Predict Academic Performance of Students using an K Nearest Neighbour Algorithm Case Study: MATLAB Course

The aim of the sudy predictability of varying female third level. In the academic year 1437 department of chemistry at MATLAB subject and knowledge of the academic performance of female students in support of the educational process, use the search applied study in the area of detecting knowledge and techniques and data Mining their appropriateness and taking advantage of the volume of data, used Classification techniques represented in K-nearest neighbour (KNN) algorithms to take advantage of the grades for which obtained by female students in MATLAB subject in the previous two years (1435/1436) with the Grade Point Average (GPA) female students had been applied through WEKA tool that supports many of the latest technologies and algorithms in data Mining, and reached the search of many of the results, the most important of which is that the value of (K) in the algorithm based on the value of the Root Mean Square Error (RMSE), which lead to the predictability of the largest possible number of female students to be know the academic performance For them, The predictability of a course will help to assess the performance of members of the teaching staff and knowledge of the failures and negatives and work to address before the end of the semester.