This page has only limited features, please log in for full access.

Dr. Meng-Leong How
Nanyang Technological University

Basic Info


Research Keywords & Expertise

0 Artificial Intelligence
0 Educational Research
0 Artificial Intelligent Systems/artificial Neural Network
0 artificial intelligence and machine learning
0 AI-Thinking

Fingerprints

Artificial Intelligence
artificial intelligence and machine learning
AI-Thinking
AI for Good

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 04 August 2020 in Sustainability
Reads 0
Downloads 0

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, food availability, food quality and safety, and the resilience of natural resources. Exemplars are provided in this paper to illustrate how computational simulations can be used to produce forecasts of good and bad conditions in food security using multi-variant optimizations. The forecast of these future scenarios is useful for informing policy makers and stakeholders across domain verticals, so they can make decisions that are favorable to global food security.

ACS Style

Meng-Leong How; Yong Chan; Sin-Mei Cheah. Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence. Sustainability 2020, 12, 6272 .

AMA Style

Meng-Leong How, Yong Chan, Sin-Mei Cheah. Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence. Sustainability. 2020; 12 (15):6272.

Chicago/Turabian Style

Meng-Leong How; Yong Chan; Sin-Mei Cheah. 2020. "Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence." Sustainability 12, no. 15: 6272.

Journal article
Published: 27 April 2020 in Big Data and Cognitive Computing
Reads 0
Downloads 0

According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfully. The application of artificial intelligence (AI) on legacy data can help FSPs to anticipate how prospective customers may respond when they are approached. However, it remains challenging for FSPs who are not well-versed in computer programming to implement AI projects. This paper proffers a no-coding human-centric AI-based approach to simulate the possible dynamics between the financial profiles of prospective customers collected from 45,211 contact encounters and predict their intentions toward the financial products being offered. This approach contributes to the literature by illustrating how AI for social good can also be accessible for people who are not well-versed in computer science. A rudimentary AI-based predictive modeling approach that does not require programming skills will be illustrated in this paper. In these AI-generated multi-criteria optimizations, analysts in FSPs can simulate scenarios to better understand their prospective customers. In conjunction with the usage of AI, this paper also suggests how AI-Thinking could be utilized as a cognitive scaffold for educing (drawing out) actionable insights to advance financial inclusion.

ACS Style

Meng-Leong How; Sin-Mei Cheah; Aik Cheow Khor; Yong Jiet Chan. Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach. Big Data and Cognitive Computing 2020, 4, 8 .

AMA Style

Meng-Leong How, Sin-Mei Cheah, Aik Cheow Khor, Yong Jiet Chan. Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach. Big Data and Cognitive Computing. 2020; 4 (2):8.

Chicago/Turabian Style

Meng-Leong How; Sin-Mei Cheah; Aik Cheow Khor; Yong Jiet Chan. 2020. "Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach." Big Data and Cognitive Computing 4, no. 2: 8.

Journal article
Published: 03 February 2020 in AI
Reads 0
Downloads 0

According to the World Health Organization (WHO) and the World Bank, malnutrition is one of the most serious but least-addressed development challenges in the world. Malnutrition refers to the malfunction or imbalance of nutrition, which could be influenced not only by under-nourishment, but also by over-nourishment. The significance of this paper is that it shows how artificial intelligence (AI) can be democratized to enable analysts who are not trained in computer science to also use human-centric explainable-AI to simulate the possible dynamics between malnutrition, health and population indicators in a dataset collected from 180 countries by the World Bank. This AI-based human-centric probabilistic reasoning approach can also be used as a cognitive scaffold to educe (draw out) AI-Thinking in analysts to ask further questions and gain deeper insights. In this study, a rudimentary beginner-friendly AI-based Bayesian predictive modeling approach was used to demonstrate how human-centric probabilistic reasoning could be utilized to analyze the dynamics of global malnutrition and optimize conditions for achieving the best-case scenario. Conditions of the worst-case “Black Swan” scenario were also simulated, and they could be used to inform stakeholders to prevent them from happening. Thus, the nutritional and health status of vulnerable populations could be ameliorated.

ACS Style

Meng-Leong How; Yong Jiet Chan. Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach. AI 2020, 1, 68 -91.

AMA Style

Meng-Leong How, Yong Jiet Chan. Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach. AI. 2020; 1 (1):68-91.

Chicago/Turabian Style

Meng-Leong How; Yong Jiet Chan. 2020. "Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach." AI 1, no. 1: 68-91.

Journal article
Published: 11 January 2020 in Information
Reads 0
Downloads 0

Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic reasoning approach can also be used as cognitive scaffolding to educe AI-Thinking in the analysts to ask more questions and provide decision making support to inform policy making in sustainable development. This paper uses the 2018 Environmental Performance Index (EPI) data from 180 countries which includes performance indicators covering environmental health and ecosystem vitality. AI-based predictive modeling techniques are applied on 2018 EPI data to reveal the hidden tensions between the two fundamental dimensions of sustainable development: (1) environmental health; which improves with economic growth and increasing affluence; and (2) ecosystem vitality, which worsens due to industrialization and urbanization.

ACS Style

Meng-Leong How; Sin-Mei Cheah; Yong-Jiet Chan; Aik Cheow Khor; Eunice Mei Ping Say. Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach. Information 2020, 11, 39 .

AMA Style

Meng-Leong How, Sin-Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor, Eunice Mei Ping Say. Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach. Information. 2020; 11 (1):39.

Chicago/Turabian Style

Meng-Leong How; Sin-Mei Cheah; Yong-Jiet Chan; Aik Cheow Khor; Eunice Mei Ping Say. 2020. "Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach." Information 11, no. 1: 39.

Journal article
Published: 31 July 2019 in Big Data and Cognitive Computing
Reads 0
Downloads 0

Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.

ACS Style

Meng-Leong How; How. Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes. Big Data and Cognitive Computing 2019, 3, 46 .

AMA Style

Meng-Leong How, How. Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes. Big Data and Cognitive Computing. 2019; 3 (3):46.

Chicago/Turabian Style

Meng-Leong How; How. 2019. "Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes." Big Data and Cognitive Computing 3, no. 3: 46.

Journal article
Published: 15 July 2019 in Education Sciences
Reads 0
Downloads 0

In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.

ACS Style

Meng-Leong How; Wei Loong David Hung. Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education. Education Sciences 2019, 9, 184 .

AMA Style

Meng-Leong How, Wei Loong David Hung. Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education. Education Sciences. 2019; 9 (3):184.

Chicago/Turabian Style

Meng-Leong How; Wei Loong David Hung. 2019. "Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education." Education Sciences 9, no. 3: 184.

Tutorial
Published: 25 June 2019 in Education Sciences
Reads 0
Downloads 0

Educational stakeholders would be better informed if they could use their students’ formative assessments results and personal background attributes to predict the conditions for achieving favorable learning outcomes, and conversely, to gain awareness of the “at-risk” signals to prevent unfavorable or worst-case scenarios from happening. It remains, however, quite challenging to simulate predictive counterfactual scenarios and their outcomes, especially if the sample size is small, or if a baseline control group is unavailable. To overcome these constraints, the current paper proffers a Bayesian Networks approach to visualize the dynamics of the spread of “energy” within a pedagogical system, so that educational stakeholders, rather than computer scientists, can also harness entropy to work for them. The paper uses descriptive analytics to investigate “what has already happened?” in the collected data, followed by predictive analytics with controllable parameters to simulate outcomes of “what-if?” scenarios in the experimental Bayesian Network computational model to visualize how effects spread when interventions are applied. The conceptual framework and analytical procedures in this paper could be implemented using Bayesian Networks software, so that educational researchers and stakeholders would be able to use their own schools’ data and produce findings to inform and advance their practice.

ACS Style

Meng-Leong How; Wei Loong David Hung. Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach. Education Sciences 2019, 9, 158 .

AMA Style

Meng-Leong How, Wei Loong David Hung. Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach. Education Sciences. 2019; 9 (2):158.

Chicago/Turabian Style

Meng-Leong How; Wei Loong David Hung. 2019. "Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach." Education Sciences 9, no. 2: 158.

Tutorial
Published: 20 May 2019 in Education Sciences
Reads 0
Downloads 0

Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.

ACS Style

Meng-Leong How; Wei Loong David Hung. Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations. Education Sciences 2019, 9, 110 .

AMA Style

Meng-Leong How, Wei Loong David Hung. Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations. Education Sciences. 2019; 9 (2):110.

Chicago/Turabian Style

Meng-Leong How; Wei Loong David Hung. 2019. "Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations." Education Sciences 9, no. 2: 110.

Chapter
Published: 03 May 2019 in Computational Thinking Education
Reads 0
Downloads 0

Many countries that recognise the importance of Computational Thinking (CT) skills are implementing curriculum changes to integrate the development of these skills and to introduce programming into formal school education. In countries such as the United Kingdom, Lithuania, Finland, Korea and Japan, initiatives and policies are made to introduce the development of CT skills and programming in the schools. This chapter provides an in-depth analysis of policies of CT in the education of one particular country, namely Singapore. We review Singapore’s approach to its implementation of CT education by first describing various initiatives in Singapore for Preschool, Primary and Secondary schools. Unlike several countries that have decided to implement computing as compulsory education, Singapore has taken a route of creating interest amongst children in computing in age-appropriate ways. Singapore’s pragmatic approach of relying on an ecosystem is characterised by allowing schools the choice to opt-in, nurturing students’ interest in computing, upskilling teachers in computing and a multi-agency approach.

ACS Style

Peter Seow; Chee-Kit Looi; Meng-Leong How; Bimlesh Wadhwa; Long-Kai Wu. Educational Policy and Implementation of Computational Thinking and Programming: Case Study of Singapore. Computational Thinking Education 2019, 345 -361.

AMA Style

Peter Seow, Chee-Kit Looi, Meng-Leong How, Bimlesh Wadhwa, Long-Kai Wu. Educational Policy and Implementation of Computational Thinking and Programming: Case Study of Singapore. Computational Thinking Education. 2019; ():345-361.

Chicago/Turabian Style

Peter Seow; Chee-Kit Looi; Meng-Leong How; Bimlesh Wadhwa; Long-Kai Wu. 2019. "Educational Policy and Implementation of Computational Thinking and Programming: Case Study of Singapore." Computational Thinking Education , no. : 345-361.

Articles
Published: 03 July 2018 in Computer Science Education
Reads 0
Downloads 0

Unplugged activities have been one approach to introduce computational thinking (CT) to students before any form of coding is involved. This paper reports on a study that examines the evaluation of the types of CT skills inculcated through an unplugged activity. Students in a grade 9 class were engaged in an unplugged activity on sorting before being asked to represent their understanding in the form of pseudo-English, flowchart or Python code. The assessment of CT skills comprises the aspects of decomposition, algorithmic design, generalization, abstraction and evaluation. Qualitative Comparative Analysis (QCA) was used to take a closer look at the unplugged CT activity and the subsequent artifact production. Such a QCA analysis can be used to inform a framework for designing instruction and tasks to target and teach certain types of CT knowledge in novice programmers, as well as for assessing an instruction package as to what CT knowledge is being covered.

ACS Style

Chee-Kit Looi; Meng-Leong How; Wu Longkai; Peter Seow; Liu Liu. Analysis of linkages between an unplugged activity and the development of computational thinking. Computer Science Education 2018, 28, 255 -279.

AMA Style

Chee-Kit Looi, Meng-Leong How, Wu Longkai, Peter Seow, Liu Liu. Analysis of linkages between an unplugged activity and the development of computational thinking. Computer Science Education. 2018; 28 (3):255-279.

Chicago/Turabian Style

Chee-Kit Looi; Meng-Leong How; Wu Longkai; Peter Seow; Liu Liu. 2018. "Analysis of linkages between an unplugged activity and the development of computational thinking." Computer Science Education 28, no. 3: 255-279.

Journal article
Published: 01 May 2018 in International Journal of Computer Science Education in Schools
Reads 0
Downloads 0

Computational Thinking (CT) is pervasive in our daily lives and is useful for problem-solving. Decision-making is a crucial part of problem-solving. In the extant literature, problem-solving strategies in educational settings are often conveniently attributed to intuition; however, it is well documented that computer programmers might even have difficulty describing about their intuitive insights during problem-solving using natural language (such as English), and subsequently convert what has been described using words into software code. Hence, a more analytical approach using mathematical equations and descriptions of CT is offered in this paper as a potential form of rudimentary scaffolding, which might be useful to facilitators and learners of CT-related activities. In the present paper, the decision-making processes during an unplugged CT activity are delineated via Grey-based mathematical equations, which is useful for informing educators who may wish to explain to their learners about the various aspects of CT which are involved in the unplugged activity and simultaneously use these mathematical equations as scaffolds between the unplugged activity and computer code programming. This theoretical manuscript may serve as a base for learners, should the facilitator ask them to embark on a software programming activity that is closely associated to the unplugged CT activity.

ACS Style

Meng-Leong How; Chee-Kit Looi. Using Grey-based Mathematical Equations of Decision-making as Teaching Scaffolds: from an Unplugged Computational Thinking Activity to Computer Programming. International Journal of Computer Science Education in Schools 2018, 2, 29 -46.

AMA Style

Meng-Leong How, Chee-Kit Looi. Using Grey-based Mathematical Equations of Decision-making as Teaching Scaffolds: from an Unplugged Computational Thinking Activity to Computer Programming. International Journal of Computer Science Education in Schools. 2018; 2 (2):29-46.

Chicago/Turabian Style

Meng-Leong How; Chee-Kit Looi. 2018. "Using Grey-based Mathematical Equations of Decision-making as Teaching Scaffolds: from an Unplugged Computational Thinking Activity to Computer Programming." International Journal of Computer Science Education in Schools 2, no. 2: 29-46.