Abstract: Digital learning or tutoring systems as described herein embed, by a trained machine learning knowledge tracing engine, an array for learner interactions X into a static representation ej corresponding to a prior learner interaction xj and determine a contextualized interaction representation hj based on this. Digital tutoring systems described herein calculate, by a masked attention layer of the trained machine learning knowledge tracing engine, an attention weight Aij based on a time gap between two learner interactions with the system, and can calculate a contextualized interaction representation hj, wherein the contextualized interaction representation hj is proportional to the attention weight Aij. The systems can provide for display at the GUI a second question item based on the contextualized interaction representation hj, the second question item corresponding to a recommended learner recommendation.

Abstract: Digital learning or tutoring systems as described herein embed, by a trained machine learning knowledge tracing engine, an array for learner interactions X into a static representation ej corresponding to a prior learner interaction xj and determine a contextualized interaction representation hj based on this. Digital tutoring systems described herein calculate, by a masked attention layer of the trained machine learning knowledge tracing engine, an attention weight Aij based on a time gap between two learner interactions with the system, and can calculate a contextualized interaction representation hj, wherein the contextualized interaction representation hj is proportional to the attention weight Aij. The systems can provide for display at the GUI a second question item based on the contextualized interaction representation hj, the second question item corresponding to a recommended learner recommendation.

Abstract: Systems and methods for dynamically assessing and providing feedback to a learner include displaying a set of assessment questions on a graphical user interface, obtaining a set of responses corresponding to the assessment questions, obtaining a set of diagnostic scoring rules including a set of diagnostic parameters corresponding to each assessment question and a response key, obtaining a set of learner-specific behavioral parameters, applying the set of diagnostic scoring rules to the set of responses to generate a learner response matrix, generating a learner attribute profile by applying as a set of probabilities of mastering each learning category to the learner response matrix, and estimating a learner response to a subsequent assessment question by applying a cognitive diagnostic model (CDM) or a Bayesian knowledge tracing (BKT) process to the learner attribute profile to the learner attribute profile.