Dynamic education planning methods and systems

A computing system for generating a plurality of education plans for a plurality of fellows, comprising:

• one or more processors;
• and
• one or more memories having stored thereon executable instructions that, when executed by the one or more processors, cause the computing system to:
• receive, via the one or more processors, a set of fellow skill graphs,
• each including a respective plurality of nodes, wherein each node represents a respective skill corresponding to a respective skill rating, and wherein a first node from the respective plurality of nodes is related to a second node from the respective plurality of nodes, and
• each corresponding to a respective one of the plurality of fellows, wherein updating each of the set of fellow skill graphs comprises:
• generating a first update to a corresponding skill rating of the first node based on updated performance information associated with the first node;
• and
• generating a second update to a corresponding skill rating of the second node by propagating the first update based on one or more of:
• one or more decay relevance value representing skill decay between the first node and the second node;
• or
• one or more amplifying relevance value representing skill amplification between the first node and the second node;
• receive, via the one or more processors, a plurality of electronic calendar objects,
• each corresponding to a respective one of the plurality of fellows, and
• each including at least one free block of time;
• process, via the one or more processors, the set of fellow skill graphs and the plurality of electronic calendar objects using a trained machine learning model to predict a set of sessions corresponding to the plurality of fellows,
• wherein the trained machine learning model is trained using successive evaluation of historical training samples to predict the set of sessions, the successive evaluation including creating weights of the trained machine learning model and adjusting the weights, and
• and wherein processing the set of fellow skill graphs and the plurality of electronic calendar objects includes, for each fellow, determining that a corresponding session has a duration less than or equal to the at least one free block of time included in a corresponding electronic calendar object;
• and
• cause, via the one or more processors, the set of sessions to be displayed on a roadmap graphical user interface, wherein each of the set of sessions includes a respective session topic, a respective session type, a respective session date, a respective session start time and a respective session duration.

A non-transitory computer-readable medium having stored thereon executable instructions that, when executed, cause a computer to:

• receive, via one or more processors, a set of fellow skill graphs,
• each including a respective plurality of nodes, wherein each node represents a respective skill corresponding to a respective skill rating, and wherein a first node from the respective plurality of nodes is related to a second node from the respective plurality of nodes, and
• each corresponding to a respective one of a plurality of fellows, wherein updating each of the set of fellow skill graphs comprises:
• generating a first update to a corresponding skill rating of the first node based on updated performance information associated with the first node;
• and
• generating a second update to a corresponding skill rating of the second node by propagating the first update based on one or more of:
• one or more decay relevance value representing skill decay between the first node and the second node;
• or
• one or more amplifying relevance value representing skill amplification between the first node and the second node;
• receive, via the one or more processors, a plurality of electronic calendar objects,
• each corresponding to a respective one of the plurality of fellows, and
• each including at least one free block of time;
• process, via the one or more processors, the set of fellow skill graphs and the plurality of electronic calendar objects using a trained machine learning model to predict a set of sessions corresponding to the plurality of fellows,
• wherein the trained machine learning model is trained using successive evaluation of historical training samples to predict the set of sessions, the successive evaluation including creating weights of the trained machine learning model and adjusting the weights, and
• and wherein processing the set of fellow skill graphs and the plurality of electronic calendar objects includes, for each fellow, determining that a corresponding session has a duration less than or equal to the at least one free block of time included in a corresponding electronic calendar object;
• and
• cause, via the one or more processors, the set of sessions to be displayed on a roadmap graphical user interface, wherein each of the set of sessions includes a respective session topic, a respective session type, a respective session date, a respective session start time and a respective session duration.