Contribution: This article proposes the systematic integration of embedded systems into training hardware to bridge the gap in structured troubleshooting education. Traditional methods often rely on manual explanations, virtual simulations, or on-the-job training, which lack structured learning experiences. The proof-of-work module, developed…

Graded Troubleshooting (GTS) is a powerful routine that teachers can use easily to engender students' metacognitive thinking and boost their understanding of mathematics concepts and procedures. This article describes a new GTS activity designed to prompt students to efficiently exploit worked examples when asked to diagnose erroneous examples…

Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students' behavior patterns in programming among beginners and non-beginners to identify solver types,…

Debugging is essential for identifying and rectifying errors in programming, yet time constraints and students' trivialization of errors often hinder progress. This study examines differences in debugging challenges and strategies among students with varying computational thinking (CT) competencies using weekly coding journals from an online…

Recent years have witnessed a growing application of generative artificial intelligence (GenAI) technology in writing instruction. Students should mobilise their metacognitive strategies during this endeavour to maximise the benefits of GenAI while avoiding the potential negative impacts. Within the context of tertiary education in Hong Kong, this…

SQL compiler error messages are the primary way users receive feedback when they encounter syntax errors or other issues in their SQL queries. Effective error messages can enhance the user experience by providing clear, informative, and actionable feedback. Despite the age of SQL compilers, it still remains largely unclear what contributes to an…

This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…

In the digital age, computational thinking (CT)-based problem-solving skills have emerged as essential competencies. Particularly, students with intellectual disabilities need equal educational opportunities and high-quality informatics education to cultivate CT-based problem-solving skills. However, research on the enhancement of CT-based…

Background: Moments of failure during learning present a wide range of opportunities for growth. However, experimental research and meta reviews focused on failure and learning tend to target singular valued learning processes, such as efficient fixes or transfer of conceptual understanding. These analytical decisions conflict with research…

Large Language Models (LLMs), such as ChatGPT, have become widely popular for various software engineering tasks, including programming, testing, code review, and program comprehension. However, their impact on improving software quality in educational settings remains uncertain. This article explores our experience teaching the use of Programming…

Background: Students experience higher-order thinking skills by finding ways to solve the problem, debugging errors while applying the solution, and testing the solution in programming. However, the inability to create schemas that will characterise programming structures is one of the difficulties during this process. Objectives: This study aimed…