Reference Study:
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey.
(Widely cited in EDM research)

What the study did

Analyzed thousands of student interactions in digital learning environments.

Used patterns in performance, time spent on tasks, and click behavior.

Identified which learning activities led to better achievement and engagement.

What EDM can tell us

✅ Which concepts students struggle with the most
✅ Early warning signs of dropout or disengagement
✅ Personalized recommendations for practice and review
✅ Which instructional strategies lead to deeper learning

These insights help teachers adjust lessons and give support at the right moment — which can change a student’s entire learning journey.

What EDM cannot fully tell us

❌ Why a student feels anxious, overwhelmed, or unmotivated
❌ How personal life or emotional stress affects learning
❌ The creativity or critical thinking behind an answer
❌ The quality of a student’s relationships and confidence

Even the best models can only capture part of the learning reality. Students’ feelings, unique talents, and personal growth can’t always be reduced to numbers or prediction scores.

Insight

As educators, we want to notice every student — even the quiet ones who struggle silently. EDM gives us a new pair of eyes in the classroom, helping us notice patterns we might miss on our own. But our hearts still need to lead the work. The data may show the “what,” but teachers understand the “why.”

The most meaningful learning happens when data and human care work together.