voyeurism

Disciplinary Intuitions: welcoming college students to NTU's Undergraduate Research Experience on Campus (URECA) AY 2022 / 2023

further to my post two months’ ago, my team and i are really happy and privileged to welcome four college students (from a total of twenty-three applicants) to our projects offered under NTU’s Undergraduate Research Experience on Campus (URECA) programme for the Academic Year 2022 / 2023.

 

three of our students are from the School of Electrical and Electronic Engineering, and the fourth is pursuing a Double Major from the School of Physical and Mathematical Sciences and the School of Computer Science and Engineering.

 

the opportunity to participate in URECA is extended through invitation only to select second- and third-year undergraduates, who will subsequently be conferred as NTU President Research Scholars.

 

the projects we have offered are:

 

NIE22349 – Investigating local micro-climate and thermal comfort with open-source

 

NIE22355 – Designing a neural network-based approach for generating custom feedback based on stakeholder specifications for novice coders

 

NIE22356 – Designing a tool to analyse and score the readability of source code with a view towards encouraging programmers to improve the readability of their code

 

NIE22357 – Training Natural Language Processing (NLP) models to parse and analyse Python code in order to predict logic errors with the aim of improving the process of debugging code

i have been invited to share - in-person - at a symposium of the Learning Sciences at Seoul National University

this is a simple post, but a deeply meaningful one - the opportunity for which i thank God.

Seoul National University is hosting a symposium on the Learning Sciences on the 7th of July, and they have very graciously extended an invitation to share our team's recent work in-person.

the symposium has, as its theme, Future Directions of Learning Sciences: AI, Brain and Education.

sharing the day's events with me are Professor Carolyn Penstein Rose, who is currently the interim Director of the Language Technologies Institute at Carnegie Mellon, and Dr Minkang Kim from the Faculty of Arts and Social Sciences at Sydney.

:-)

Student-initiated inquiry of our local environments with Data Science and the Internet of Things

Following up from my post from two weeks' ago, projects from two pairs of my students were shared during the official launch of the [email protected] Blueprint.

Under the general theme of Student-initiated inquiry of our local environments with Data Science and the Internet of Things, students Terence and Tristan developed an integrated module that utilises Internet of Things to measure various environmental factors in the local microclimate in a fish pond, and also uses AI to count the number of fishes and correlate data with the environmental factors from instrument readings.

As a second example, students Minh Anh and Minh Tuan starting from 2021, assembled their own microclimate devices, EEG headsets and devised their own model to collect microclimate, biometric and EEG data using IoT. Machine Learning models are then used to find out different relationships between them. We describe this latter project as Learning at the intersection of AI, physiology, EEG, our environment and well-being (The Life2Well Project).

'Exploring the utility of deep learning in Geographic Information Systems (GIS) to infer socio-economic development' at RGS-IBG 2022

i am thrilled to share that a paper co-authored with a pair of my students has been accepted to be read at this year's annual conference of the Royal Geographical Society :-)

our paper is titled Exploring the utility of deep learning in Geographic Information Systems (GIS) to infer socio-economic development, and represents my team's first foray in the intersection of Data Science, AI and GIS. it is scheduled to be read on the 31st of August, in the session on Data and development.

this year's conference will take place as a hybrid event at Newcastle, from the 30th of August till the 2nd of September.

the abstract of our paper reads:

Substantive and reliable data on the economic well-being of a population is crucial to the effective formulation of research and policy. With this information, governments are able to understand and monitor the living standards of their people, evaluate the effectiveness of targeted policies, and make decisions about the allocation of scarce resources.

However, there currently exists a large gap in data available on these measures of economic development, particularly in developing countries, severely hampering future policy efforts.

Due to this, the regions of low socio-economic background are not addressed and the lives of those residing there are negatively impacted.

Hence, this project aims to explore the feasibility of applying remote sensing-based methods of poverty prediction in other countries to the Indonesian context. Specifically, this paper describes an independent research project conducted by a pair of Sixth Form students under the mentorship of a Research Scientist at the National Institute of Education in Singapore. In this project, we aimed to develop a Deep Learning model that is able to output relative wealth index classifications based on the Daytime Satellite imagery of a specific region.

The program utilized capable and established Artificial Intelligence (AI) such as Convolutional Neural Network (CNN). This project involved data preparation, training, fine-tuning our model, extracting our CNN features, and finally conducting ridge regression. We utilised proxy training tasks. With our results, we sought to demonstrate the applicability of this poverty prediction Deep Learning framework to a new geographical context.

The latter involved model training, fine-tuning the model, and finding appropriate datasets with which the model could be trained.

Using the results of this experiment, we hope that the necessary policies can be made to improve socio-economic backgrounds, the likes of which could not be done before.

Personal spaces and communal consequences: navigating geographical tensions with the Socially Responsible Behaviour through Embodied Thinking (SORBET) Project

i am very happy to share that a paper authored by myself and my friend Samuel Li has just been published in the journal Frontiers in Virtual Reality.

titled Personal spaces and communal consequences: navigating geographical tensions with the Socially Responsible Behaviour through Embodied Thinking (SORBET) Project, the paper is published in a section of the journal on Virtual Reality and Human Behaviour, in the article collection on Human Spatial Perception, Cognition, and Behaviour in Extended Reality. 

its abstract reads:

The coronavirus pandemic has impacted societies in different ways. This variation is inherently geographical, with patterns across space and time reflecting underlying societal inequalities. This paper describes a learning intervention designed to afford participants opportunities to experience the diffusion of a (virtual) virus within a community, albeit in a safe way. The intervention was enacted in March 2021 among nine undergraduate teachers-in-training at a teacher-education institute in Singapore. Participants were invited to reflect on the connections between their personal decision-making and its impact on the broader community, after having first undertaken a collaborative task in a virtual environment. The Socially Responsible Behaviour through Embodied Thinking (SORBET) Project draws on the learning sciences in terms of embodied cognition and projective identity, and in this paper, seeks to apply them to geographical understandings of diffusion. Responses from post-activity interviews are reported along three themes, as structured by Cultural-Historical Activity Theory. Through encouraging reflection on authentic experience, the authors hope to catalyse a more grounded appreciation of the need to practise positive social habits in the context of a pandemic and, by extension, contribute to the fight against the virus through the application of geographical thinking at scales local and global.

longitudinal effects in the effectiveness of educational virtual fieldtrips

Happy New Year :-)

last year i was privileged to be invited to be part of an authorial team comprising faculty from the Emerging Analytics Center at the University of Arkansas at Little Rock, the Center for Immersive Experiences at Penn State, the Department of Geosciences at Penn State, and the Laboratory of Geo-information Science and Remote Sensing at Wageningen University.

our paper on the Longitudinal effects in the effectiveness of educational virtual fieldtrips, helmed by Jiayan Zhao, has just been published in the Journal of Educational Computing Research.

its abstract reads:

Virtual and immersive virtual reality, VR and iVR, provide flexible and engaging learning opportunities, such as virtual field trips (VFTs). Despite its growing popularity for education, understanding how iVR compared to non-immersive media influences learning is still challenged by mixed empirical results and a lack of longitudinal research. This study addresses these issues through an experiment in which undergraduate geoscience students attended two temporally separated VFT sessions through desktop virtual reality (dVR) or iVR, with their learning experience and outcomes measured after each session. Our results show higher levels of enjoyment and satisfaction as well as a stronger sense of spatial presence in iVR students in both VFTs compared to dVR students, but no improvement in learning outcomes in iVR compared to dVR. More importantly, we found that there exists a critical interaction between VR condition and repeated participation in VFTs indicating that longitudinal exposure to VFTs improves knowledge performance more when learning in iVR than through dVR. These results suggest that repeated use of iVR may be beneficial in sustaining students’ emotional engagement and compensating the initial deficiency in their objective learning outcomes compared to other less immersive technologies.

my first sharing on our team's perspective on AI in education at ICER 2021

here's a recording of my very first sharing of our team's perspective on Artificial Intelligence, Data Science, Machine Learning and Computer Vision in education.

i was privileged to be invited to share during the 21st International Conference on Education Research which has been taking place over the past two days. ICER 2021 is hosted by Seoul National University, to whom i remain very grateful for the trust they extended to me.

sharing our work on physiological stress, local microclimate, wearables and Data Science at AAG 2022

my team and i are very excited to share that we have been afforded the opportunity to share our early and ongoing work at the intersection of physiological stress, local microclimate, wearables and Data Science, during the conference of the American Association of Geographers (AAG) which runs from the 25th of February till the 1st of March 2022.

our paper on Investigating the relationship between physiological stress and environmental factors through Data Science, the Internet of Things and DIY wearables has been invited to be part of a symposium on the 25th of February organised by Ast/P Guiming Zhang from the University of Denver and Ast/P Xi Gong from the University of New Mexico. My co-authors are Nguyen Duc Minh Anh and Nguyen Thien Minh Tuan.

the theme of the symposium is Human Dynamics Research: Mining Human Dynamics with Big Data, and it is sponsored by the Spatial Analysis and Modeling (SAM) specialty group, the Geographic Information Science and Systems specialty group, and the Cyberinfrastructure specialty group of the AAG, as part of the 8th Symposium on Human Dynamics Research.

Dr Gong is a faculty member of the Department of Geography & Environmental Studies, at the UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Dr Zhang is a faculty member of the Department of Geography and the Environment at his university. Together, they describe the symposium as follows:

With the advancement of information and communication technologies (ICT), location-aware technology, and mobile technology, data about human behaviors and interactions in physical, virtual, and network space has been generated at an unprecedented scale. The so-called big data bring in both opportunities and challenges for understanding, modeling, and predicting human dynamics. On one hand, the big data are collected from ubiquitous data sources (social media data, volunteered geographic information - VGI, sensor data, GPS tracks, transaction records, etc.); the data can cover aspects and scales of the human dynamics that are unseen from traditional data. On the other hand, revealing meaningful spatio-temporal patterns are challenging due to the high volume, velocity, and variety nature of the big data. Recent cutting-edge techniques such as data mining, machine learning, and artificial intelligence (AI) open up new opportunities for unveiling the hidden spatio-temporal and network patterns of human dynamics in the big data.

This session welcomes both methodological research on mining human dynamics with big data and human dynamics applications utilizing big data. Potential session topics include, but not limited to:
• Mining, detecting, or modeling human dynamics in physical, virtual or network space using big data (such as social media data, VGI, sensor data, GPS tracks, and transaction data).
• Algorithm design/optimization for mining human dynamics in big data.
• Spatio-temporal analytics of human dynamics using big data.
• Comparison of big data and traditional data in human dynamics research.
• Innovative data sources and data collection methods for human dynamics research.
• Data quality and privacy issues of big data in human dynamics research.
• Visualization of human dynamic patterns in big data.
• Predicting human dynamics based on historical pattern analysis on big data.

 

Data Science and AI, the SORBET Project and Yunnan Rangers shared during the opening keynote of ELSI 2021

my team and i are very happy and humbled that our various projects on Data Science and AI, the Socially Responsible Behaviour through Embodied Thinking (SORBET) Project, and the Yunnan Rangers hybrid on-site / off-site Augmented Reality board game has been chosen by the Director of the National Institute of Education, Singapore to be shared during her opening keynote delivered during this year's Educational Leadership Summer Institute (ELSI).

ELSI 2021 was hosted by the College of Education, De La Salle University, the Philippines, from the 30th of September till the 2nd of October. This year's theme was Educational Leadership in the New Normal: Navigating Technodependence.

we would like to thank our Director for her gracious encouragement.

:-)

Disciplinary Intuitions: welcoming college students to NTU's Undergraduate Research Experience on Campus (URECA) AY 2021 / 2022

further to my post two months’ ago, my team and i are really happy and privileged to welcome four college students (from a total of fifteen applicants) to our projects offered under NTU’s Undergraduate Research Experience on Campus (URECA) programme for the Academic Year 2021 / 2022.

one of our students is from the National Institute of Education, another is from the School of Computer Science and Engineering, another is from the School of Materials Science and Engineering, and another from the School of Physical and Mathematical Sciences.

the opportunity to participate in URECA is extended through invitation only to select second- and third-year undergraduates, who will subsequently be conferred as NTU President Research Scholars.

the projects we have offered are:

NIE21271 - Exploring Data Science through the visualisation of environmental data from the Internet of Things

NIE21276 - Game-Based Learning and the surfacing of leadership dispositions within a campus

NIE21287 - The design and development of an adaptive and assistive coding tool for students through the use of artificial intelligence and machine learning

NIE21303 - Safe management in endemic COVID through monitoring the capacity of defined spatial areas using machine learning