Abstract
Existing educational platforms often operate without longitudinal awareness of student academic histories, with performance data remaining fragmented across disconnected systems, courses, and academic years. This fragmentation limits the ability of tutoring systems to identify root causes of learning difficulties or to leverage documented student strengths when generating instructional responses. The disclosed technology provides a tutoring system that aggregates academic records from multiple educational data sources and constructs unified learner profiles by analyzing both structured data, such as assessment scores, and unstructured educational artifacts, such as essays and teacher feedback. A response generation component synthesizes personalized explanations based on the comprehensive learner profile, addressing prerequisite knowledge gaps and incorporating references to the student's demonstrated proficiencies. This approach enables context-aware tutoring that accounts for cross-domain academic history and longitudinal learning patterns.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Arthur, Brittany; Taylor, Tim; Chang, Teresa; Chase, Craig; Mendez Ruiz, Mauricio; Ocampo Diaz, Gerardo; Goodgame, Sam; and Jamison, Tricia, "Longitudinal Learner Profile Construction From Aggregated Multi-Source Educational Data for Personalized Tutoring", Technical Disclosure Commons, (July 28, 2026)
https://www.tdcommons.org/dpubs_series/11162