Think of this as an automated math tutor that watches how a student answers questions, figures out exactly what level of understanding they are at, and then routes them onto the best next learning path instead of giving the same fixed lesson to everyone.
Traditional teaching and testing treat most students the same, failing to adapt to each learner’s actual level of understanding. This system uses machine learning to diagnose where a student sits in the SOLO taxonomy (structure of observed learning outcomes) and then recommends customized learning pathways, reducing time wasted on too-easy or too-hard content and improving learning outcomes.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Quality and volume of labeled student response data mapped to SOLO levels; potential model drift as curricula and question styles change
Early Adopters
Instead of generic ‘adaptive learning’ that only reacts to right/wrong answers, this approach explicitly anchors predictions to the SOLO taxonomy of learning outcomes, allowing finer-grained diagnosis of conceptual depth and alignment with pedagogical theory when constructing customized learning pathways.