Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Computer Science Education | 3 |
Cues | 3 |
Programming Languages | 3 |
Artificial Intelligence | 2 |
Computational Linguistics | 2 |
Computer Software | 2 |
Readability | 2 |
Teaching Methods | 2 |
Art Education | 1 |
Case Studies | 1 |
Coding | 1 |
More ▼ |
Author
Jeevan Chapagain | 2 |
Priti Oli | 2 |
Rabin Banjade | 2 |
Vasile Rus | 2 |
Arun-Balajiee… | 1 |
Kayama, Mizue | 1 |
Maruyama, Ryoga | 1 |
Mohammad Hassany | 1 |
Nagai, Takashi | 1 |
Ogata, Shinpei | 1 |
Tachi, Nobuyuki | 1 |
More ▼ |
Publication Type
Speeches/Meeting Papers | 3 |
Reports - Research | 2 |
Reports - Evaluative | 1 |
Education Level
Elementary Education | 2 |
Junior High Schools | 2 |
Middle Schools | 2 |
Secondary Education | 2 |
Elementary Secondary Education | 1 |
Grade 4 | 1 |
Grade 5 | 1 |
Grade 7 | 1 |
Grade 8 | 1 |
Grade 9 | 1 |
High Schools | 1 |
More ▼ |
Audience
Location
Japan | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Flesch Reading Ease Formula | 1 |
What Works Clearinghouse Rating

Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages

Arun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Vasile Rus – Grantee Submission, 2024
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide…
Descriptors: Coding, Computer Science Education, Computational Linguistics, Artificial Intelligence
Maruyama, Ryoga; Ogata, Shinpei; Kayama, Mizue; Tachi, Nobuyuki; Nagai, Takashi; Taguchi, Naomi – International Association for Development of the Information Society, 2022
This study aims to explore an educational learning environment that supports students to learn conceptual modelling with the unified modelling language (UML). In this study, we call the describing models "UML programming." In this paper, we show an educational UML programming environment for science, technology, engineering, art, and…
Descriptors: Case Studies, Programming Languages, Learning Processes, Models