Focus

An overview of CRADLE's focus

Research at CRADLE is an interdisciplinary effort. Our researchers come from diverse backgrounds, ranging from the Learning Sciences, Science of Learning, Computer Sciences, Neurosciences, Organisational behaviour, and Engineering. We actively engage with instructors and professors from schools cross NTU, local and international collaborators. We work closely with our colleagues, policy-makers, and industry stakeholders to advance research for designing new models and innovative tools for learning and capacity building. We iteratively build systems, test for learning, and enthusiastically share our research practices with our academic and practice collaborators.

CRAD​LE's research falls under three thrusts:

Research Thrusts​

Sub Areas of Focus​

Outcomes​

Science of Learning​

Educational Neuroscience​

Lifelong Learning​

  • Optimizing domain (e.g. language, numeracy) learning​

  • Learning outcomes of Translational Neuroscience​

  • Motivated Learning and Engagement​

  • Centre for Lifelong Learning and Individualized Cognition (CLIC) (Supported by the National Research Foundation, under the Campus for Research Excellence and Technological Enterprise (CREATE) programme)


Research Thrusts​

Sub Areas of Focus​

Outcomes​

Learning Sciences​

Computer-supported Collaborative Learning​

Learning analytics and e-Learning​

  • Online collaborative knowledge building ​

  • Text-analysis and learning analytics tools​

  • Understanding Learning behaviours and outcomes​

  • Apps for just-in-time learning (e.g. peer tutoring)​

 

Research Thrusts​

Sub Areas of Focus​

Outcomes​

Future Economy and Workforce Learning

Technology and Innovation in Adult Learning

Learning Cities and Smart Cities

Digital Futures and Human Capabilities

  • Societal and individual economic outcomes

  • I​ntergenerational social and cultural learning

  • Mental and physical well-being​

  • Competencies and skills (technological and non-technological) for future digital economies and societies

  • Artificial intelligence-aided decision making​