Research Associate (Office for Research) [NIE]

Nanyang Technological University Singapore; Singapore; On-site; SGD 60,000 - 85,000; Full time

Job summary

The National Institute of Education invites applications for a Research Associate on a 12-month contract (renewable) at the Office for Research. You will contribute to the AI4SoL project, focusing on data-driven approaches to mathematics education and prompt engineering in learning analytics.

Ideal candidates hold a Master’s degree in Education or related fields, with strong experience in research design, data analysis, and academic writing.

Qualifications

• Master's degree in Education, Psychology, Learning Sciences, Mathematics Education, or a related field.
• Prior experience teaching Primary Mathematics.
• Proficiency in SPSS, R, NVivo, and Excel for data analysis.
• Strong written and verbal communication skills.

Responsibilities

• Lead literature reviews and synthesize core research findings.
• Design, develop, and refine mathematics learning materials and instruments.
• Manage and execute quantitative and qualitative data analysis.
• Coordinate on-site data collection in schools and research settings.
• Draft research updates and technical reports.
• Support Principal Investigators with administrative tasks.

Skills

Research design

Data analysis

Academic writing

Team collaboration

Education

Master's degree in Education, Psychology, Learning Sciences, Mathematics Education, or related field

Tools

SPSS

R

NVivo

Excel

Job description

The National Institute of Education invites suitable applications for the position of a Research Associate on a 12-month contract (renewable) at the Office for Research.

Project Title

Data and Theory Driven Artificial Intelligence to Boost the Science of Learning (AI4SoL)

Project Introduction

The use of educational technologies is increasingly becoming more ubiquitous in mathematics education. While artificial intelligence (AI) has been integrated into the development of educational technologies, for example Intelligent Tutoring Systems (ITSs), the recent advancements in generative AI (gen AI) promise personalized learning in a more natural way. In particular, leveraging the natural language capabilities of large language models (LLMs) - a type of gen AI – to enable dialogic practice is a promising nascent field of study. While mathematics learning requires both conceptual and procedural knowledge, students learn mathematics through sense-making of these types of knowledge through problem-solving. This requires students to access and/or construct their own relevant mathematics knowledge, create representations of said knowledge, and map their representations to the knowledge. Besides using these steps to problem-solve, mathematics learning also requires students to communicate their problem-solving strategies and solutions. From a socio-constructivist perspective, co-constructing knowledge requires a dialogic exchange between teacher and students, and feedback from teachers is essential in mathematics discourse. Based on Thurlings et al.’s models of feedback processes, most feedback in computer systems is cognitivist in nature. The advancements in LLMs appear promising in bridging this dialogic gap in feedback and learning via computer systems. This study aims to test the efficacy of LLMs in teaching mathematics word problem solving through dialogue in structured inquiry with/without adaptive learning tasks compared to self-directed problem-solving in improving mathematical problem-solving accuracy, metacognition and self-regulation, and long-term transfer of problem-solving strategies. Findings could contribute to the growing literature on gen AI in education within the field of Artificial Intelligence in Education (AIED) and implications of design and development of LLM-applications and prompt engineering. Furthermore, the use of process data as a study instrument could contribute to both methodology (introduce system process data to support findings from research on technology-education interactions) and design (system designs that leverage gen AI to enact educational practices that work, i.e., dialogic practice).

Education

Education Study 2 aims to test the efficacy of AI-Supported Adaptive Structured Inquiry in mathematics word problem solving. Specifically, this study investigates the extent to which an AI-Supported Adaptive Structured Inquiry can:

• Improve problem-solving accuracy and conceptual understanding.
• Foster independent learning through scaffolded inquiry.
• Facilitate transfer of problem-solving strategies to new problems.

Findings could contribute to the growing literature on LLMs in education within the field of AIED and implications of design and development of LLM-based learning applications (e.g., through prompt engineering). Furthermore, the use of process data as a study instrument could contribute to both methodology (introduce system process data to support findings from research on technology-education interactions) and design (system designs that leverage LLM to enact educational practices (e.g., dialogic practice) that work.

Requirements

• Master's degree in Education, Psychology, Learning Sciences, Mathematics Education, or a related field.
• Prior experience teaching Primary Mathematics, preferably within a local school context.

Desirable Interests, Skills, And Attributes

• Deep familiarity with primary Mathematics curriculum, teaching and assessment practices.
• Strong interest in educational research, school-based studies, and classroom learning design.
• Proficiency in research and data analysis tools (e.g., SPSS, R, NVivo, Excel).
• Demonstrated experience or strong interest in AI applications, educational technology, learning analytics, or prompt engineering.
• Proven project coordination, organizational, and strong written/verbal communication skills.
• Proactive, self-directed, detail-oriented, and adept at managing complex project deadlines.

Key Responsibilities

• Lead the design, development, and refinement of primary mathematics learning materials, assessments, and research instruments.
• Conduct comprehensive literature reviews and synthesize core research findings.
• Manage and execute advanced quantitative and qualitative data analysis.
• Oversee and coordinate on-site data collection efforts in schools or other research settings (e.g., distributing survey materials, test administration, and equipment setup).
• Maintain comprehensive project documentation and draft research updates or technical reports.
• Carry out other research-related administrative and support duties as assigned by the Principal Investigators.

Closing Date

Closing date for advertisements will be set to 14 calendar days from date of posting.

Hiring Institution: NIE