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Learning Analytics

Learning Analytics

Learning Analytics


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.

For Staff

Learning Analytics Community mid-term SEAS The Scholarship of Learning and Teaching

Learning Analytics Community

Mid-term CTLE

Student Early Alert System

The Scholarship of Learning and Teaching

Surveys Teaching and Learning Data Warehouse Portfolio  
Surveys Teaching and Learning Data Warehouse Teaching & Learning Evaluation and Governance  

For Students

Avoiding Plagiarism mid-term    
Avoiding Plagiarism Mid-term CTLE    


This approach to Learning Analytics reflects the following principles;

  • The implementation of the University’s institutional surveys related to teaching and learning, and analysis of data generated therefrom should be aligned to the University’s strategic goals
  • The University’s institutional surveys pertain to collective and concerted endeavours among various stakeholders. Therefore, coordination and collaboration are indispensable to effective implementation, meaningful analysis and interpretation of data, and formulation of pertinent recommendations to inform design, development and enhancement of curriculum, co-curriculum and extra-curriculum, as well as teaching and learning
  • Individual institutional surveys should have their respective focus, so as to avoid overlapping and to gather specific sets of data, in order to derive specific recommendations at institutional level
  • A comprehensive picture of student learning experiences and achievements can be ascertained through triangulating various forms of data and evidence, in order to derive overall recommendations at the institutional level
  • A central repository should be in place to store, organize, retrieve and archive current and historical data from various sources, so as to facilitate data analysis, management and retention. The ultimate prototype of such repository should possess certain business intelligence functions, characterized with some customized retrieval, analytical, infographic and presentation functions
  • The scholarship of teaching and learning can be enhanced via the collection, analysis and dissemination of findings as an essential aspect of the University’s Quality Assurance (QA) cycle



The objectives of this approach to Learning Analytics are to:

  • integrate various aspects related to design, administration, data analysis, storage and dissemination, and review of institutional surveys into a comprehensive framework
  • collaborate with relevant units/ stakeholders together to develop, monitor and enhance the university’s institutional surveys on continuing basis
  • advance the utilization of data and evidence generated therefrom to inform change and development in curriculum and teaching and learning
  • enhance the development of the scholarship of teaching and learning



General Enquiries

Phone  Phone: (852) 2616-7117
E-mail  E-mail:

2/F, B Y Lam Building,
Lingnan University,
Tuen Mun, The New
Territories, Hong Kong.

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