Publication Date
In 2025 | 1 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
Computational Linguistics | 4 |
Computer Science Education | 4 |
Introductory Courses | 4 |
Programming | 4 |
Artificial Intelligence | 2 |
College Faculty | 2 |
College Students | 2 |
Computer Software | 2 |
Feedback (Response) | 2 |
Foreign Countries | 2 |
Teacher Attitudes | 2 |
More ▼ |
Source
Information and Learning… | 1 |
International Educational… | 1 |
Journal of Learning Analytics | 1 |
Journal of Research on… | 1 |
Author
Atapattu, Thushari | 1 |
Cambronero, José | 1 |
Chung, Cheng-Yu | 1 |
Falkner, Katrina | 1 |
Gulwani, Sumit | 1 |
Hsiao, I-Han | 1 |
Kohn, Tobias | 1 |
Lin, Yi-Ling | 1 |
Majumdarm, Rupak | 1 |
Mark G. Dawson | 1 |
Phung, Tung | 1 |
More ▼ |
Publication Type
Journal Articles | 3 |
Reports - Research | 3 |
Reports - Evaluative | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Audience
Location
Australia | 1 |
Germany (Berlin) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
Chung, Cheng-Yu; Hsiao, I-Han; Lin, Yi-Ling – Journal of Research on Technology in Education, 2023
Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without…
Descriptors: Artificial Intelligence, Programming, Questioning Techniques, Heterogeneous Grouping
Samuel Boguslawski; Rowan Deer; Mark G. Dawson – Information and Learning Sciences, 2025
Purpose: Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision. Design/methodology/approach: Twelve students and six members of faculty…
Descriptors: Programming, Computer Science Education, Personal Autonomy, Learning Motivation
Atapattu, Thushari; Falkner, Katrina – Journal of Learning Analytics, 2018
Lecture videos are amongst the most widely used instructional methods within present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, student engagement behaviour, including interaction with videos, directly impacts the student success or failure and accordingly, in-video dropouts…
Descriptors: Lecture Method, Video Technology, Online Courses, Mass Instruction