Job Description

The NUS Safety and Resilience Research Unit (SaRRU) is seeking a highly motivated Research Fellow (RF) to join our multidisciplinary team. The RF will work on the research project "Modelling Self-Regulated Online Learning: Video- and Digital Game-Based Learning (VBL and DGBL) for Construction Professionals." This project aims to investigate the self-regulated learning (SRL) subprocesses of online learners in the context of construction education, leveraging advanced learning analytics, machine learning, and deep learning techniques.

The candidate shall work under the supervision of the Principal Investigator (PI) and Co-PIs to conduct academic research and administrative work. Specific job activities may include:
•    Conduct literature review on topics related to the project;
•    Conduct experiments using EEG, eye tracker, video cameras, learning management system and other relevant instruments;
•    Collect and clean data obtained from the experiments;
•    Liaise with subjects and collaborators on meetings and data collections;
•    Design and develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols);
•    Analyze large-scale learning analytics data to uncover SRL patterns in video-based learning (VBL) and digital game-based learning (DGBL) environments;
•    Conduct process mining and network analysis to differentiate SRL patterns between high- and low-performing learners;
•    Contribute to data visualization, interpretation, and the preparation of high-quality research publications;
•    Write other research papers, reports and proposals;
•    And any other tasks required by the principal investigator.

Job Requirements

•    A PhD within the fields of computer science, learning analytics, engineering, statistics, mathematics, construction management or related field;
•    Strong expertise in machine learning, deep learning, and data mining;
•    Experience with multimodal data analysis (e.g., eye-tracking, EEG, fNIRS) is highly desirable
•    Proficiency in Python, R, or similar programming languages;
•    Experience with network analysis, process mining, and time-series data analysis is a plus
•    Strong analytical, problem-solving, and communication skills
•    Ability to work independently and collaboratively in a multidisciplinary team; and
•    Strong English writing and communication skills.

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : The Built Environment

Employee Referral Eligible: No

Job requisition ID : 28845