Title: Educational data mining: prediction of students’ academic performance using machine learning algorithms (2022)
This is an open-access research article published in Smart Learning Environments. It uses EDM techniques to analyze student records and predict final exam performance in a Turkish university course. (SpringerLink)

How the research uses EDM

• The authors collected large amounts of student data, primarily:

  • Midterm exam scores

  • Department and faculty characteristics

  • Final exam grades

• They applied machine learning algorithms — such as random forests, support vector machines, logistic regression, Naïve Bayes, and k-nearest neighbor — to develop predictive models of final exam performance. (SpringerLink)

• The goal was to identify patterns linking early performance and course context with later success or risk of failing.

• The best model achieved about 70–75% classification accuracy in predicting whether students would succeed, indicating that EDM can reasonably forecast student outcomes. (SpringerLink)

📊 What Educational Data Mining Can Tell Us

EDM is powerful for revealing insights that traditional statistical approaches might miss:

1. Predictive patterns

EDM can forecast outcomes such as:

• Which students are at risk of failing

• Likelihood of course completion or dropout
  These predictions use historical data and machine learning to flag students who may need support early. (SpringerLink)

2. Student behavior and strategies

By mining patterns in activity logs (e.g., time spent, sequence of activities), researchers can see how students interact with learning materials and identify common learning strategies. One study showed how clustering techniques revealed distinct strategies in an online flipped classroom environment. (MDPI)

3. Important predictors

EDM can identify which variables matter most for outcomes — for example, midterm grades or participation behaviors may be stronger predictors of final performance than demographic factors. (SpringerLink)

4. Group patterns

Techniques like clustering and association rule mining can reveal groups of students with similar learning progressions or frequent sequences of learning activities. (je-lks.org)

5. Actionable feedback

Results can inform teaching decisions, curriculum redesign, and targeted interventions — e.g., instructors can intervene earlier with students predicted to struggle.

🔍 What EDM Cannot Tell Us

Despite its strengths, EDM has limitations:

❌ Causal relationships

EDM primarily identifies associations and patterns — but it cannot prove why something happens. For example, finding that early assignment scores predict final grades doesn’t reveal why those early scores matter (motivation? prior knowledge?). This is a classic correlation vs. causation limitation.

❌ Deep human context

Quantitative data doesn’t fully capture:

• student motivation

• emotional states

• nuanced reasoning

These qualitative aspects may influence learning but are not directly measurable by EDM. (PubMed)

❌ Ethical and fairness issues

• Predictions may reflect biases in historical data

• Certain student populations may be misrepresented
  Thus, EDM cannot automatically ensure equity without careful design and oversight. (SpringerLink)

❌ Generalizability

Models built on one dataset or context don’t always apply elsewhere. For example, a model trained in one university or course might not predict well in a different setting.

🧠 Summary

If you’re interested, I can also summarize a specific EDM technique (like clustering or sequence mining) with examples of how it’s applied in real educational systems.