ZeroR and OneR classifiers, while fundamental in classification tasks, exhibit critical limitations in predicting educational outcomes, particularly in diverse datasets such as those from Assam. ZeroR, which predicts solely based on the majority class, and OneR, which builds models based on a single most significant feature, demonstrate substantial challenges in accurately differentiating between classes. Both models predominantly misclassify instances into a few categories, leading to a severe lack of correct predictions for other classes. Confusion matrices reveal that both models struggle with class differentiation, as evidenced by zero true positives for most classes and an inability to compute standard performance metrics like Precision, Recall, and F-Measure.To address these limitations, it is essential to enhance the dataset with comprehensive features capturing student demographics, socio-economic factors, historical performance data, and regional educational resources. Additionally, applying preprocessing steps such as normalization, handling missing values, and feature selection can refine the data and improve model effectiveness. Exploring hybrid approaches or ensemble methods that combine ZeroR and OneR with more sophisticated techniques may also offer better performance by leveraging multiple models for more informed predictions. This study underscores the need for significant improvements in ZeroR and OneR to enhance their ability to accurately predict and differentiate educational outcomes, providing a foundation for the development of more advanced classification models.