Research Assistant (Office for Research) [NIE]

The National Institute of Education invites suitable applications for the position of a Research Assistant 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 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:

Qualifications:

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

Desirable interests, skills, and attributes:

• Strong proficiency in quantitative research and data analysis tools (e.g., SPSS, R, Excel). Knowledge of qualitative software like NVivo is a plus.
• Experience or foundational knowledge in educational assessment, test design, and measurement validity (e.g., content/construct validity, reliability analysis).
• Experience in data cleaning, dataset management, and handling quantitative survey or test data.
• Solid foundation in basic statistical concepts and data visualization.
• Familiarity with primary Mathematics curriculum content, and general classroom environments.
• Exposure to or interest in AI applications, educational technology, learning analytics, or prompt engineering will be an advantage.
• Strong written, verbal, and interpersonal communication skills.
• High attention to detail, strong organizational skills, and dependability.
• Able to work both independently and collaboratively within a multidisciplinary team.

Key Responsibilities:

• Perform quantitative data management tasks, including data entry, cleaning, dataset preparation, and statistical analysis.

• Support measurement and assessment validation processes, including instrument pilot testing, item response tracking, and validity/reliability data entry.

• Support the formatting, proofreading, and organization of primary mathematics learning materials, assessments, and research tools.

• Conduct initial literature searches, locate academic sources, and maintain organized citation databases.

• Support on-site data collection efforts in schools or other research settings (e.g., distributing survey materials, test administration, and equipment setup).

• Assist with research administration, project documentation, and stakeholder communication.

• Carry out other research-related administrative and support duties as assigned by the Principal Investigators.

Application

Applicants (external and internal) will apply via Workday. We regret that only shortlisted candidates will be notified.

Closing Date

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