This research, conducted in an upper post-primary Computer Science classroom, explores how students learning Python engaged with ChatGPT. A key output of the study was the co-creation, with students, of heuristics (guiding principles) to support the ethical and effective use of ChatGPT while learning to program. These include: (i) a general set to support learning in contexts where generative AI is available, and (ii) the SAV framework (Specify the problem, Ask again, Verify the output), a specific set for engaging with ChatGPT. These heuristics are presented as working prototypes.
This poster captures our experiences of incorporating video components into assessments on Software Engineering programmes. We highlight their dual role as a tool for developing soft skills and a mechanism for verifying the attainment of learning outcomes. Our approach focuses on various types of video production tasks in varied contexts, contributing to ongoing efforts to promote soft skills development in software engineering education [1, 2].
Introductory programming courses (CS1) are widely recognized as challenging for many students. Understanding the factors that contribute to student performance in CS1 has been a long-standing focus of research in computing education. One factor often associated with course performance is motivation. However, since motivation is multifaceted, it is essential to determine which specific facets of motivation contribute most significantly to success. In this study, we validate a recently proposed instrument designed to measure social aspect, meaning, independence, and self-efficacy, within the context of a CS1 course. We compare this instrument to an established intrinsic motivation scale and students’ self-reported time use. Furthermore, we examine how these instruments and self-reported time use are related to persistence and performance in CS1. Our findings reveal that among motivational factors, perceived interest and enjoyment stand out as the most significant predictors of course performance. Additionally, perceived meaning of studies plays a noteworthy role in improving outcomes, particularly over self-efficacy. These observations underscore the value of intrinsic motivation in CS1 settings.
Code comprehension refers to the ability to read, understand, and explain code effectively. While students may successfully complete introductory programming assignments, they often do so without fully grasping the underlying code, which can lead to difficulties in more advanced classes. This work presents our approach to addressing this challenge through targeted interventions that assess students’ comprehension using personalised, AI-generated questions. We share insights from a pilot study conducted to gather initial student feedback on the effectiveness of this method.
This study presents expert feedback on an immersive virtual reality environment designed to teach 3D transformations and improve spatial visualization skills in computer graphics. Traditional 2D methods can limit spatial understanding, while the VR tool offers interactive and real-time manipulation of 3D objects. The evaluation of teaching staff and domain experts highlights the educational value, usability, and potential of this approach to enhance spatial learning in computing education.
