Adaptive Assessment and Guessing Detection Implementation

Abstract

Computerized adaptive testing (CAT) is a context-based adaptive assessment. How-ever, the assessment result may not be valid because the examinee might cheat or guess the answers. Although there are many guessing detection methods, there are not many discussions about their implementation into CAT. Therefore, this paper presents an example of a modification of an existing software so the newly modified software can detect guessed answers and be able to select questions adaptively. The system can detect assuming behavior by recording the examinee’s answer time. Also, the designed system can like questions adaptively by connecting Fuzzy logic, which calculates what level the question should select for the next iteration. The system is responded well by elementary and college students. A total of 56.6% felt the system was straightforward to use. The detection methods can detect guessing behavior of about 73%. However, the system’s sensitivity is low if the method is forced to classify answers which answered in a long response time / general guessing. Never-theless, when we limit the data classified within 10s response time (rapid-guessing), the method’s sensitivity rises to 68.78%.