Synergising Learning Theory, Learning Analytics, and Educational Technologies

The School of EECS is hosting the following PhD Thesis Review Seminar

Synergising Learning Theory, Learning Analytics, and Educational Technologies

Speaker: Hatim Lahza
Host: A/Prof Hassan Khosravi

Abstract: The adoption of educational technologies in modern educational systems has significantly advanced the field of learning sciences. This ecosystem shift, particularly evident within digital learning environments, has enriched pedagogical strategies and redefined learning evaluation methodologies by leveraging sophisticated developments in learning analytics. Despite these advancements, a notable gap persists in effectively applying learning theories within these digital environments. This shortfall partly stems from the ongoing development of empirical evidence supporting these theories and a prevalent reliance on software engineering perspectives, which may not fully integrate learning theory insights. In the area of learning analytics, initial methodologies, primarily based on data mining, often neglect critical aspects of learning theories and the educational context, highlighting the need for a more comprehensive approach. This thesis addresses this gap by proposing a triadic relationship among learning theories, educational technologies, and learning analytics. It aims to leverage learning theory to inform the development of learning analytics, taking into account the educational technology context. Simultaneously, it explores how learning theory can guide the development of educational technology, using learning analytics to gain insight into its effectiveness. Specifically, the thesis employs a theory-based learning analytics methodology to develop learning analytics about students’ learning tactics and strategies across two learning platforms with distinct contexts. It also proposes and evaluates theory-driven user interfaces designed to assist students in creating learning resources and providing peer feedback.

Bio: Hatim Lahza is a PhD candidate in the Data Science group at the School of Electrical Engineering and Computer Science, University of Queensland, under the supervision of A/Prof Hassan Khosravi and A/Prof Gianluca Demartini. Hatim received his BSc degree in Computer Science from Umm AlQura University in Saudi Arabia and his MSc degree in Computer Science from the University of Queensland. His primary research interests include machine learning, learning analytics, and learning sciences.