Research Assistant (Office for Research) [NIE]

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

Academic qualifications:

• A Bachelor’s degree in Education, Psychology, Sociology, Learning Sciences, or a closely related discipline.

Work experience:

• Prior experience in teaching Primary Mathematics, preferably in local school context.
• Preferably have prior experience in academic writing, literature reviews, data collection, or mixed-methods analysis (quantitative and qualitative).

Desirable interests, skills, and attributes:

• Familiar with primary Mathematics curriculum, teaching and assessment practices.
• Strong interest in education, especially in school-based research and classroom learning design.
• Excellent communication and organizational skills, with strong attention to detail in managing research data and documentation.
• Strong interpersonal skills, with the ability to engage professionally and collaboratively with researchers, teachers, and students.
• A self-motivated and proactive team player who is also able to work independently when required.

Responsibilities:

• Assist in the preparation and formatting of research materials, such as mathematics-related learning tasks and assessment instruments.
• Assist with literature reviews by searching, organizing, and drafting summaries of relevant literature.
• Support data management tasks, including data entry, data cleaning, and conducting preliminary statistical analyses.
• Coordinate and support data collection efforts in schools or other research settings.
• 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.

Hiring Institution: NIE