Educational Data Mining and Learning Analytics: An Updated Survey

This paper provides a comprehensive review of the evolution of Educational Data Mining (EDM) and Learning Analytics (LA) over the past decade. The study explores:

• Current state of educational data mining and learning analytics
• Major publications and key milestones in the field
• The knowledge discovery cycle and its impact on learning outcomes
• Educational environments, specialized tools, and available datasets
• Applied methods and techniques for data-driven learning insights
• Future trends in AI for education

Introduction

In recent years, educational data mining (EDM) and learning analytics (LA) have emerged as essential fields in improving student outcomes and optimizing educational strategies. These fields analyze large sets of educational data to identify trends, improve learning experiences, and predict student performance.

The Evolution of Educational Data Mining (EDM) and Learning Analytics (LA)

The field of educational data mining has grown significantly over the past decade. Key breakthroughs include:

• The use of machine learning algorithms to predict student success
• The development of personalized learning systems
• The implementation of real-time data analysis in online learning platforms

Tools and Techniques in EDM & LA

There are various tools used in EDM and LA, including:

• Learning Management Systems (LMS) such as Moodle and Blackboard
• Data visualization tools like Tableau
• AI-based predictive analytics models

Future Trends in AI for Education

The future of educational data mining will likely include:

• More advanced AI-driven tutoring systems
• Increased use of big data and cloud computing
• Further integration of natural language processing (NLP) and AI chatbots

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