Learning Analytics
Overview
Learning Analytics, as described by Siemens and Long (2011), is the process of collecting, analyzing, and interpreting data from educational environments to gain insights and enhance learning outcomes. It utilizes techniques from data mining and machine learning. Additionally, Learning Analytics encompasses the utilization of data and analysis techniques from disciplines like data science, educational research, and human-centered design to understand and improve learning and education. Its applications and benefits extend to learners, teachers, and educational institutions alike.
PRINCIPLES
- Align implementation of institutional surveys with the university's strategic goals.
- Foster coordination and collaboration among stakeholders for effective survey implementation, analysis, and formulation of recommendations.
- Ensure individual surveys have specific focus to gather targeted data and derive institution-level recommendations.
- Triangulate various forms of data and evidence for a comprehensive understanding of student learning experiences and achievements.
- Establish a central repository with business intelligence functions for efficient data storage, analysis, and presentation.
OBJECTIVES
The objectives of this approach to Learning Analytics are to:
- Integrate various aspects of institutional surveys into a comprehensive framework.
- Collaborate with relevant units/stakeholders to develop, monitor, and enhance institutional surveys continuously.
- Utilize data and evidence to drive change and development in curriculum, teaching, and learning.
General Enquiries
 | Phone: (852) 2616-7117 |
 | E-mail: [email protected] |
 | 2/F, B Y Lam Building, Lingnan University, Tuen Mun, The New Territories, Hong Kong. |