⚙️ Ralph, the ultimate Learning Record Store (and more!) for your learning analytics
My Learning Analytics (MyLA)
An AI-driven productivity tracking app built with Python, Streamlit, SQLite, and Machine Learning. It logs and analyzes study sessions, predicts productivity using Random Forest models, and visualizes key insights to help learners improve focus, habits, and overall academic efficiency.
An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.
Adaptive STEM learning system using Q-learning to personalize educational pathways. Integrated with Moodle via LTI 1.3. Achieves 22.5% score improvement and 51% reduction in weak skills through AI-driven recommendations and explainable decisions (SHAP).
This is a work-in-progress repository for a deep learning based moderator agent for critical discourse in discussion forums.
Aggregation of GitHub activities and transformation in Pandas Dataframes
Module Progress is a Python script and Tableau workbook for visualizing students progress in a Canvas course.
A crawler for Coursera
Open source application to sonify learning analytics data based on users preference. Designed to be used as a data exploration tool.
Scripts for the OpenLRW API
AI-powered Knowledge Space Theory for adaptive education. 10 Claude Code skills implementing the full KST pipeline — from course materials to knowledge graphs, adaptive assessment, personalized materials, and JIT instruction planning.
Aggregation of github activities on multiple repositories based on github2pandas
一个围绕学习记录、专注、数据分析、预测与 AI 复盘规划构建的个人学习分析系统
Our demo is a web-based learning analytics dashboard called the Learning Trace Platform (学迹通). It can take in CSV files containing students’ answers on quizzes and questions and turn that into visual dashboards for both teachers and students to watch and use, and powered by LLM to provide personal feedback and suggestions.
Early academic risk detection using student engagement data (OULAD) with time-aware machine learning features.
MCP server that tracks student performance and identifies learning gaps
An AI-powered platform that predicts student risk levels from engagement metrics using machine learning. Built with Python, Pandas, NumPy, Scikit-learn, and Streamlit, featuring an end-to-end ML pipeline, automated retraining, interactive dashboard, and multiple ML model comparisons for best performance.
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