Team: Tammy Kwan and Brenden Lake
Overview: This project develops a training pipeline for neural-network-based simulated student models. The approach combines synthetic reasoning traces generated by rule-based student models with human student reasoning data to substantially reduce the amount of human data required.
Subject Focus: Fraction arithmetic within grades 6-8, with exploratory transfer to decimals, percentages, and early algebra.
Targeted Universalism Focus: This project responds to the reality that high-quality, individualized diagnostic attention in mathematics is unevenly distributed, frequently leaving students in under-resourced classrooms without consistent formative feedback due to large class sizes, limited staffing, or high learner variability.
Public Goods & Deliverables:
- An open-source codebase (released via GitHub under the Apache 2.0 license) detailing workflows for dual synthetic-human optimization.
- Fine-tuned neural weights that replicate empirically observed human accuracy, error distributions, and individual student response patterns in fraction arithmetic.
- Implementation Guidance: Technical reports, metadata schemas, and adaptation guidelines to help learning engineers extend the pipeline to adjacent mathematics domains.