voyeurism

commemorating the twentieth anniversary of the Centre for Research in Pedagogy and Practice

i am very happy to share that a commemorative book celebrating the twentieth anniversary of the Centre for Research in Pedagogy and Practice has just been made available for download :-)

do feel free to browse Two decades and beyond: growing education research in Singapore :-)

The Life2Well Project: new student-initiated investigations under its aegis

since 2021, we have been sharing our work in neuroergonomics on Learning at the intersection of AI, physiology, EEG, our environment and well-being (The Life2Well Project). 

its mandate is deliberately framed broadly to encompass student-initiated inquiry in to their own health and well-being.

this year, we are excited and happy to share that students whom we mentor are currently working on the next suite of projects; these include:

- Integration of Deep Learning in digital Clock Drawing Tests onto consumer platforms for enhanced detection of mild cognitive impairment;
- Machine Learning implementation in robotics for emotional support companions;
- Investigating the relationship between microclimate factors and human exercise performance;
- Investigating the effect of microclimate on human's physiological health using Electrodermal activity; and
- Optimizing Learning Environments: A Microclimate Study of a School Campus in Singapore using the Integrated Environment Modeller Simulation Tool (IEMsim).

the Life2Well Project to be shared with visiting doctoral students from the National Tsing Hua University

my students and i are very happy to be presented with the opportunity to share our work in neuroergonomics on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) to a small team of visiting doctoral students in cognitive neuroscience from the Department of Education and Learning Technology, National Tsing Hua University, on the 16th of August.

we thank God for this opportunity. :-)

students' sharing to members of the Learning Sciences & Innovation group

today my students Jing Peng Lee and Bruce Yu were given the opportunity to share their work on their development of a maker-centric hardware prototype and deep learning model sleep analysis system to members of the Learning Sciences & Innovation group at the Centre for Research in Pedagogy and Practice, National Institute of Education, Singapore.

Bruce also took the opportunity to share the inspiring story behind his experiences in ESpero: founding a youth-led nonprofit in the 21st century.

Disciplinary Intuitions / Six Learnings / Maker Motes / AI & data science offered as part of NTU's Undergraduate Research Experience on Campus (URECA) programme 2023/24

are you an NTU undergraduate eligible for the Undergraduate Research Experience on Campus (URECA) programme 2023/24?

if so, please do consider applying for

NIE23075 Exploring the potential roles that game-based worlds and immersive environments play in learning

NIE23076 Exploring the development of maker dispositions in learning through open-source hardware and software

NIE23077 Exploring the role of coding, data science and machine learning in the Humanities classroom

NIE23078 Exploring the use of AI-augmented tools to provide feedback to novice coders :-)

it would be my privilege to work alongside you :-)

two papers to be read during the International Conference on Health and Social Care Information Systems and Technologies

i am thrilled to share that we have been given the opportunity to share our work through two papers to be read at this year's International Conference on Health and Social Care Information Systems and Technologies (HCist) :-)

this year's conference will take place as a hybrid event in Porto, from the 8th till the 10th of November.

An investigation on the effects of different types of odours on stress level of high school students when studying is co-authored with Wu Xinyue. its abstract reads:

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.

Designing and prototyping of AI-based real-time mobile detectors for calisthenic push-up exercise is co-authored with Zhang Xiyuan and Shawn Han. its abstract reads:

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.

two papers to be read during the annual conference of the Royal Geographical Society

i am thrilled to share that we have been given the opportunity to share our work through two papers to be read at this year's annual conference of the Royal Geographical Society :-)

this year's conference will take place as a hybrid event in London, from the 29th of August till the 1st of September.

Exploring Ornithological Identification with Deep Learning is co-authored with Edwin Ong and Shang Yu Liow. its abstract reads:

Only 4.5 percent of Singapore's land area is set aside as nature reserve. Despite this, Singapore is an important stopover point for more than forty species of migratory birds along both the Central Asian Flyway and the East Asian - Australasian Flyway. The birds are attracted to the wetlands and mudflats of the island-nation. This paper reports an independent research project conducted by a pair of high school students between April 2022 and March 2023, under the mentorship of a senior research scientist at the National Institute of Education in Singapore. It documents the identification and evaluation of Deep Learning Models in ornithological identification. Specifically, the capabilities of two neural network frameworks, VGG16 and InceptionV3, were identified due to their ability to build image classification models. The models were trained on publicly available datasets comprising more than 70000 images, representing more than 400 species. By the end of the project, the iteration of the model achieved a validation accuracy of 73.04% on forty locally sighted species. As an example of misidentification, an image of a juvenile Barred Eagle-Owl was classified as a White-headed Fish Eagle due to the similar white colouration. The bird prediction could be impacted by the lack of a variety of training data which represents the bird at all stages of its life, all while including images of both genders. A web-based service which linked to the external Singapore Birds Project was subsequently designed as a proof of concept for future scaling.

Designing an Autonomous Platform using Hobbyist Robotics Embedded Computing for Performing Geospatial 3D Mapping is co-authored with Christopher Cheng and Yi Xin Ong. its abstract reads:

Typical use cases of geospatial 3D mapping relate to outdoor environments, where virtual navigation platforms are generated via satellite and aerial imagery, and photogrammetry. Much remains to be discovered about mapping indoor environments, where data may not be readily accessible over long periods of time. This paper describes an independent research project conducted by a pair of high school students between April 2022 and March 2023, under the mentorship of a senior research scientist at the National Institute of Education in Singapore. The project investigated remote sensing methods of Light Detection and Ranging sensors (LiDAR), as well as computer vision functionalities of the NVIDIA Jetson Nano, which is a compact microcomputer ideal for machine learning applications. More specifically, through the utilisation of open-source hardware and software, the project aimed to design a mapping device by pairing autonomous robot navigation with map plotting and depth maps. The device organised and interpreted data sourced from Raspberry Pi cameras and a 360° LiDAR fitted onto a mobile platform. Depth perception models and mapping algorithms were explored as precedent for comparing their accuracy in data collection and efficacy in map generation. LiDAR systems - particularly those implemented for large-scale, outdoor scenarios - are costly to maintain. Although a LiDAR system with more available optical power has increased accuracy, it is also more expensive to operate. Nevertheless, autonomous platforms surpass manual labour in 3D mapping. By scheduling timely deployment of the mapping device, up-to-date information and visual representations of a mapped region could be more easily obtained.

invited to share our student project on biodiversity education during the 11th Student Scientific Conference, Konstantin Preslavsky University of Shumen

we are happy and excited to share our students' work on biodiversity education, as part of the 50th anniversary celebrations of the Department of Biology, Faculty of Natural Sciences, Konstantin Preslavsky University of Shumen, on the 22nd of April.

my students Edwin Ong and Shang Yu Liow will be sharing our paper Facilitating student inventiveness and inquiry: a case example from STEM, as part of the the 11th Student Scientific Conference, themed on ‘Ecology and the Environment’, in the section ‘Environmental education, Biodiversity, Cell biology, Technology’.

we are very grateful to our friends at the Biology Department for extending the invitation to us :-)

student projects on biodiversity education and sleep quality at the 13th annual symposium of the Mobiles for Education Alliance

today our team received news that we have been invited to share our students' work on biodiversity education and the investigation of sleep quality during the 13th annual symposium of the Mobiles for Education Alliance in the track on Climate Education. This track focuses on the intersection of STEM education and climate change to enhance students' understanding of climate science, sustainable practices, and the role of technology in addressing environmental challenges.

we thank God for His continued grace and provision.

this year, the symposium is being conducted as an in-person event between the 11th to the 14th of September, with the overarching theme of Inspiring the next generation of STEM leaders - everywhere! 

other teams having representations are from UNESCO, the World Bank, the UNHCR, among others.

The mEducation Alliance was formed by USAID in 2010. It is an international collaborative effort between bilateral and multilateral donors, NGOs, foundations, private sector partners, and academic researchers. The collective agenda of the alliance is to: highlight and catalyze promising information and communications technology for education (ICT4E) practices and initiatives, particularly in low-resource and developing country context; to reduce duplicative efforts; to promote collective knowledge-sharing, and to identify and support efforts to evaluate and scale promising ICT4E interventions.

:-)

the Life2Well Project to be shared during the 10th International Conference on Human Interaction & Emerging Technologies

we are very happy to share that we have been given the opportunity to share our work on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) during the 10th International Conference on Human Interaction & Emerging Technologies in the track on Human-computer interaction. IHIET 2023 will be held in Nice from the 22nd till the 24th of August; our paper is scheduled for the 23rd.

Designing for the investigation of microclimate stressors and physiological and neurological responses from the perspective of maker culture is co-authored with Nguyen Duc Minh Anh and Nguyen Thien Minh Tuan. its abstract reads:

The 2021 United Nations Climate Change Conference (COP26) resulted in the Glasgow Climate Pact. Initial work in the study reported in this paper investigated relationships between environment and physiological measurements using smartwatches, and self-designed bespoke environmental modules which are wearable around the waist. Data from this initial phase was analysed with a Random Forest regression model. The next phase of this project involves neurophysiological measurement, specifically electroencephalography (EEG). EEG was introduced to the model to explore how the changes in environmental or biometric measurements correlate with changes in neurophysiological measurements. In this latter phase, EEG data is viewed as an independent data type that is distinct from environmental and other physiological data. The headset model used to record EEG data is again a bespoke hand-made design, comprising a combination of biosensing board and electrodes from OpenBCI and widely available items like adhesive tapes and staples. A subsequent step involved validation of this DIY EEG headset data against research grade equipment, of which the analysis of different features of EEG data have shown to be of statistically comparable trends. For data collection, all data recorded is stored in Google Drive; Python is used to synchronize, pre-process data and train regression models. The first headset prototype was assembled in mid-October 2021, and was tested and developed in early November. From mid-November to late January 2022, the authors wore the devices for one to two hours per day to collect data. For EEG data, eight channels were recorded, basic filters (bandpass and notch) and REST re-referencing are applied. In this project, EEG time-series are used as input in regression models with other data types as output. Two regression models were trained then compared, the first being convolutional neural network with pre-built architecture and the other being a Random Forest model with features extracted from EEG time-series. Inferences are made from the models using open-source interpreters, with an eventual aim to infer how one’s local environment might impact one’s emotions and health. The results suggest that sound level, carbon dioxide concentration, and dust concentration feature more importantly in the regression models trained on collected datasets. These factors were continually associated with high feature importance scores in the EEG data signal and in both the objective scores recorded from the electronic instruments and the more subjective self-report forms. Furthermore, it was found that visual stimulus and problem processing, in terms of information, touch, and spatial relationships, are the most influential factors affecting the participants’ physiological well-being in this research. Most recently, one aspect that is currently being investigated is electrodermal activity (EDA). EDA is marker of sympathetic network activity (Zangróniz et al., 2017). As such, it is an indication of human stress and emotion arousal, (Rahma et al., 2022). It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. Early validation experiments comparing DIY EDA devices against research-grade Empactica E4 sets have shown promising results.