<![CDATA[Determination of initial ability of examinees is one of the important stages in the adaptive assessment system. The accuracy of the examinee's ability level prediction will influence the appropriateness of choosen item difficulty level for each examinee. This paper discusses the patterns mining of cognitive ability based on cluster using K-Means. The K-means method is utilized to mine the examinees’ ability pattern from examinees’ pretest answers. The patterns are used for developing rules to determine examinee’s ability level in the adaptive assessment system. The addition of this method is proposed to improve the performance of the prediction methods to predict the examinees’ ability level. Extraction of graduation value at each level is done before the pattern excavation process. Patterns found become the basis of making the rules as well as replace the rules from the experts in previous studies. The prediction of participants' ability is done by implementing rule based method classifier. A total of 140 data were used for the experiment. Based on the results of the experiment, it can be concluded that the cluster-based pattern mining using K-means can be utilized to determine the cognitive ability level of examinee. The application of this method to student pretest data shows the performance improvement of all the prediction methods used in this paper, i.e. Naive Bayes, MLP, SMO, Decision Table, JRIP, and J48. This method is suitable for adaptive assessment system where the rules can be adjusted along with the addition of the number of the data as well as the addition of the number of variations in the ability pattern of examinees.]]>
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