Research & Evaluation



Research and evaluation form the backbone of ALSET’s evidence-based approach to educational improvement. In order to effectively enhance teaching and learning practices at NUS, we investigate new educational methodologies and assess their validity.

ALSET brings together researchers across disciplines, and cultivates collaborations within and beyond the university. Taking a wide lens to learning, we support a range of research interests and types of projects. Drawing from various NUS divisions and external sources, we will help open up access to big data for the improvement of learning outcomes.

Multidisciplinary Approach: Learning Science

An emerging discipline, learning science is the systematic study, design, and development of psychological, social, and technological processes and systems that support learners and learning in diverse contexts. Accordingly, learning science draws from many disciplines in order to develop evidence-based claims about how people learn. These disciplines include anthropology, biology, cognitive psychology, computer science, design, education, engineering, linguistics, medicine, sociology, etc. ALSET engages faculty members and research fellows from diverse academic backgrounds, and encourages cross-disciplinary research collaborations.

Learner Orientation

While research at ALSET is broad in scope, its overall focus is on learners and their learning contexts. Situated within NUS, we have ample opportunity to investigate what works for students in Singapore and the region. Examples of potential research topics include:

  • Mindset: Promote vs Prevent or Fixed vs Growth
  • Communal vs Individualist values
  • Levels of Self-Regulation
Application of Data Science to Learning

ALSET, in conjunction with the NUS Computing Centre, will coordinate the development of an in-house data warehouse, serving as a key resource for NUS faculty and our partners. This will enable cross-sectional and longitudinal analyses in areas such as:

  • Improving feedback to teachers and students
  • Identifying early predictors of academic success or challenges
  • Combining unique datasets to evaluate programs and student learning
  • Assessing a range of skills beyond simple academic performance
  • Documenting evidence-based claims about how people learn


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