Assessing Educational Data Mining Approach for Student Performance and Quotient Level Prediction based on Blooms Cognitive Level

The present study investigates the potential for predicting student performance and cognve quotient levels through the combined use of Bloom's cognitive levels and educational data mining (EDM). In order to comprehend cognitive hierarchies, the study applies Bloom's Taxonomy as a framework, utilizing a wide dataset that includes examinations, cognitive tasks, and learning activities. Utilizing factors like assessment scores, task completion durations, and interaction patterns, advanced analytics and machine learning algorithms are used to derive patterns from the data. Relying on Bloom's taxonomy, the predictive models are designed to predict short-term subject-specific performance and overall levels of cognitive quotient. Because the study allows educators and policymakers to customize instructional tactics and provides insights into individual learning trajectories, it is important. Finally, by adjusting to students' cognitive capacities, the research helps create intelligent educational systems that enable individualized instruction and prompt interventions. By bridging the gap between data-driven decision-making and educational philosophy, the findings advance the conversation on how to use technology and predictive analytics to optimize educational practices.