Singapore University of Social Sciences

Analytics for Education and Learning

Analytics for Education and Learning (ADL525)

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: To be confirmed

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Education

Schemes: To be confirmed

Funding: To be confirmed

School/Department: S R Nathan School of Human Development


ADL525 Analytics for Education and Learning enables students to understand how raw data could and should be handled so that useful information can be extracted from the data. A knowledge society presents unique and fundamental questions of what data means and how to use it responsibly and well. In particular, the recent advent of big data and analytics has magnified the question, and creates both opportunity and conundrum for organisations and institutions, including educators. Students will have an overview of fundamental issues governing the use and analysis of data, with emphasis on education-related issues. Increasingly, educators have access to student data through the institution’s student management system. This course critically examines the place and use of educational analytics within the context of educational administrative and academic decision-making.

Level: 5
Credit Units: 5
Presentation Pattern: Every January


  • Data mining
  • Data analysis techniques
  • Fundamental Statistics
  • Descriptive techniques in analytics
  • Predictive models in analytics
  • Statistics and modelling
  • Data management
  • Data analysis and adult education issues
  • Human rights and educational research
  • Educational analytics
  • Results validation
  • Educational analytics and academic decision making

Learning Outcome

  • Assess the suitability of different data analysis techniques
  • Discuss the key principles of data analysis
  • Examine the use of educational analytics
  • Evaluate various approaches to best present your findings
  • Select appropriate methods in data analysis for different scenarios
  • Prepare and organise data derived from an identified field of specialisation
Back to top
Back to top