Intelligent modelling for predicting students' final grades

The main objective of this paper is producing an intelligent virtual teacher who will be able to predict the students' final grades at the end of the semester. Our approach is based on continual observation of the student's activities on the particular course during the semester. In order to achieve realistic modelling of the students' devotion to the given lectures and also the degree of how much the student has learned from the given lecture, we take into account both the e-Learning and the e-Assessment results through the semester. In our previous work we did an intelligent students' Profiling to classify the students into a pass, or, fail category. In this paper we go deeper into the problem, achieving more precise modelling according to which we will be able to determine the student's most likely final grade, using multi classification methodology. The advantage of our model is in its ability to take into account all the assessments during the semester, not relying only on the results from the last student's assessment. It can be a good indicator whether the teacher needs to perform additional testing of the student's knowledge in order to derive an overall conclusion on the most appropriate grade.