Educational data analytics (EDM) offers a variety of benefits. By using data analytics techniques, we can understand more about how students learn, especially in bold or blended learning. EDM uses learning algorithms to analyze data from learning management systems (LMS), resulting in information that can be used to improve teaching methods.

The applications of EDM are very broad, including:

1. Improving learning effectiveness.
2. Improving graduation rates.
3. Help students choose the right courses.
4. Create student profiles.
5. Detect students at risk of dropping out.
6. Explain better synchronization.
7. Predict student performance.
8. Aid decision-making in student admissions.

In addition, EDM can produce student academic performance in college, help detect students who need special attention, and help universities retain their students.

Challenges in Educational Data Analytics

One of the main challenges is plagiarism. Although there are plagiarism detection tools, this problem is still difficult to be completely overcome by EDM. Another challenge is data privacy. With easy access to data tools, the analysis of students' personal information is at risk of being misused. Therefore, it is important for EDM managers to protect user privacy and be transparent about data usage.

In short, educational data analytics enables educators to better understand their students, thereby providing more effective and efficient education.