Assistant Professor,
Dr. Regina received her Bachelor's degree in Computer and Mathematical Sciences from Victoria University of Technology (Australia) and a PhD in Creative Multimedia from Multimedia University (Malaysia). Regina has more than twenty years of teaching experience in higher learning institutions. Her key research areas encompass AI in higher education, learning analytics, and data science, with an emphasis on NLP models.
Software Applications and Programming Languages
1. NLP-Derived Semantic Representations of Overreliance on AI
This study investigates how Natural Language Processing (NLP) can be used to analyse students' reflective writing in AI-assisted programming education. The research specifically explores how NLP models can detect patterns of overreliance on generative AI tools, such as ChatGPT, when students complete programming tasks. The work contributes to the growing field of AI-supported education and learning analytics, in which NLP techniques analyse students' textual data to understand their cognitive engagement and learning behaviour.
2. AI-augmented gamified handwriting learning system
I am currently exploring a prototype of an AI-augmented, gamified handwriting-learning system to enhance writing skills among younger children. The aim of this project is to combine AI handwriting recognition with real-time, personalized feedback to support children with writing issues, transforming handwriting practice from a repetitive task into an interactive and enjoyable learning experience.
My research focused on improving learning effectiveness through the design and application of multimedia learning objects in technology-enhanced education. Early in my work, I investigated how learning object design influenced students' cognitive understanding, particularly in complex subjects such as computer programming.
I examined how the granularity and structure of multimedia learning objects affected students' ability to comprehend programming concepts. The findings demonstrated that appropriately sized learning objects reduced cognitive overload and helped learners focus on specific concepts, leading to improved conceptual understanding. This work contributed to the fields of computer science education and instructional multimedia design by highlighting the importance of modular, well-structured digital learning resources.
Building on this foundation, my research direction expanded from multimedia learning object design toward AI-enhanced learning environments and predictive analytics. By integrating learning analytics, artificial intelligence, and adaptive learning technologies, my research aimed to develop systems capable of predicting student learning risks, personalising learning experiences, and improving educational outcomes.
University of Nottingham Malaysia Jalan Broga, 43500 Semenyih Selangor Darul EhsanMalaysia
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