E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review

Area of Science:

• Educational Technology
• Data Science
• Learning Analytics

Background:

• E-learning is integral to modern education, accelerated by the Covid-19 pandemic.
• Students increasingly use digital tools, necessitating enhanced e-learning instructional mechanisms.
• Educational Data Mining (EDM) is gaining significance for improving e-learning systems.

Purpose of the Study:

• To review the role of EDM in enhancing e-learning environments.
• To discuss commonly-used EDM techniques and their applications.
• To explore student performance prediction and future e-learning focus areas.

Main Methods:

• Literature review of research on Educational Data Mining in e-learning.
• Analysis of commonly-used EDM techniques.
• Examination of studies on student performance prediction and e-learning trends.

Main Results:

• EDM techniques offer valuable insights for adapting e-learning to student needs.
• Data mining can help improve student academic performance in technology-assisted learning.
• The Covid-19 pandemic has significantly impacted e-learning adoption and development.

Conclusions:

• EDM is crucial for optimizing e-learning effectiveness and student outcomes.
• Future e-learning strategies should leverage data-driven insights for personalization.
• Continued research in EDM is vital for advancing educational technology.