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…

This paper undertakes a thorough evaluation of ChatGPT's code generation capabilities, contrasting them with those of human programmers from both educational and software engineering standpoints. The emphasis is placed on elucidating its importance in these intertwined domains. To facilitate a robust analysis, we curated a novel dataset comprising…

Motivation: Students learning to program often reach states where they are stuck and can make no forward progress--but this may be outside the classroom where no instructor is available to help. In this situation, an automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what…

Novice programmers often struggle on assignments, and timely help, such as a hint on what to do next, can help students continue to progress and learn, rather than giving up. However, in large programming classrooms, it is hard for instructors to provide such real-time support for every student. Researchers have, therefore, put tremendous effort…

In this study, an interactive programming learning environment was built with two types of error prompt functions: 1) the key prompt and 2) step-by-step prompt. A quasi-experimental study was conducted for five weeks, in which 75 sixth grade students from disadvantaged learning environments in Taipei, Taiwan, were divided into three groups: 1) the…

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…

The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in…

Purpose: This study aims to examine if a pedagogical agent's expressed anger, when framed as a feedback cue, can enhance mental effort and learning performance in a multimedia learning environment than expressed happiness. Design/methodology/approach: A between-subjects experiment was conducted in which learners engaged with a multimedia learning…

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…