Utilizing Machine Learning Algorithms to Identify Pain Points in Educational Systems Based on Student Performance Data

EasyChair Preprint 13774

Abstract

The educational system plays a crucial role in shaping the future of individuals and societies, but it is not without its challenges. Identifying pain points and areas of improvement within educational systems is essential for enhancing student outcomes and optimizing resource allocation. This abstract proposes using machine learning algorithms to analyze student performance data and uncover valuable insights about the underlying factors affecting educational effectiveness. By collecting and preprocessing diverse data sources, such as assessment results and attendance records, machine learning models can be trained to identify patterns and trends related to student performance. Feature selection and engineering techniques further refine the data to extract relevant information for analysis. Various machine learning algorithms, including supervised and unsupervised methods, can be employed to detect underperforming students or groups, highlight factors contributing to poor performance, and identify systemic issues within educational systems. The results of the analysis can be visualized and interpreted to provide actionable insights for educators, policymakers, and other stakeholders. Targeted interventions and personalized learning approaches can be designed based on the identified pain points, leading to improved educational outcomes. However, ethical considerations, such as fairness, privacy, and bias, must be carefully addressed during the implementation of machine learning algorithms in educational systems. Real-world case studies and success stories demonstrate the potential of machine learning to revolutionize education and provide valuable lessons and best practices for future implementations. This abstract concludes by emphasizing the importance of ongoing research and collaboration to harness the full potential of machine learning in identifying pain points and driving positive change in educational systems.

Keyphrases: Pain Points, Teacher shortages, achievement gap, educational systems, interventions, limited resources, quality of education

@booklet{EasyChair:13774,
  author    = {Godwin Olaoye and Samon Daniel},
  title     = {Utilizing Machine Learning Algorithms to Identify Pain Points in Educational Systems Based on Student Performance Data},
  howpublished = {EasyChair Preprint 13774},
  year      = {EasyChair, 2024}}