Emotion and motivation in everyday informal learning: A big data machine learning approach

Students learn in classrooms and schools. However, students also learn a lot more from everyday experiences in informal environments. They pursue their personal curiosities, interests and hobbies, seek information, create personal projects, pose questions to anonymous peers online, all in technology-rich environments from videos to social media to chatbots. The project aims to address important questions of emotion and motivation in authentic, everyday informal learning situations. For example, how students express and manage their feelings, pose questions online, seek text information and videos, pursue their personal interests, and discourse and collaborate with peers. The project will leverage big data our group has uniquely curated. One dataset relates to tens of millions of social media posts by adolescents in two different countries, and another dataset has billions of datapoints of digital device use. Both datasets are longitudinal. This project allows an unprecedented macro- and personal-level view of student motivation and emotion in everyday informal learning.

Applicants with an interest in applying cutting edge machine learning and data science tools to education and learning are welcome to apply. Applicants are expected to know basic programming and data science/data mining. Machine learning knowledge will be added advantage.

Important Note

We welcome you to have a conversation with Assistant Professor Farhan Ali to discuss this PhD project further. You are strongly encouraged to reach out to him at least 3 months in advance of application deadline (i.e. 31 January 2025) as the process of confirming the supervision and preparing your proposal for application may take longer than you anticipate. 

Minimum Entry Requirement

A Bachelor’s degree with honours at least at Second Class Upper level, Master’s degree in the relevant areas and the ability to pursue research in the candidate’s proposed field of advanced study.

Shortlisted applicants will undergo an interview session as part of the selection process.

A valid GRE score is required for applicants who are not graduates of the Autonomous Universities in Singapore. See detailed requirements for English language on competency and GRE requirements  here.