To study how big data can be used to create personalized learning trajectories for students and what implications this may have for the learning process and educational systems.

1. Introduction:
Explain what big data is and its role in education. Identify how big data analysis can help in understanding student behavior, performance, and needs.

Sample content:

Definition of big data.
Examples of data sources in the educational environment (online platforms, tests, performance, interaction with educational materials).
The importance of big data for personalization of learning.
2. How does it work?
Describe the processes by which big data is used to personalize learning. Focus on specific aspects:

Data collection:

Data is collected through distance learning platforms (LMS), online tests, student activity records, etc.
Data may include academic performance, time spent studying materials, participation in discussions, and interactions with platforms.
Data analysis:

Using machine learning algorithms to analyze student behavior patterns.
Identifying student strengths and weaknesses and automatically customizing learning assignments.
Examples of algorithms such as clustering or predictive learning.
Data application:

Personalized learning recommendations based on preferences, performance, and learning styles.
Adaptive learning programs that offer customized assignments and resources for each student.
3. The impact of big data in personalized learning
Positive impacts:

Individualized approach: Each student receives personalized assignments and materials, which helps them learn information more effectively.
Increased motivation: Personalization allows you to offer tasks at the level of the student’s current knowledge, avoiding tasks that are too difficult or too easy.
Timely feedback: Educators can see problem areas and quickly adjust the learning process.
Potential risks:

Privacy issues: Collecting large amounts of data on students raises privacy concerns.
Algorithmic bias: Misinterpretation of data can lead to erroneous recommendations or limit student development.
Dependency on technology: Excessive dependence on data can reduce the value of creative thinking and the ability to solve non-standard problems.
4. Real-life examples of big data in education
Include analysis of real-life examples of big data use to personalize learning:

Case 1: DreamBox Learning is an online platform that adapts math assignments based on student progress in real time.
Case 2: Coursera - analyzing course interaction data allows educators to improve materials and make personalized recommendations.
Each case study should demonstrate how data is used to create individual learning paths and what results were achieved.

5. Conclusion
Summarize your research by highlighting the importance of personalization using big data to improve the educational process. Emphasize a balanced approach to the use of data, considering both the positive aspects and possible risks.