Conference Papers

We propose the design of an observatory dedicated to the tactical analysis of handball, involving the use of dynamic graphs to represent and study patterns of play. This innovative methodology was used to capture the complex interactions between players, their movements on the court and passing sequences. By using techniques for extracting frequent subsequences from a dynamic graph constructed from a gnomonic transformation (azimuthal map projection that transforms circles into straight lines) of the court, it was possible to identify the most recurrent and therefore significant patterns of play. The extracted sequences were valued by various measures of interest: emergence rate and expected goal. These measures of interest introduced a hierarchy between the sequences, enabling them to be navigated. Within the observatory, the tactics discovered were illustrated by an intuitive graphical representation, giving coaches and analysts a better understanding of the trends, strengths and weaknesses of the tactics employed.

Mathematics has always been a difficult subject for many students. Consequently, many students fear the subject and refuse to engage in this subject. There are many educational theories which attempt to address students’ motivational and cognitive issues in learning mathematics. In this lecture, I attempt to propose an alternative approach of teaching mathematics through the use of comics based on my experience of infusing comics into the teaching of mathematics for the low progress learners. I further present a framework of infusing comics for mathematics instructions, and take reference from not only educational theories but also from the perspective of communication theory. In designing lessons using comics, I make reference to how the various elements of the communication model are taken into consideration in the design process. I illustrate the application of this framework with the use of exemplars, making reference from both primary and secondary levels. With the advent of Generative Artificial Intelligence, what appears to be a rather expensive process of developing comics package could potentially become relatively inexpensive, so that educators and designers could invest more time in conceptualizing the content of the comics package. Thus, the theoretical framework for the design process deserves greater attention to researchers and educators.

In view of the mental health issues among adolescents in Singapore, aromatherapy is proposed to mitigate their stress level when studying. An experiment was conducted both in the classroom and home setting, to test the effectiveness of the give odours, lavender, rosemary, ylang-ylang, lemon and bergamot on reducing stress level, using the no odour scenario as control. Objective data (SpO2, heart rate and stress score) is collected using the HUAWEI Band 6 smartwatch. Subjective data was self-reported by the participants through Google forms, rating their emotional health on a 10-point scale and elaborating in prose. It was found that the anti-anxiety effects of the stimulants (lemon, bergamot, rosemary) were much larger than that of the sedatives (lavender, ylang-ylang). In particular, lemon showed the best objective anxiolytic effect, while bergamot was the best in terms of self-perceived effect. Rosemary relieved stress through raising productivity, but some effects of overworking were observed. On the other hand, ylang-ylang showed inconsistent effects, while lavender was not suitable to relieve stress when studying.

This research investigates distinct human-generative AI collaboration types and students’ interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-solving tasks and how these factors relate to students’ sense of agency and perceived collaborative problem solving. By analyzing the surveys and reflections of 79 undergraduate students, we identified three human-generative AI collaboration types: even contribution, human leads, and AI leads. Notably, our study shows that 77.21% of students perceived they led or had even contributed to collaborative problem-solving when collaborating with ChatGPT. On the other hand, 15.19% of the human participants indicated that the collaborations were led by ChatGPT, indicating a potential tendency for students to rely on ChatGPT. Furthermore, 67.09% of students perceived their interaction experiences with ChatGPT to be positive or mixed. We also found a positive correlation between positive interaction experience and a sense of positive agency. The results of this study contribute to our understanding of the collaboration between students and generative AI and highlight the need to study further why some students let ChatGPT lead collaborative problem-solving and how to enhance their interaction experience through curriculum and technology design.

Fitness exercises, including push-ups, are very beneficial to personal health. Many Artificial Intelligence (AI)-based fitness trainers are developed based on human pose estimation models or assisted by Internet of Things (IoT) devices. However, many of them require access to a graphing processing unit (GPU) for model training or IoT sensors to deploy, less accessible for individuals. In our work, we designed and prototyped real-time mobile push-up detectors using three distinctive approaches: (1) Push-up pose classification, (2) Angle-heuristic estimation and (3) Optical flow detection. We trained our deep-learning model with over 2000 images to achieve a high accuracy for real time deployment. Models are tested on our video dataset applied data augmentation techniques to simulate real-world environmental conditions to evaluate model performance based on accuracy metrics (precision, recall, F1 score) and processing frame rate (FPS). From the results, we concluded that the angle-heuristic estimation method has the best overall performance and we analysed the reasons for the relatively poorer performance of the push-up pose classification and optical flow detection methods. All methods developed are capable of working on mobile devices without the need of GPU or IoT sensors.