Patent ID: 11915615
Assignee: nan
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A method for identifying specific groups of colluding students in academic exams and level of confidence by a false positive rate from an input electronic file of test data, the method comprising:
receiving, by a collusion detection system from a client device, an input electronic file comprising a set of academic test data representing results of at least one student exam, where the academic test data includes student identifiers associated with students taking the at least one student exam, question scores associated with each student identifier, and the total score for each student on the test;
calculating an identity score for each pair of students, the identity score representing a quantified similarity of question scores of the students within each pair;
determining a maximum identity score for each student, which is the highest identity score of all pairs of students including that student;
generating a collusion score for each student from the maximum identity score of that student by:
subtracting from the maximum identity score an average identity score to generate an identity metric for that student, where the average identity score is an estimated average of identity scores associated with pairs of students including that student;
constructing a ranked list of students where the students are ranked by total test score; and
dividing the identity metric of that student by a local average identity metric to generate a collusion score for that student, where the local average identity metric is an estimated average of a set of identity metrics for a subset of students who lie adjacent to one another on the total test score ranked list;

determining one or more collusion groups of students from the collusion scores by identifying students whose collusion scores are above at least one collusion score threshold;
generating synthetic exam results for a plurality of synthetic exams by generating random question scores for synthetic students that follow the frequencies of questions scores in the at least one student exam using the collusion detection system;
determining one or more synthetic collusion groups from the synthetic exam results using the at least one collusion score threshold;
estimating a synthetic false positive rate for one or more collusion groups in the at least one student exam based on the number of synthetic students identified as being members of one or more collusion groups in the synthetic exam results; and
sending the determined one or more collusion groups and the student identifiers of their members, and the estimated synthetic false positive rates associated with the collusion groups, to the client device by the collusion detection system.