Lim, Kenneth Yang Teck

This paper describes an independent research project carried out by a pair of junior college students under the mentorship of a Research Scientist at the National Institute of Education. Our project aimed to design a working device to aid kindergarten children in learning about common household equipment such as tables, chairs, and so on. We hoped that this device would help to solve the problem of kindergarten children not being able to learn during the Covid-19 pandemic due to the closure of kindergartens. We trained an Object Detection model and ran it on a Raspberry Pi with screen, speakers and camera connected to it. We took photos of different household equipment and labelled them using software in order to train these images for our Object Detection model. We made use of the Google Open Images and COCO image datasets to sift out the related images of the objects we wanted to train in the model. Overall, the use of the software such as TensorFlow helped us with the training of the different types of objects for the model and the use of Text-to-Speech software allowed us to incorporate the use of sound to project the pronunciation of the objects. Eventually, there are many more aspects we could explore with our prototype. We could make it so that it can say out the object names in different languages to help teach Mother Tongue to kindergarten children.

This paper presents an initial analysis of data obtained from an implementation of citizenship education using the videogame Space Station Leonis in two Singapore classrooms. The paper highlights the importance of citizenship education today and describes how game environments can be used to foster identity construction as part of the broader agenda of citizenship education in Singapore. The data presented comprises a series of t-tests which were run between pre- and post-test questionnaires on attitudes related to civic consciousness and citizehsnhip administered to seventy-seven students who participated in the design intervention. Analysis of the results suggests that such participation can result in significant and positive changes in attitudes of relevance to various aspects of citizenship.

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.

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 paper describes a learning activity using Augmented Reality (AR) which seeks to take advantage of the potential for learning about history and culture through exploration. This is represented by a garden in a university campus that affords visitors a scaffolded experience comprising a game-driven narrative in which visitors to the garden may assume the roles of different protagonists. In addition, we also sought to design a paper- based board-game for visitors who are not yet able to visit the garden in person. Both aspects of the learning activity – namely, the game-driven narratives in the actual garden as well as in the board-game equivalent – were piloted in December 2020. The study suggests that a combination of Augmented Reality, storyline and role-play could increase the probability of encounters with spontaneous elements in learners’ local environments that encourage learning.

This paper describes an independent research study undertaken by a high school student under the mentorship of a Research Scientist at the National Institute of Education, Singapore. It explores how dialogic interactions on a given Mathematical topic, decimals, can be constrained in the remote learning platform Zoom. This research utilises Laurillard’s Conversational Framework for a small-scale intervention of two virtual learning sessions in Minecraft Education Edition, focusing on the decimal learning for primary school students. The study found that the overlapping of the immersive learning environment and remote learning platform engenders miscommunications, disorientation, and cognitive dissonance amongst both the teacher and the student, prolonging the discursive and adaptive phases in the dialogic interactions.

Dementia is set to become a major global health challenge. Studies show that there is a significant surge in cases of adults above 40 living with dementia. This alarming increase is due to various factors and early detection of dementia can allow for proper treatment to be administered. There exist various screening methods, but all come with shortcomings, particularly, subjectivity in hand-scored tests. Digitalisation of the tools mitigates this concern but can bring about newer concerns such as feasibility. The clock drawing test is a simple pen and paper test, to differentiate normal individuals from those with cognitive impairment, such as dementia. Our project aims to enhance the use of the clock drawing test, via digitalising it and equipping it with machine learning capabilities. We aim to offer an increased potential for early detection of cognitive impairment, to overall, improve the state of dementia detection already in practice.

This paper describes an independent research project undertaken by a pair of high school students in Singapore under the mentorship of a Research Scientist at the National Institute of Education. The importance of Artificial Intelligence (AI) and Machine Learning (ML) continues to increase every day in our constantly advancing society. This investigative study aims to uncover the practicality of both AI and ML by utilizing it in a real world context, more specifically, to monitor aquatic behaviour. The aim of this investigation focuses on how ML can be used alongside appropriate hardware to effectively monitor the behaviour of aquatic organisms in response to changes in environmental factors. This paper describes the design, construction, testing and design decisions behind the development of a module which allows us to use ML in tandem with IoT sensors to fulfill the above aim. This investigation ultimately concludes that we were able to successfully conceptualize and create two designs and accompanying prototypes where their strengths lie in the presence of an ecosystem in which sensor data measured can be easily compared to the number of fishes detected by our object detection model to draw relationships between aquatic behaviour and different environmental factors. However it is limited due to the main hindrance of the prototypes’ incapability of maintaining clear visibility of fish in murky waters with high turbidity.

Serious games have showcased tremendous potential in transforming the way we teach and learn. This paper explores the potential affordances of Augmented Reality (AR) game-based learning, specifically in the context of preserving school heritage. The AR game-based learning experience is proposed to increase students’ knowledge of their school’s heritage. By incorporating digital technology and story-telling, the game is also proposed to make the subject of school heritage more tangible for the enhancement of learning. The study involves 10 students playing an AR adventure role-playing game (RPG) which uses device location within the campus of Hwa Chong Institution, Singapore, to trigger in-game events. To assess the effectiveness of the AR game-based experience as a medium for learning, a general survey is used to collect feedback about the gameplay experience, while a Situational Interest survey collects data about participants’ situational interest, which emerges in response to the learning environment created, using the Situational Interest Scale (Chen et al., 1999). Results confirmed a positive correlation between players’ situational interest and absorption of information, shed light on the significance of game design elements in influencing the gameplay experience, and pointed to specific rooms for improvement for future AR game-based learning environments. It is hoped that this paper will contribute to an understanding of the wider effectiveness of game-based learning environments in educational contexts.

This paper describes an intervention piloted in secondary schools in Singapore in the second half of 2020, in response to the COVID-19 pandemic. The intervention aims to afford learners more authentic understandings of the need to invest effort and self-discipline in nurturing the new habit of practicing safe-distancing, beyond just doing so because of public exhortation. It seeks to achieve this objective through two complementary halves, the first being an activity within a virtual environment during which a (virtual) virus is diffusing, and the second being dialogue and discussion around students’ decision-making and behaviours, as informed from an analysis of data of interaction from the first half, via a web-based interface. In this way, the Socially Responsible Behaviour through Embodied Thinking (SORBET) Project represents not only an intervention designed to meet the challenges to learning imposed by COVID-19, but also one of the current few which attempt to do so by leveraging students’ evolving conceptions about the diffusion of a virus amongst a population. Understood thusly, the intervention has potential curricular applications in a number of disciplinary domains, such as in mathematics, geography, biology and citizenship education.