What’s in a Word? Extending Learning Factors Analysis to Model Reading Transfer

Learning Factors Analysis (LFA) has been proposed as a generic solution to evaluate and compare cognitive models of learning [1]. By performing a heuristic search over a space of statistical models, the researcher may evaluate different cognitive representations of a set of skills. We introduce a scalable application of this framework in the context of transfer in reading and demonstrate it upon Reading Tutor data. Using an assumption of a word-level model of learning as a baseline, we apply LFA to determine whether a representation with fewer word independencies will produce a better fit for student learning data. Specifically, we show that representing some groups of words as their common root leads to a better fitting model of student knowledge, indicating that this representation offers more information than merely viewing words as independent, atomic skills. In addition, we demonstrate an approximation to LFA which allows it to scale tractably to large datasets. We find that using a word root-based model of learning leads to an improved model fit, suggesting students make use of this information in their representation of words. Additionally, we present evidence based on both model fit and learning rate relationships that low proficiency students tend to exhibit a lesser degree of transfer through the word root representation than higher proficiency students.