Enhancing MLOps for Educational Data Mining: A Comparative Study of Weka and KNIME in Model Lifecycle Management

Abstract

This paper presents a comparative case study of Weka and KNIME for supporting Machine Learning Operations principles in Educational Data Mining. The goal of the study is to evaluate how two widely used data mining platforms support both analytical modeling and model lifecycle management in an educational data analysis context. The study uses the Ocisceni_Demografski_Podaci.csv dataset, containing approximately 15,000 demographic and education-related records from 42 municipalities. Both platforms were evaluated using the same preprocessing logic, classification, clustering, visualization, and lifecycle-management tasks. The analytical evaluation included Naive Bayes, k-nearest neighbors, K-Means, and Farthest First algorithms, while the lifecycle evaluation assessed workflow reproducibility, pipeline automation, deployment readiness, and integration extensibility using a five-level maturity rubric. The results show that Weka provides strong support for rapid algorithmic experimentation and model comparison, with Naive Bayes achieving 87.3% classification accuracy under the selected evaluation protocol. In contrast, KNIME provides stronger support for reproducible workflow construction, automation, integration with external systems, and operational deployment through server-based infrastructure. The findings indicate that neither platform is universally superior; rather, their usefulness depends on the phase of the machine learning lifecycle. The study concludes that educational institutions can benefit from a hybrid approach in which Weka is used for early-stage model exploration, while KNIME is used for workflow reconstruction, automation, reporting, and reproducible operationalization.