Ean Teng Khor | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Ean Teng Khor
National Institute of Education, Nanyang Technological University, Singapore
Emmanuel Omeje
Affiliation National Institute of Education, Nanyang Technological University
Country Singapore
Scopus 54393424800
Documents 30
Citations 176
h-index 6
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0001-6817-9332
Google Scholar Ra1HNaIAAAAJ

Ean Teng Khor is an education researcher, lecturer, and academic leader whose work integrates artificial intelligence, learning analytics, educational data mining, and technology-enhanced learning. Based at the National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore, she has contributed to the advancement of AI-enabled educational innovation through research, teaching, academic leadership, and externally funded projects. Her research portfolio demonstrates sustained engagement with personalised learning systems, educational predictive analytics, generative artificial intelligence, conversational agents, and human–AI collaboration for learning.[1]

Abstract

This article presents an academic profile of Ean Teng Khor and evaluates her scholarly contributions in the fields of Artificial Intelligence in Education, Learning Analytics, Educational Data Mining, and Technology-Enhanced Learning. Through interdisciplinary research, externally funded projects, academic leadership, and international engagement, she has contributed to the development of evidence-based educational innovations designed to support personalised learning, educator development, and digital transformation across educational sectors. Her work demonstrates a sustained focus on integrating advanced computational methods with learning sciences to improve educational outcomes and learner experiences.[1]

Keywords

Artificial Intelligence in Education; Learning Analytics; Educational Data Mining; Generative AI; Conversational AI; Personalised Learning; Machine Learning; Educational Technology; Adaptive Learning; Human–AI Collaboration.

Introduction

The increasing integration of artificial intelligence into educational environments has created opportunities for innovative approaches to teaching, learning, assessment, and learner support. Within this evolving landscape, Ean Teng Khor has established a research profile focused on leveraging data-driven methods and intelligent technologies to support educational improvement. Her academic work spans generative AI, learning analytics, educational data mining, predictive modelling, adaptive learning systems, and technology-enhanced pedagogy.[1]

Her contributions have been developed through collaborations with educators, policymakers, researchers, and institutions across multiple educational sectors. This combination of technical expertise and educational insight has enabled the translation of research findings into practical educational applications that address contemporary challenges in teaching and learning.[1]

Research Profile

Ean Teng Khor earned a Bachelor of Information Technology (Honours) in 2005 and a Master of Science in Information Technology in 2007 from Multimedia University, followed by a Doctor of Philosophy from Universiti Sains Malaysia in 2015. Her academic appointments include positions at Wawasan Open University, East Asia Institute of Management, Nanyang Technological University, and the National Institute of Education.[1]

Her research expertise encompasses AI-enabled learning design, conversational AI, educational predictive analytics, personalised learning systems, machine learning applications in education, digital learning ecosystems, and data literacy development. She currently serves as Founding Leader of the Learning Analytics Special Interest Group at NIE and participates in multiple editorial, reviewing, and conference leadership roles within the international learning sciences community.[1]

Research Contributions

A defining characteristic of Ean Teng Khor’s research is the application of artificial intelligence and analytics to support personalised and adaptive learning. As Principal Investigator and Co-Principal Investigator, she has led and contributed to multiple competitive research projects funded by organizations including Singapore’s Ministry of Education, SkillsFuture Singapore, and AI Singapore.[1]

  • Development of AI-mediated learning systems using retrieval-augmented generation and intelligent interventions.
  • Research on teacher data literacy and evidence-informed educational decision-making.
  • Learning analytics frameworks for workplace and lifelong learning environments.
  • Adaptive AI-supported language learning and multilingual tutoring technologies.
  • Educational predictive modelling for identifying and supporting at-risk learners.

Beyond funded research, she has contributed to academic community development through editorial board memberships, peer review activities, keynote presentations, conference leadership, educator professional development initiatives, and academic assessment roles across international institutions and scholarly organizations.[1]

Publications

Ean Teng Khor’s publication record includes journal articles and conference papers addressing learning analytics, educational technology adoption, microlearning, predictive modelling, and personalised learning. Several publications have appeared in internationally recognized journals and have contributed to scholarly discussions surrounding data-informed educational practice and AI-supported learning environments.[2][3][4][5]

Research Impact

According to the Scopus author profile, Ean Teng Khor has accumulated 176 citations across indexed publications with an h-index of 6. These metrics reflect measurable scholarly engagement within fields related to educational technology, learning analytics, and artificial intelligence in education.[1]

Her impact extends beyond publication metrics through successful supervision of student researchers, leadership of collaborative research initiatives, development of AI-enabled educational tools, and professional engagement with educators and policymakers. The practical orientation of her research has supported knowledge transfer between academic scholarship and educational practice.[1]

Award Suitability

Ean Teng Khor’s academic profile demonstrates several characteristics commonly associated with recognition through research and innovation awards. These include sustained scholarly productivity, leadership of externally funded projects, contributions to educational innovation, international academic service, successful mentorship of emerging researchers, and recognition through multiple research awards and distinctions.[1]

Her research agenda aligns with contemporary priorities in education and digital transformation by addressing responsible applications of artificial intelligence, personalised learning, educator capacity building, and data-informed decision-making. The combination of research excellence, practical impact, and academic leadership provides a substantive basis for consideration within academic recognition programs such as the Top Teachers Awards.[1]

Conclusion

Ean Teng Khor has established a multidisciplinary academic profile at the intersection of artificial intelligence, learning sciences, educational technology, and analytics. Through research leadership, funded projects, scholarly publications, teaching innovation, and service to the international academic community, she has contributed to advancing understanding of how intelligent technologies can support learning and educational improvement. Her record of scholarship and professional engagement reflects an ongoing commitment to evidence-based educational innovation and the responsible integration of AI within learning environments.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Ean Teng Khor, Author ID 54393424800. Scopus. https://www.scopus.com/pages/authors/54393424800
  2. Khor, E. T., & K. M. (2023). A systematic review of the role of learning analytics in supporting personalized learning. Education Sciences, 14(1), 51. DOI: https://doi.org/10.3390/educsci14010051
  3. Teng, K. E. (2014). An Analysis of ODL Student Perception and Adoption Behaviour using Technology Acceptance Model. International Review of Research in Open and Distance Learning, 15(6), 275–288. DOI: https://doi.org/10.19173/irrodl.v15i6.1732
  4. Puah, S., Khalid, M. I. S., Looi, C. K., & Khor, E. T. (2022). Investigating working adults’ intentions to participate in microlearning using the decomposed theory of planned behaviour. British Journal of Educational Technology, 53(2), 367–390. DOI: https://doi.org/10.1111/bjet.13170
  5. Khor, E. T. (2022). A data mining approach using machine learning algorithms for early detection of low-performing students. The International Journal of Information and Learning Technology, 39(2), 122–132. DOI: https://doi.org/10.1108/IJILT-09-2021-0144

Raghi K R | Deep Learning | Research Excellence Award

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Ms. Raghi K R | Deep Learning | Research Excellence Award

Sathyabama University | India

Ms. Raghi K R is a distinguished researcher in the field of Computer Science and Engineering, with expertise spanning Artificial Intelligence, Deep Learning, Machine Learning, Cloud Security, and Web Mining. Her research primarily focuses on developing privacy-preserving and secure computing frameworks, innovative AI and IoT-based solutions for healthcare, smart surveillance, and environmental monitoring, and advancing cloud-based neural network applications. She has made significant contributions to privacy-preserving deep neural network classification over signature cryptosystems in cloud environments and has been recognized for her innovative work in smart healthcare systems, anomaly detection in CCTV surveillance, and real-time data analysis. With 29 citations, an h-index of 4, and an i10-index of 0 (Google Scholar), her research impact is further reflected in her scholarly publications and patents, encompassing AI-driven predictive modeling, blockchain-based data security, and deep learning applications in medical and industrial domains. Dr. Raghi has published extensively in IEEE and other international conferences, including landmark works on Huntington’s disease prediction, touch-free smart home systems, proactive detection of cybersecurity threats, and driver distraction detection. Her research is characterized by integrating theoretical rigor with practical applications, often combining AI, IoT, and cloud computing to solve complex real-world problems. She actively mentors student projects and has guided multiple innovative projects recognized by professional societies. Her work has been cited across numerous documents, demonstrating both the quality and relevance of her contributions in AI, cybersecurity, and cloud computing research. Dr. Raghi K.R has been honored with the Research Excellence Award for her outstanding contributions to advanced computing and AI applications.

Publication Profile

Orcid | Google Scholar

Featured Publications

  • Raghi, K.R., & Sudha, K. (2024). Software development automation using generative AI. International Conference on Emerging Research in Computational Science.

  • Thomas, R.K., Lemuel, C.P., Sanjay, G., Pandeeswaran, C., & Raghi, K.R. (2024). Advanced CCTV surveillance anomaly detection, alert generation and crowd management using deep learning algorithm. 3rd International Conference on Artificial Intelligence for Internet of Things.

  • Devi, S.R., Priya, S.G., Sathi, G., Kumar, S.N., & Raghi, K.R. (2024). Design and development of a touch free smart home controlling system based on virtual reality (VR) technology. International Conference on Intelligent Systems for Cybersecurity (ISCS 2024).

  • Vethavikashini, A.M., Jamal, S.M., & Raghi, K.R. (2024). Huntington’s disease prediction using Xception CNN. 2nd International Conference on Disruptive Technologies (ICDT 2024), 201-208.

  • Raghi, K.R., & Paramarthalingam, A. (2024). Proactive detection of Mirai botnet threats: Leveraging XGBoost for enhanced cybersecurity. IET Conference Proceedings CP900, 34-39.

Mingyue Zhang | Artificial Intelligence | Best Researcher Award

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Dr. Mingyue Zhang | Artificial Intelligence | Best Researcher Award

University of South China | China

Dr. Mingyue Zhang, Ph.D., is a dynamic researcher in computer vision, intelligent systems, and human–computer interaction, currently serving in the field of computer science with a focus on gesture recognition and lightweight deep learning models for edge devices. His research integrates advanced computer vision algorithms with human–machine collaboration, emphasizing intelligent gesture recognition for rehabilitation training, embedded AI, and Internet of Things (IoT)-enabled systems. Dr. Zhang has significantly contributed to developing efficient algorithms such as lightweight convolutional neural networks, adaptive Kalman filtering, and multi-sensor fusion frameworks, which enhance real-time performance in gesture estimation, object tracking, and assistive technologies. His innovative work bridges the gap between deep learning theory and practical deployment on embedded systems and mobile platforms. With over 25 publications in high-impact journals indexed in SCI and EI, including IEEE Internet of Things Journal, Expert Systems with Applications, IEEE Access, and Journal of Supercomputing, his research has achieved growing academic recognition. Dr. Zhang’s work has been widely cited across the global impact of his contributions in computer vision and artificial intelligence. His research is currently supported by the Hunan Provincial Department of Science and Technology, focusing on intelligent gesture recognition in rehabilitation. In recognition of his outstanding contributions to scientific innovation and scholarly excellence, Dr. Zhang is honored with the Best Researcher Award for his pioneering advancements in AI-driven human–computer interaction and lightweight network modeling.

Publication Profile

Orcid

Featured Publications

  • Jiang, C., Zhang, M., Wang, Y., & Zhang, A. (2025). AHMOT: Adaptive Kalman Filtering and Hierarchical Data Association for 3D Multi-Object Tracking in IoT-Enabled Autonomous Vehicles. IEEE Internet of Things Journal.

  • Zhang, M., Zhou, Z., Tao, X., & Deng, M. (2023). Hand pose estimation based on fish skeleton CNN: Application in gesture recognition. Journal of Intelligent & Fuzzy Systems, 44, 8029–8042.

  • Zhang, M., Zhou, Z., Wang, T., & Zhou, W. (2023). A lightweight network deployed on ARM devices for hand gesture recognition. IEEE Access, 11, 45493–45503.

  • Zhang, M., Zhou, Z., & Deng, M. (2022). Cascaded hierarchical CNN for 2D hand pose estimation from a single color image. Multimedia Tools and Applications, 81, 25745–25763.

  • Zhang, M., & Zhou, Z. (2025). Speed-accuracy trade-off in lightweight-based hand pose estimation. The Journal of Supercomputing, 81, 1212

Jinping Xue | Artificial Intelligence | Best Researcher Award

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Ms. Jinping Xue | Artificial Intelligence | Best Researcher Award

Renergy Overseas Limited | China

Ms. Jinping Xue is an emerging researcher and engineer specializing in the integration of Artificial Intelligence with sustainable urban environments. Her research focuses on Smart Cities, Industrial AI, Edge Intelligence, and Environmental AI—fields that explore how data-driven intelligence can transform urban infrastructure into more adaptive, efficient, and sustainable systems. She has made significant contributions to the development of a privacy-preserving AI framework for smart city environmental monitoring, integrating federated learning with LSTM and genetic algorithms to achieve high pollution source traceability accuracy while maintaining data privacy. Her interdisciplinary expertise bridges environmental modeling, urban systems optimization, and AI-driven perception technologies, contributing to innovative solutions in sustainable city management and smart mobility. Xue’s scholarly work demonstrates a strong interest in the application of distributed sensing and federated learning for pollution source analysis, contributing to the broader goal of achieving clean, data-secure, and intelligent urban ecosystems. She has collaborated with experts in environmental sensing and intelligent perception to enhance the real-time adaptability of urban monitoring systems. Her publications, indexed in Scopus and Google Scholar, reflect her growing impact in the domains of computational intelligence and environmental systems. With research outputs recognized in peer-reviewed international journals such as Sensors, her work has begun to gain citations across the environmental AI research community. Her citation records are progressively expanding, with verified documentation and indexing available through Scopus and Google Scholar, and her h-index count demonstrates her early but impactful research trajectory. Through her interdisciplinary approach, Xue continues to push the boundaries of AI applications for sustainable development and smart city transformation.

Publication Profile

Google Scholar

Featured Publications

Xue, J., Hu, X., Liu, Q., Yin, C., Ni, P., & Bo, X. (2025). Air pollutant traceability based on federated learning of edge intelligent perception agents. Sensors, 25(19), 6119.

 

 

Feudjio Ghislain | Deep Learning | Best Research Article Award

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Mr. Feudjio Ghislain | Deep Learning | Best Research Article Award

Academician at University of Dschang, Cameroon.

Feudjio Ghislain, born on March 24, 1986, in Batcham, Cameroon, is a dedicated educator and researcher in electronics and applied physics. He is a Technical and Professional Education Teacher at Government Technical High School of Bangou and a Part-time Lecturer at Fotso Victor University Institute of Technology, University of Dschang. With a strong background in electronics, he has mentored students in various technical domains and supervised numerous academic projects. His research focuses on image classification, segmentation, deep learning, and machine learning. Ghislain has actively participated in multiple academic conferences and seminars. His passion for education, research, and technological advancement drives his contributions to academia and industry. Beyond academia, he is actively involved in community development initiatives and enjoys reading, music, and sports.

Professional Profiles📖

Scopus 

Education 🎓

Feudjio Ghislain is currently in his third year of a Doctorate/Ph.D. in Physics, specializing in Electronics at the University of Dschang, Cameroon. He holds a Master of Science in Physics (2019–2020) from the same university, where he specialized in Electronics with a ‘Good’ grade. His academic journey includes a DIPET II (2011) from HTTTC Douala, University of Douala, specializing in Electronics, a Bachelor of Science in Physics (2007) from the University of Dschang with a ‘Fairly good’ grade, and a Baccalaureate in Science (2004) from Lycée de Batcham. His academic progression showcases a consistent focus on electronics and applied physics, equipping him with in-depth expertise in his field.

work Experience💼

Feudjio Ghislain has been a part-time and professional teacher at the Fotso Victor University Institute of Technology (IUT-FV of Bandjoun) at the University of Dschang since 2018, teaching courses in Electrotechnics, Electronic Systems Maintenance, and Electrical Engineering. His subjects range from electronic construction to telecommunications and microcontroller applications. Since 2012, he has also served as an Electronics Teacher at the Government Technical High School of Bangou, teaching digital circuits, solar energy, and maintenance troubleshooting. His responsibilities extend to supervising final-year projects and serving on examination committees. Between 2020 and 2022, he was a part-time lecturer at the Evangelical University of Cameroon, teaching Biomedical Engineering. His expertise in teaching spans various educational levels, contributing significantly to the professional and technical development of students.

Research Focus

Feudjio Ghislain‘s research interests encompass image classification and segmentation, deep learning, and machine learning. His Ph.D. research focuses on the real-time analysis of medical images using second-generation wavelets, under the guidance of Professor TCHIOTSOP Daniel at the University of Dschang. His Master’s thesis explored embedded image processing systems for medical diagnostics, while his DIPET II research delved into spectral texture analysis using wavelets. His projects reflect a strong inclination toward practical and impactful applications of artificial intelligence in medical imaging and electronics. Through participation in conferences and research collaborations, he remains at the forefront of innovation in AI-based image processing.

Awards & Honors🏆 

Feudjio Ghislain has been recognized for his outstanding contributions to technical education and research. His dedication to advancing electronics education has earned him several professional affiliations, including membership in the SciPinion Scientific Community since 2024. He has also played a significant role in academic committees, acting as a referee for esteemed international journals like Heliyon and Computers in Biology and Medicine. His influence extends beyond the classroom, where he has contributed to organizing and evaluating national examinations. His research and teaching contributions have solidified his reputation as a committed educator and researcher in applied electronics and image processing.

Conclusion✅

Feudjio Ghislain is a strong candidate for the Best Researcher Article Award due to his expertise in electronics, image processing, and machine learning, alongside his teaching, peer-review, and project supervision roles. However, expanding his publication record in indexed journals, securing research funding, and increasing global collaborations would further strengthen his application for such a prestigious award.

Publications to Noted 📚

Title: “An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model”

Authors: Feudjio Ghislain​, Saha Tchinda Beaudelaire​, Tchiotsop Daniel