Wargijono Utomo, Waras Kamdi, Eddy Sutadji, Dwi Agus Sudjimat
Adaptive assessment has become an important tool in education to measure students' knowledge more accurately. In the context of information and communication technology courses, traditional approaches often fail to capture the complexity and variation of students' abilities. This article proposes the integration of Item Response Theory (IRT) with the Fuzzy C-Means (FCM) algorithm to improve the effectiveness of adaptive assessment. IRT is used to model students' responses to test items by considering item difficulty and students' abilities. Meanwhile, FCM is applied to group students based on their ability profiles more flexibly, accommodating uncertainty and variation in answers. This method is tested on assessment data from information and communication technology students, and compared with traditional assessment methods. The results show that the integration of IRT and FCM not only improves the accuracy of assessing students' abilities but also provides more precise and relevant feedback. These findings demonstrate the great potential of this approach in supporting adaptive assessment that is more responsive to students' individual needs in a dynamic educational environment. © 2025 IEEE.
State University of Malang, Malang, Indonesia
