Knowledge Prediction of Different Students’ Categories Trough an Intelligent Testing

Student’s modelling, prediction, and grouping have remained open research issues in the multi-disciplinary area of educational data mining. The purpose of this study is to predict the correct knowledge of different categories of tested students: good, very good, and all. The experimental data set was gathered from an intelligent post-test performance containing student’s correct, missing, and wrong knowledge, time undertaken, and final mark. The proposed procedure applies consequently correlation analysis, simple and multiple liner regression using a power specialized tool for programming by the teacher. The finding is that the accuracy of the procedure is satisfactory for the three students’ categories. The experiment also confirms some findings of other researchers and previous authors’ team studies.