GamaLearn

• Develop and implement data science models to enhance learning, assessment, and training platforms. • Conduct data analysis, statistical analysis, and predictive modeling to identify trends and patterns in educational data. • Develop machine learning models including logistic regression, random forest, and clustering algorithms tailored for educational purposes. • Use psychometric methods like Item Response Theory and Rasch Models to improve assessment data quality. • Create data visualizations and dashboards using Power BI and Tableau for educational stakeholders. • Work with cross-functional teams, including educators and researchers, to understand requirements and deliver insights. • Optimize ETL processes for data integration and preprocessing specific to educational datasets. • Support the development of adaptive learning systems and personalized learning experiences through data-driven approaches. • Implement and utilize Graph API and OData endpoints to integrate and analyze data from various sources. • Work with large language models (LLMs) and retrieval-augmented generation (RAG) techniques to enhance educational tools and provide personalized learning experiences. • Leverage vector databases to store and retrieve high-dimensional data efficiently.