Academic Profile : Faculty

farhan.ali_1_1.jpg picture
Asst Prof Farhan Ali
Assistant Professor, Learning Sciences and Assessment
Assistant Professor, National Institute of Education - Learning Sciences & Assessment
External Links
 
Farhan Ali is an Assistant Professor in the Learning Sciences and Assessment Academic Group. A neuroscientist by training, he was previously an Associate Research Scientist at Yale University School of Medicine, performing translational neuroscience research related to brain disorders and learning using neuroimaging. Farhan received his PhD (Neuroscience) from Harvard University doing basic research on brain circuits underlying learning and a BSocSci (Psychology, 1st Class Honours) from National University of Singapore with undergraduate research in brain and cognition.

Farhan has taught and supervised students at NTU and Yale University in the areas of neuroscience, psychology, and education. His students have co-published papers and gone on to pursue education and STEM-related careers. At NIE, his teaching portfolio includes AI/machine learning, quantitative methods, technologies for learning and science of learning.

Research goals:
How we feel and what pushes us can determine what, and how much, we learn. Thus, our group is interested in using modern methods from educational data mining/data science, machine learning, and big data to better understand the complexities of emotions and motivation such as interest, curiosity, engagement. We tackle these issues particularly in technology-rich environments such as digital learning and social media. The overall goal is to discover and apply new knowledge to enhance learning experiences/outcomes, human functioning, and well-being.

Undergraduates, graduate students and research fellows are welcome to join our group.
Funding for PhD studies is available. More information at: https://www.findaphd.com/phds/project/emotion-and-motivation-in-everyday-informal-learning-a-big-data-machine-learning-approach/?p174912
educational data mining/data science, machine learning; emotion; motivation; AI for education
 
  • Enhancing guiding inquiry-based learning through teacher-chatbot collaboration
  • An Investigation of Learning and Teaching Supported by Personal Learning Devices (PLDs) in Secondary Schools
  • Emotion Recognition from EEG Signals using Machine Learning Approach
  • Test anxiety in students in Singapore
  • Sustained Creative Discourse for Deep Learning: Synchronous, asynchronous and blended environment.
  • NMASTE: Network Meta-AnalysiS in Translating Educational Neuroscience Research
  • Designing an Analytics Toolbox for Enculturating Teachers' Data-driven Mindset and Reflective Teaching Practice in Singapore Schools
  • Self-Regulated Learning, Emotion, and Achievement: Applying Multimodal Classroom Research in a Singapore School
  • AI-Scaffolded Seamless Chinese Character and Vocabulary Learning for Young Learners: The Design and Implementation of ARCHe 2.0
  • Development of Brain Networks and Social-Emotional Functions
  • VIZ: A live data visualisation app to promote classroom engagement and thinking