Technology-Enhanced Learning (TEL)
Modern digital education requires platforms that are not only scalable but also highly personalised. Learners have different backgrounds, learning speeds, preferences, and cognitive styles.
My research in Technology-Enhanced Learning aims to harness cloud computing and artificial intelligence to design adaptive, personalised, and robust e-learning systems.
Research Themes
- Cloud-Based E-Learning Architectures: Designing models and frameworks for deploying e-learning systems on cloud environments, providing high availability, scalability, and cost-efficiency.
- Adaptive Presentation & Personalisation: Constructing personalisation algorithms that adapt course presentation styles based on an individual's learning style and working memory capacity.
- Context-Aware Ubiquitous Learning: Developing ubiquitous learning systems that can retrieve educational resources based on context (e.g. learner location, device type, environment) utilising Case-Based Reasoning (CBR) and Nearest Neighbor algorithms.
- Open Learning and Open Educational Resources (OER): Researching the optimisation of open educational resources in cloud-based environments to make high-quality education globally accessible (specifically looking at Indonesia's OER initiative).
Related Publications
- Cloud-Based E-Learning: A Proposed Model and Benefits by Using E-Learning Based on Cloud Computing for Educational Institution
- Open Learning Optimisation Based on Cloud Technology: Case Study Implementation in Personalisation E-Learning
- Adaptive Presentation Based on Learning Style and Working Memory Capacity in Adaptive Learning System
- Context-Aware Ubiquitous Learning on the Cloud-Based Open Learning Environment: Towards Indonesia Open Educational Resources (I-OER)
- An Approach to Detect Learning Types Based on Triple-Factor in E-Learning Process