Abstract: A health monitoring and location tracking behavior modification system and associated methods are disclosed. The health monitoring and location tracking behavior modification system is configured to monitor health, track location, and modify behavior of a custodial inpatient by way of a wearable wristband device, a beacon device, and a token-based reward system that provides an incentive for positive conduct in the form of digital tokens for entertainment and recreational communication usage of a tablet computing device. The health monitoring and location tracking behavior modification system tracks location and monitors health by a unique wristband device and locator beacons throughout a facility. The pairing of the unique wristband device and locator beacons enable automatic and accurate logging of information about each custodial inpatient in a facility inpatient monitoring log.

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.