Recommendation engine for testing conditions based on evaluation of test entity scores

A method comprising:

• receiving, by a server comprising a server computing device coupled to a computer network and including a processor executing instructions within a memory, a threshold set by an administrator of an exam data and representing a desired level of trustworthiness set by the administrator for an anticipated testing activity which, if exceeded by an anticipated situational risk score, triggers a recommendation for at least one recommended predefined action in a plurality of recommended predefined actions stored in a data store coupled to the computer network;
• selecting, by the server from the data store, at least one entity data associated with the anticipated testing activity, the at least one entity data being selected from a plurality of entity data stored in the data store and comprising:
• the exam data;
• a test center data associated with a test center at which the anticipated testing activity will occur;
• a candidate data associated with:
• each of a plurality of candidates anticipated to be involved in the anticipated testing activity;
• and
• a historical testing data associated with each of the plurality of candidates;
• a country data;
• and
• a proctor data;
• performing, by the server, a risk assessment of each of the at least one entity data, indicating a risk level associated with the anticipated testing activity, and comprising:
• selecting, from the data store, a machine learning model stored in the data store;
• inputting the at least one entity data as a machine learning parameter into the machine learning model;
• receiving, as output from the machine learning model, the anticipated situational risk score, indicating a probability of an anticipated situational risk during the anticipated testing activity, and associated with, and based on, at least one metric derived from the at least one entity data;
• and
• comparing the anticipated situational risk score to the threshold;
• responsive to the anticipated situational risk score exceeding the threshold, updating, by the server, a database entry, associated in the data store with the at least one entity data, to include the at least one recommended predefined action in the plurality of recommended predefined actions, the at least one recommended predefined action providing a level of trustworthiness at least as high as the threshold.

A system comprising a server, comprising a server computing device coupled to a computer network and including at least one processor executing instructions within a memory which, when executed, cause the system to:

• receive a threshold set by an administrator of an exam data and representing a desired level of trustworthiness set by the administrator for an anticipated testing activity which, if exceeded by an anticipated situational risk score, triggers a recommendation for at least one recommended predefined action in a plurality of recommended predefined actions stored in a data store coupled to the computer network;
• select, from the data store, at least one entity data associated with the anticipated testing activity, the at least one entity data being selected from a plurality of entity data stored in the data store and comprising:
• the exam data;
• a test center data associated with a test center at which the anticipated testing activity will occur;
• a candidate data associated with:
• each of a plurality of candidates anticipated to be involved in the anticipated testing activity;
• and
• a historical testing data associated with each of the plurality of candidates;
• a country data;
• and
• a proctor data;
• perform a risk assessment of each of the at least one entity data, indicating a risk level associated with the anticipated testing activity, and comprising:
• selecting, from the data store, a machine learning model stored in the data store;
• inputting the at least one entity data as a machine learning parameter into the machine learning model;
• receiving, as output from the machine learning model, the anticipated situational risk score, indicating a probability of an anticipated situational risk during the anticipated testing activity associated with, and based on at least one metric derived from, the at least one entity data;
• and
• comparing the anticipated situational risk score to the threshold;
• responsive to the anticipated situational risk score exceeding the threshold, updating, by the server, a database entry, associated in the data store with the at least one entity data, to include the at least one recommended predefined action in the plurality of predefined actions, the at least one recommended predefined action providing a level of trustworthiness at least as high as the threshold.