Invited Speakers

 

 

 

Suraya Masrom, Universiti Teknologi MARA, Malaysia

 

Associate Professor Ts. Dr. Suraya Masrom leads the Machine Learning and Interactive Visualization (MaLIV) Research Group at the Perak Branch of Universiti Teknologi MARA (UiTM), MALAYSIA. She serves as the chief editor of the Mathematical Sciences and Informatics Journal published by UiTM Press and has been recognized as the recipient of the university's Best Journal Editor award in 2023. She received her Ph.D. in Information Technology and Quantitative Science from UiTM in 2015. Her professional journey began in the realm of information technology, holding a position as an Associate Network Engineer at Ramgate Systems Sdn. Bhd (a subsidiary of DRB-HICOM) in June 1996, shortly after completing her bachelor's degree in computer science from Universiti Teknologi Malaysia (UTM) in March 1996.
After achieving a master's degree in computer science from Universiti Putra Malaysia in 2001, she transitioned into an academic career, starting as a lecturer at UTM. Her path led her to the Universiti Teknologi MARA (UiTM) campus in Seri Iskandar, Perak, Malaysia, in 2004. Her commitment to excellence in education was acknowledged when she was honored with the Best Educator award as part of the University Academic Award Program in 2019.
Suraya Masrom remains a highly active researcher in the fields of meta-heuristics search approach, scripting language, machine learning, and educational technology. Her research endeavors have secured substantial funding, totaling 600K in grants, encompassing 10K in international grants, 20K in industry grants, and 900K in national grants. Over the past five years, she has authored and co-authored more than 200 indexed journal and conference papers, culminating in a noteworthy h-index of 6 in Scopus and 8 in Google Scholar from 404 citations. For those wishing to get in touch, she can be reached via email at suray078@uitm.edu.my.

 

Speech Title: Understanding Hyper-Parameters and Feature Importance in GP-Based AutoML for ESG Compliance Prediction
Abstract: Automated Machine Learning (AutoML) offers substantial benefits in addressing real - world problems by expediting the development of machine learning models. In scenarios involving the analysis of companies’ Environmental, Social, and Governance (ESG) performance—where datasets often present significant challenges—AutoML emerges as a promising solution to manage these complexities effectively.
Despite the growing interest in leveraging Genetic Programming (GP) within AutoML for complex datasets, a critical gap remains: a comprehensive understanding of GP hyper - parameters and their impact on machine learning performance. While GP - based AutoML excels at automating various aspects of model development, limited research has explored the significance of individual features and the effect of GP population size within these models.
This study addresses this gap by presenting an in - depth analysis of model performance from multiple perspectives, including feature selection, GP population sizes, and comparisons with different machine learning algorithms. Additionally, the study offers valuable insights into the relationship between Pearson correlations, machine learning performance, and the importance of specific features.
The results indicate that incorporating all relevant determinants as features in GP - based AutoML, or focusing solely on firm characteristics, leads to superior performance with an optimal balance between True Positive Rate and False Positive Rate. The proposed models demonstrate high accuracy, with Area Under the Curve (AUC) values exceeding 0.9.
The novelty of this study lies in its empirical evaluation of various approaches to implementing GP - based AutoML. These findings offer alternative strategies for business investors to identify companies with robust sustainability practices more effectively.

 

Ying Tang, Southwest University, China

 

Dr. Ying Tang is an Associate Professor of Educational Technology at the Faculty of Education, Southwest University, China, and serves as Associate Editor for Future in Educational Research. Prior to joining Southwest University, Dr. Tang was a postdoctoral researcher at Indiana University’s School of Informatics, Computing, and Engineering, where she contributed to an NSF-funded project aimed at enhancing cybersecurity and privacy education for computing students in higher education. She holds a Ph.D. in Information and Technology Studies from the University of Hong Kong, a Master’s degree in Education from Vanderbilt University, and a Bachelor’s degree in English Language and Literature from the University of International Relations, China.
With a rich, cross-disciplinary academic background, Dr. Tang has authored over forty peer-reviewed publications, many of which have been featured in high-impact journals and conferences, including Computers & Education, Educational Research Review, British Journal of Educational Technology, and the International Journal of Educational Technology in Higher Education. Her research primarily focuses on optimizing student learning outcomes in computer-mediated environments, with an emphasis on the integration of innovative educational technologies and effective pedagogical strategies. Her work has been widely cited, with over 2,000 citations since 2020 and an h-index of 16. In her most recent research, Dr. Tang explores the ethical and moral implications of leveraging artificial intelligence and big data in education, focusing on how these technologies can be harnessed to enhance learning while ensuring fairness and responsibility.

 

Speech Title: A Systematic Review on Using Chatbots to Support Computer-Supported Collaborative Learning

Abstract: As artificial intelligence and natural language processing continue to evolve, chatbots have emerged as promising tools in Computer-Supported Collaborative Learning (CSCL). These intelligent conversational agents facilitate student interactions, support group collaboration, and provide personalized assistance, making them valuable tools in digital learning environments. However, their integration into CSCL remains an evolving field, requiring a deeper understanding of their capabilities, limitations, and impact on learning dynamics. This study presents a systematic review of recent research on the role of chatbots in CSCL, uncovering their technological and pedagogical potentials alongside critical challenges. Examinations reveal key affordances, including personalization, scalability, and real-time support, which enhance collaborative learning through adaptive group coordination, scaffolded knowledge-building, and AI-driven feedback mechanisms. Challenges such as balancing automation with human agency, aligning chatbot functionalities with pedagogical goals, and refining evaluation metrics are analyzed, highlighting design and implementation complexities. The synthesis proposes future strategies to transform chatbots into robust tools for collaborative learning ecosystems. By bridging theoretical insights with actionable design principles, this work offers educators and developers a roadmap to harness chatbots’ transformative potential in reshaping CSCL practices.

 

 

 

 

Jining Han, Southwest University, China

 

Dr. Jining Han is an associate professor in the Faculty of Education at Southwest University. He earned his Ph.D. in Second Language Acquisition and Educational Technology from the University of South Florida and holds a Master’s degree in Pedagogy from Arizona State University. He also received a postdoctoral fellowship position at the Georgia Institute of Technology. He conducts research in applying AI-supported learning, virtual reality in education, and smart learning environment.

 

Speech Title: Continue Using or Gathering Dust? A Mixed Method Research on the Factors Influencing the Continuous Use Intention for an Ai-Powered Adaptive Learning System for Middle School Students

Abstract: This study investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in middle school settings.