Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated…

Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…

In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…

Artificial Intelligence (AI) has rapidly emerged as a powerful tool with the potential to enhance learning environments. However, effective use of new technologies in education requires a good understanding of the technology and good design for its use. Generative AI such as ChatGPT requires particularly well-designed instructions due to its ease…

This study explores the use of machine learning and physiological signals to enhance learning performance based on students' personality traits. Traditional personality assessment methods often yield unreliable responses, prompting the need for a novel approach utilizing objective data collection through physiological signals. Participants from a…

Recent advancements in generative artificial intelligence (GenAI) have drawn significant attention from educators and researchers. However, its effects on learners' programming performance, self-efficacy and learning processes remain inconclusive, while the mechanisms underlying its efficiency-enhancing potential are underexplored. This study…

A posttest-only control group experimental design compared novice Arduino programmers who developed their own programs (self-programming group, n = 17) with novice Arduino programmers who used ChatGPT 3.5 to write their programs (ChatGPT-programming group, n = 16) on the dependent variables of programming scores, interest in Arduino programming,…

With the rapid development of artificial intelligence technology in the field of education, AI-Agents have shown tremendous potential in collaborative learning. However, traditional Computer-Supported Collaborative Learning (CSCL) methods still have limitations in addressing the unique demands of programming education. This study proposes an…

Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative programming learning is supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency of students' efficiency of programming learning and development of…

The programming curriculum is crucial in today's digital age, but a common issue is its focus on technical training while neglecting essential skills enhancement. Teachers lack structured and practical guidance on instructional methods, and the wide range of students' foundational abilities makes it difficult to cater to personalized learning…

Over the years, researchers have studied novice programming behaviors when doing assignments and projects to identify struggling students. Much of these efforts focused on using student programming and interaction features to predict student success at a course level. While these methods are effective at early detection of struggling students in…

Generative artificial intelligence (AI) tools like ChatGPT are becoming increasingly common in educational settings, especially in programming education. However, the impact of these tools on the learning process, student performance, and best practices for their integration remains underexplored. This study examines student experiences and…

Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming…

This research explored the creative thinking, learning achievement, and engagement of students when they integrated the application of the personal audio classifier (PAC) into the competition of a computational thinking (CT) board game (i.e., the experimental group), or did not integrate it into the competition but only collaborated with peers to…

This paper describes the first stages on the development of a design method of digital trainings using the collaborative authoring tool "ALO". Based on the theory of instrumental conflict (Marquet, 2005), this method highlights the necessity of the design digital trainings under the optimal harmonization for users/learners in didactic,…