e-Testing from artificial intelligence approach

This paper presents a review of advanced technologies for e-testing using an artificial intelligence approach. First, this paper introduces state-of-the-art uniform test assembly methods to guarantee examinee test score equivalence even if different examinees with the same ability take different tests. More formally, each uniform test form has equivalent measurement accuracy but with a different set of items. To increase the number of assembled tests, some test assembly methods allow that any two tests of uniform tests can include fewer common items than a user allows as a test constraint. This situation is designated as the overlapping condition. However, these methods used with an overlapping condition are often adversely affected by bias of the item exposure frequency and decreased reliability of items and tests. Second, this paper introduces state-of-the-art uniform test form assembly with a constraint of item exposure. Most earlier studies of e-testing employ item response theory (IRT) to obtain each examinee’s test score. However, IRT has several strict assumptions. Recently, Deep-IRT, which employs deep learning to relax the assumptions, has attracted attention. Finally, this paper introduces Deep-IRT models.