KNN Approach to Evaluating the Feasibility of Using Scientific Publications as Final Projects

This study aims to explore the feasibility of using scientific publications as a substitute for traditional final assignments in higher education by applying the K-Nearest Neighbors (K-NN) algorithm. Traditional final assessments, such as theses, are widely used in evaluating students, but with the increasing availability of peer-reviewed scientific publications, there is potential to use them as a more dynamic and relevant assessment tool. This study uses a dataset containing scientific publications and theses, with features such as research quality, relevance, methodology, and clarity. This study applies the K-NN algorithm to classify these materials and determine whether scientific publications can serve as an effective substitute. The results show that the K-NN algorithm, using k=4, achieved 95% accuracy, successfully distinguishing between scientific publications and theses. However, some misclassifications occurred, indicating areas for improvement, such as incorporating additional features such as citation counts or peer-review scores. These findings suggest that scientific publications, if properly classified, can indeed replace traditional final assignments, encouraging critical thinking and engagement with current research. Future research should refine the feature set and explore other machine learning models to improve accuracy. The practical implications of this research are the potential to develop more innovative and relevant approaches to assessment in higher education, which are more aligned with modern educational practice.