An algorithm of constructing concept lattices for CAT with cognitive diagnosis

Concept lattices have proved useful in many fields such as machine learning, knowledge discovery, in which building concept lattices is extensively studied. A modification of the Godin’s algorithms is proposed. Considering attribute hierarchies, an algorithm are presented for computerized adaptive testing (CAT) with cognitive diagnosis (CD). Both of algorithms are studied experimentally, and the algorithm for CD–CAT is studied theoretically. Algorithmic complexity of the algorithms for CD–CAT is studied theoretically (time complexity is studied in the best case and the worst case). With eight attributes, the test results are generated for both algorithms. With 10 attributes, quadratic regression is given for CPU time for the algorithm of CD–CAT.