, Programming
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Sandie Han; Nadia Stoyanova Kennedy; Diana Samaroo; Urmi Duttagupta – PRIMUS, 2024
This paper describes the implementation of a STEM scholarship program which utilized a holistic approach to providing a multi-dimensional student support system. The program has been successful in encouraging and supporting women in Applied Mathematics and Computer Science by offering a diverse suite of extracurricular opportunities, actively…
Descriptors: Undergraduate Students, Females, Mathematics Education, Computer Science Education
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Donald M. Johnson; Will Doss; Christopher M. Estepp – Natural Sciences Education, 2024
Microcontrollers are widely used in agriculture, yet most undergraduate agriculture students do not have the programming skills necessary to make use of these devices in their academic programs or careers. However, generative artificial intelligence (AI) chatbots, such as ChatGPT, have the ability to write complex microcontroller programs when…
Descriptors: Undergraduate Students, Agricultural Education, Programming, Artificial Intelligence
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Shetay Ashford-Hanserd; Lillianna Franco Carrera; Twyla D. Hough; Toni D. Moreno; April J. Mouton; Ila Wallace – Career and Technical Education Research, 2024
To promote racial and gender equity in the field of science, technology, engineering, mathematics, and computing (STEM+C), we investigated prominent influences on historically underrepresented minority (HURM) women's persistence in undergraduate education. With the underrepresentation of Black and Hispanic women in the computing workforce, this…
Descriptors: STEM Education, Undergraduate Students, Females, African American Students
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Meghan L. McSherry; Becca A. Leininger; Maria L. Johnson; AnnMarie P. Thomas; Susan Outlaw; Douglas C. Orzolek – Journal of Science Education for Students with Disabilities, 2024
The purpose of this research project was to design, deliver, and informally assess the content and methodology of an introductory online course for the educators of Deaf and Hard of Hearing students (N=20) that focused on the block-based coding language, Scratch. Prior work by The Playful Learning Lab examined various STEM resources and their…
Descriptors: Deafness, Hard of Hearing, Online Courses, Summer Programs
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Maertens, Rien; Van Petegem, Charlotte; Strijbol, Niko; Baeyens, Toon; Jacobs, Arne Carla; Dawyndt, Peter; Mesuere, Bart – Journal of Computer Assisted Learning, 2022
Background: Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use…
Descriptors: Plagiarism, Benchmarking, Coding, Computer Science Education
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Cuervo-Cely, Karen D.; Restrepo-Calle, Felipe; Ramírez-Echeverry, Jhon J. – Journal of Information Technology Education: Research, 2022
Aim/Purpose: The purpose of this research is to examine the effect of computer-assisted gamification on the learning motivation of computer programming students. Background: The teaching-learning of computer programming involves challenges that imply using learning environments in which the student is actively involved. Gamification is an…
Descriptors: Game Based Learning, Student Motivation, Computer Science Education, Programming
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Gutiérrez, Luz E.; Guerrero, Carlos A.; López-Ospina, Héctor A. – Education and Information Technologies, 2022
This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked…
Descriptors: Programming, Comprehension, Computer Science Education, Computer Software
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Albó, Laia; Barria-Pineda, Jordan; Brusilovsky, Peter; Hernández-Leo, Davinia – International Journal of Artificial Intelligence in Education, 2022
Over the last 10 years, learning analytics have provided educators with both dashboards and tools to understand student behaviors within specific technological environments. However, there is a lack of work to support educators in making data-informed design decisions when designing a blended course and planning appropriate learning activities. In…
Descriptors: Learning Analytics, Visual Aids, Design, Learning Activities
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Heckman, Sarah; Carver, Jeffrey C.; Sherriff, Mark; Al-zubidy, Ahmed – ACM Transactions on Computing Education, 2022
Context: Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly…
Descriptors: Computer Science Education, Educational Research, Periodicals, Replication (Evaluation)
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Webb, Kevin C.; Zingaro, Daniel; Liao, Soohyun Nam; Taylor, Cynthia; Lee, Cynthia; Clancy, Michael; Porter, Leo – ACM Transactions on Computing Education, 2022
A Concept Inventory (CI) is an assessment to measure student conceptual understanding of a particular topic. This article presents the results of a CI for basic data structures (BDSI) that has been previously shown to have strong evidence for validity. The goal of this work is to help researchers or instructors who administer the BDSI in their own…
Descriptors: Measures (Individuals), Concept Formation, Computer Science Education, Test Results
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Snead, Suzanne L.; Walker, Lyndon; Loch, Birgit – International Journal of Mathematical Education in Science and Technology, 2022
In Australian universities, mathematics is often an essential core subject for disciplines such as engineering, information technology and science. These first-year mathematics subjects may have large enrolments of which a significant number fail the subject and repeat. While interventions to retain students are common, those who have failed…
Descriptors: Foreign Countries, College Mathematics, Mathematics Achievement, College Freshmen
McDonnell, Rachel Pleasants; Fraser, Shakari; Sullivan, Felicia; Webber, Alison Ascher; Vanek, Jen; Harris, Jamie – Jobs for the Future, 2022
With the urgency of adult digital skill development as a backdrop, the Digital Resilience in the American Workforce (DRAW) initiative, funded by the U.S. Department of Education's Office of Career, Technical, and Adult Education (OCTAE), conducted a national landscape scan to identify existing resources and effective approaches for digital skills…
Descriptors: Adult Students, Adult Learning, Technological Literacy, Computer Literacy
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Paiva, José Carlos; Leal, José Paulo; Figueira, Álvaro – ACM Transactions on Computing Education, 2022
Practical programming competencies are critical to the success in computer science (CS) education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not…
Descriptors: Automation, Computer Assisted Testing, Student Evaluation, Computer Science Education
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