A Predictive Model for Personalized Course Advising Using Data Mining Techniques

The course selection process in higher education has grown more complex as the students face the growing volume of academic options that must align with their personal and career goals. The traditional advising methods often fail to provide the personalized recommendations, resulting in the delayed graduations and the higher dropout rates. Therefore, the automated and intelligent course selection models and systems are becoming more important. These AI-based systems nearly deliver the tailored guidance to the students by analyzing their academic performance. In addition, this guidance is also based on the students’ individual characteristics, helping to create the personalized course recommendations. This study presents a predictive model for the personalized course advising, in which it incorporates data from the academic, personal, and the social dimensions. Then, this model uses and employs the advanced machine learning algorithms and the optimization techniques and methods to maximize the accuracy of course recommendations. The feature selection methods like Information Gain and Chi-Square are used, in addition to dedicated classifiers such as the Decision Trees (DT) and the K-Nearest Neighbors (KNN). To balance the dataset, the Synthetic Minority Oversampling Technique (SMOTE) is applied. In the implementation phase, first, the Knapsack Problem algorithm is applied to optimize the students’ course loads in order to ensure the recommendations match their abilities and the institutional requirements. Next, the KNN is applied and reaches an accuracy of 91.57% and outperforms the DT, which provides a practical solution for the problem of adaptive course advising.