Potential Abstract:
With the increasing availability of educational data, there is a growing need for sophisticated algorithms capable of analyzing and synthesizing this wealth of information. In this study, we propose the use of dynamic synthetic algorithms to enhance educational data mining processes. These algorithms are designed to adapt and evolve in response to the dynamic nature of educational data, allowing for more accurate and efficient analysis. By leveraging these novel algorithms, educators and researchers can gain deeper insights into student learning patterns, identify at-risk students, and personalize learning experiences.
Our research utilizes a standard dataset from a large educational institution to demonstrate the effectiveness of dynamic synthetic algorithms in educational data mining. Through a series of experiments, we compare the performance of traditional data mining approaches with our proposed dynamic synthetic algorithms. The results show that our approach outperforms existing methods in terms of predictive accuracy and scalability, providing valuable insights for educational practitioners and policymakers.
This study contributes to the field of educational data mining by introducing a new paradigm for algorithm development that is responsive to the evolving nature of educational data. By incorporating dynamic synthetic algorithms into data mining processes, educators can harness the power of big data to improve student outcomes and inform evidence-based decision-making in education.
Potential References:
- Educational data mining: a systematic review of research and emerging trends
- On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018
- A systematic review on educational data mining
- Educational data mining to support programming learning using problem-solving data
- Applying data mining techniques to e-learning problems
