<![CDATA[This study aims to evaluate the performance of k-NN algorithm in recommending career paths for students based on their interests, past courses, and career goals. The k-NN algorithm was applied to a dataset of student information and its performance was evaluated using quantitative or qualitative measures such as accuracy or user satisfaction. The results indicated that the algorithm provided accurate recommendations and that the choice of k and the use of Euclidean distance measure were crucial for the performance of the algorithm. However, the study also highlighted the limitations of the research, such as the size and diversity of the dataset used, which could have affected the generalizability of the results. This study emphasizes the potential of data-driven approaches in career guidance in education and the k-NN algorithm as a valuable tool in this field. Future research could include incorporating additional factors such as student demographics or academic performance into the algorithm and using more diverse and larger datasets]]>
Copyrights © 2022
