Possibilities and Challenges of Educational Data Mining (EDM)
Educational data mining uses algorithms and statistical methods to analyze large sets of learner data from digital platforms, assessments, or learning environments.

Possibilities:

Detect patterns in student learning behaviors (e.g., which study habits lead to better performance).

Predict student outcomes, such as dropout risk or mastery levels.

Personalize learning pathways by recommending resources tailored to individual needs.

Provide teachers with actionable insights to improve instructional strategies.

Challenges:

Data quality: incomplete or biased data can lead to misleading results.

Privacy and ethical issues: mining sensitive student data requires strong protections.

Overemphasis on quantifiable aspects: EDM struggles to capture motivation, creativity, or emotional well-being.

Risk of reinforcing inequalities: algorithms trained on biased data may reproduce existing disparities.